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Odabashian R, Bastin D, Jones G, Manzoor M, Tangestaniapour S, Assad M, Lakhani S, Odabashian M, McGee S. Assessment of ChatGPT-3.5's Knowledge in Oncology: Comparative Study with ASCO-SEP Benchmarks. JMIR AI 2024; 3:e50442. [PMID: 38875575 PMCID: PMC11041475 DOI: 10.2196/50442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 10/05/2023] [Accepted: 11/19/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND ChatGPT (Open AI) is a state-of-the-art large language model that uses artificial intelligence (AI) to address questions across diverse topics. The American Society of Clinical Oncology Self-Evaluation Program (ASCO-SEP) created a comprehensive educational program to help physicians keep up to date with the many rapid advances in the field. The question bank consists of multiple choice questions addressing the many facets of cancer care, including diagnosis, treatment, and supportive care. As ChatGPT applications rapidly expand, it becomes vital to ascertain if the knowledge of ChatGPT-3.5 matches the established standards that oncologists are recommended to follow. OBJECTIVE This study aims to evaluate whether ChatGPT-3.5's knowledge aligns with the established benchmarks that oncologists are expected to adhere to. This will furnish us with a deeper understanding of the potential applications of this tool as a support for clinical decision-making. METHODS We conducted a systematic assessment of the performance of ChatGPT-3.5 on the ASCO-SEP, the leading educational and assessment tool for medical oncologists in training and practice. Over 1000 multiple choice questions covering the spectrum of cancer care were extracted. Questions were categorized by cancer type or discipline, with subcategorization as treatment, diagnosis, or other. Answers were scored as correct if ChatGPT-3.5 selected the answer as defined by ASCO-SEP. RESULTS Overall, ChatGPT-3.5 achieved a score of 56.1% (583/1040) for the correct answers provided. The program demonstrated varying levels of accuracy across cancer types or disciplines. The highest accuracy was observed in questions related to developmental therapeutics (8/10; 80% correct), while the lowest accuracy was observed in questions related to gastrointestinal cancer (102/209; 48.8% correct). There was no significant difference in the program's performance across the predefined subcategories of diagnosis, treatment, and other (P=.16, which is greater than .05). CONCLUSIONS This study evaluated ChatGPT-3.5's oncology knowledge using the ASCO-SEP, aiming to address uncertainties regarding AI tools like ChatGPT in clinical decision-making. Our findings suggest that while ChatGPT-3.5 offers a hopeful outlook for AI in oncology, its present performance in ASCO-SEP tests necessitates further refinement to reach the requisite competency levels. Future assessments could explore ChatGPT's clinical decision support capabilities with real-world clinical scenarios, its ease of integration into medical workflows, and its potential to foster interdisciplinary collaboration and patient engagement in health care settings.
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Affiliation(s)
- Roupen Odabashian
- Department of Oncology, Barbara Ann Karmanos Cancer Institute, Wayne State University, Detroit, MI, United States
| | - Donald Bastin
- Department of Medicine, Division of Internal Medicine, The Ottawa Hospital and the University of Ottawa, Ottawa, ON, Canada
| | - Georden Jones
- Mary A Rackham Institute, University of Michigan, Ann Arbor, MI, United States
| | | | | | - Malke Assad
- Department of Plastic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Sunita Lakhani
- Department of Medicine, Division of Internal Medicine, Jefferson Abington Hospital, Philadelphia, PA, United States
| | - Maritsa Odabashian
- Mary A Rackham Institute, University of Michigan, Ann Arbor, MI, United States
- The Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Sharon McGee
- Department of Medicine, Division of Medical Oncology, The Ottawa Hospital and the University of Ottawa, Ottawa, ON, Canada
- Cancer Therapeutics Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
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2
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Brown OR, Hullender DA. Darwinian evolution has become dogma; AI can rescue what is salvageable. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2024; 186:53-56. [PMID: 38145808 DOI: 10.1016/j.pbiomolbio.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/06/2023] [Accepted: 12/22/2023] [Indexed: 12/27/2023]
Abstract
Artificial Intelligence (AI), as an academic discipline, is traceable to the mid-1950s but it is currently exploding in applications with successes and concerns. AI can be defined as intelligence demonstrated by computers, with intelligence difficult to define but it must include concepts of ability to learn, reason, and generalize from a vast amount of information and, we propose, to infer meaning. The type of AI known as general AI, has strong, but unrealized potential both for assessing and also for solving major problems with the scientific theory of Darwinian evolution, including its modern variants and for origin of life studies. Specifically, AI should be applied first to evaluate the strengths and weaknesses of the assumptions and empirical information underpinning theories of the origin of life and probability of its evolution. AI should then be applied to assess the scientific validity of the theory of how abundant life came to be on earth.
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Affiliation(s)
- Olen R Brown
- Emeritus of Biomedical Sciences, at the University of Missouri, Columbia, MO, USA.
| | - David A Hullender
- Mechanical and Aerospace Engineering at the University of Texas at Arlington, USA
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3
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Karpov DV, Kurdiumov S, Horak P. Convolutional neural networks for mode on-demand high finesse optical resonator design. Sci Rep 2023; 13:15567. [PMID: 37730758 PMCID: PMC10511533 DOI: 10.1038/s41598-023-42223-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 09/06/2023] [Indexed: 09/22/2023] Open
Abstract
We demonstrate the use of machine learning through convolutional neural networks to solve inverse design problems of optical resonator engineering. The neural network finds a harmonic modulation of a spherical mirror to generate a resonator mode with a given target topology ("mode on-demand"). The procedure allows us to optimize the shape of mirrors to achieve a significantly enhanced coupling strength and cooperativity between a resonator photon and a quantum emitter located at the center of the resonator. In a second example, a double-peak mode is designed which would enhance the interaction between two quantum emitters, e.g., for quantum information processing.
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Affiliation(s)
- Denis V Karpov
- Optoelectronics Research Centre, University of Southampton, Southampton, SO17 1BJ, UK
| | - Sergei Kurdiumov
- Optoelectronics Research Centre, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Peter Horak
- Optoelectronics Research Centre, University of Southampton, Southampton, SO17 1BJ, UK
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4
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Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z, Chandak P, Liu S, Van Katwyk P, Deac A, Anandkumar A, Bergen K, Gomes CP, Ho S, Kohli P, Lasenby J, Leskovec J, Liu TY, Manrai A, Marks D, Ramsundar B, Song L, Sun J, Tang J, Veličković P, Welling M, Zhang L, Coley CW, Bengio Y, Zitnik M. Scientific discovery in the age of artificial intelligence. Nature 2023; 620:47-60. [PMID: 37532811 DOI: 10.1038/s41586-023-06221-2] [Citation(s) in RCA: 76] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/16/2023] [Indexed: 08/04/2023]
Abstract
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.
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Affiliation(s)
- Hanchen Wang
- Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- Department of Research and Early Development, Genentech Inc, South San Francisco, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Tianfan Fu
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Yuanqi Du
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Wenhao Gao
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Ziming Liu
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Payal Chandak
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA
| | - Shengchao Liu
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Peter Van Katwyk
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Andreea Deac
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Anima Anandkumar
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- NVIDIA, Santa Clara, CA, USA
| | - Karianne Bergen
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Carla P Gomes
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Shirley Ho
- Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA
- Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Physics and Center for Data Science, New York University, New York, NY, USA
| | | | - Joan Lasenby
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Arjun Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Le Song
- BioMap, Beijing, China
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Jimeng Sun
- University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Jian Tang
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- HEC Montréal, Montreal, Quebec, Canada
- CIFAR AI Chair, Toronto, Ontario, Canada
| | - Petar Veličković
- Google DeepMind, London, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Max Welling
- University of Amsterdam, Amsterdam, Netherlands
- Microsoft Research Amsterdam, Amsterdam, Netherlands
| | - Linfeng Zhang
- DP Technology, Beijing, China
- AI for Science Institute, Beijing, China
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yoshua Bengio
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Data Science Initiative, Cambridge, MA, USA.
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA.
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5
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López C. Artificial Intelligence and Advanced Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208683. [PMID: 36560859 DOI: 10.1002/adma.202208683] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/01/2022] [Indexed: 06/09/2023]
Abstract
Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.
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Affiliation(s)
- Cefe López
- Instituto de Ciencia de Materiales de Madrid (ICMM), Consejo Superior de Investigaciones Científicas (CSIC), Calle Sor Juana Inés de la Cruz 3, Madrid, 28049, Spain
- Donostia International Physics Centre (DIPC), Paseo Manuel de Lardizábal 4, San Sebastián, 20018, España
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6
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Shields MD, Gurley K, Catarelli R, Chauhan M, Ojeda-Tuz M, Masters FJ. Active learning applied to automated physical systems increases the rate of discovery. Sci Rep 2023; 13:8402. [PMID: 37225752 DOI: 10.1038/s41598-023-35257-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/15/2023] [Indexed: 05/26/2023] Open
Abstract
Active machine learning is widely used in computational studies where repeated numerical simulations can be conducted on high performance computers without human intervention. But translation of these active learning methods to physical systems has proven more difficult and the accelerated pace of discoveries aided by these methods remains as yet unrealized. Through the presentation of a general active learning framework and its application to large-scale boundary layer wind tunnel experiments, we demonstrate that the active learning framework used so successfully in computational studies is directly applicable to the investigation of physical experimental systems and the corresponding improvements in the rate of discovery can be transformative. We specifically show that, for our wind tunnel experiments, we are able to achieve in approximately 300 experiments a learning objective that would be impossible using traditional methods.
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Affiliation(s)
- Michael D Shields
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21212, USA.
| | - Kurtis Gurley
- Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Ryan Catarelli
- Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Mohit Chauhan
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21212, USA
| | - Mariel Ojeda-Tuz
- Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Forrest J Masters
- Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL, 32611, USA
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7
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Xu N, Zhou F, Ye X, Lin X, Chen B, Zhang T, Yue F, Chen B, Wang Y, Du J. Noise Prediction and Reduction of Single Electron Spin by Deep-Learning-Enhanced Feedforward Control. NANO LETTERS 2023; 23:2460-2466. [PMID: 36942925 DOI: 10.1021/acs.nanolett.2c03449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Noise-induced control imperfection is an important problem in applications of diamond-based nanoscale sensing, where measurement-based strategies are generally utilized to correct low-frequency noises in realtime. However, the spin-state readout requires a long time due to the low photon-detection efficiency. This inevitably introduces a delay in the noise-reduction process and limits its performance. Here we introduce the deep learning approach to relax this restriction by predicting the trend of noise and compensating for the delay. We experimentally implement feedforward quantum control of the nitrogen-vacancy center in diamond to protect its spin coherence and improve the sensing performance against noise. The new approach effectively enhances the decoherence time of the electron spin, which enables exploration of more physics from its resonant spectroscopy. A theoretical model is provided to explain the improvement. This scheme could be applied in general sensing schemes and extended to other quantum systems.
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Affiliation(s)
- Nanyang Xu
- Research Center for Quantum Sensing, Zhejiang Lab, Hangzhou 311000, China
- School of Physics, Hefei University of Technology, Hefei 230009, Anhui, China
| | - Feifei Zhou
- Research Center for Quantum Sensing, Zhejiang Lab, Hangzhou 311000, China
- School of Physics, Hefei University of Technology, Hefei 230009, Anhui, China
| | - Xiangyu Ye
- CAS Key Laboratory of Microscale Magnetic Resonance, University of Science and Technology of China, Hefei 230026, China
| | - Xue Lin
- Research Center for Quantum Sensing, Zhejiang Lab, Hangzhou 311000, China
- School of Physics, Hefei University of Technology, Hefei 230009, Anhui, China
| | - Bao Chen
- Research Center for Quantum Sensing, Zhejiang Lab, Hangzhou 311000, China
- School of Physics, Hefei University of Technology, Hefei 230009, Anhui, China
| | - Ting Zhang
- School of Physics, Hefei University of Technology, Hefei 230009, Anhui, China
| | - Feng Yue
- Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei 230009, China
| | - Bing Chen
- School of Physics, Hefei University of Technology, Hefei 230009, Anhui, China
| | - Ya Wang
- CAS Key Laboratory of Microscale Magnetic Resonance, University of Science and Technology of China, Hefei 230026, China
| | - Jiangfeng Du
- CAS Key Laboratory of Microscale Magnetic Resonance, University of Science and Technology of China, Hefei 230026, China
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8
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Homolak J. Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma. Croat Med J 2023; 64:1-3. [PMID: 36864812 PMCID: PMC10028563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023] Open
Affiliation(s)
- Jan Homolak
- Jan Homolak, Department of Pharmacology, University of Zagreb School of Medicine, Zagreb, Croatia,
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9
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Homolak J. Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma. Croat Med J 2023; 64. [PMID: 36864812 PMCID: PMC10028563 DOI: 10.3325/cmj.2023.64.1] [Citation(s) in RCA: 59] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023] Open
Affiliation(s)
- Jan Homolak
- Jan Homolak, Department of Pharmacology, University of Zagreb School of Medicine, Zagreb, Croatia,
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10
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11
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Rakhimbekova A, Lopukhov A, Klyachko N, Kabanov A, Madzhidov TI, Tropsha A. Efficient design of peptide-binding polymers using active learning approaches. J Control Release 2023; 353:903-914. [PMID: 36402234 DOI: 10.1016/j.jconrel.2022.11.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 10/21/2022] [Accepted: 11/13/2022] [Indexed: 12/23/2022]
Abstract
Active learning (AL) has become a subject of active recent research both in industry and academia as an efficient approach for rapid design and discovery of novel chemicals, materials, and polymers. Herein, we have assessed the applicability of AL for the discovery of polymeric micelle formulations for poorly soluble drugs. We were motivated by the key advantages of this approach making it a desirable strategy for rational design of drug delivery systems due toto its ability to (i) employ relatively small datasets for model development, (ii) iterate between model development and model assessment using small external datasets that can be either generated in focused experimental studies or formed from subsets of the initial training data, and (iii) progressively evolve models towards increasingly more reliable predictions and the identification of novel chemicals with the desired properties. In this study, we compared various AL protocols for their effectiveness in finding biologically active molecules using synthetic datasets. We have investigated the dependency of AL performance on the size of the initial training set, the relative complexity of the task, and the choice of the initial training dataset. We found that AL techniques as applied to regression modeling offer no benefits over random search, while AL used for classification tasks performs better than models built for randomly selected training sets but still quite far from perfect. Using the best performing AL protocol,. Finally, the best performing AL approach was employed to discover and experimentally validate novel binding polymers for a case study of asialoglycoprotein receptor (ASGPR).
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Affiliation(s)
- Assima Rakhimbekova
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan 420008, Russia
| | - Anton Lopukhov
- Laboratory of Chemical Design of Bionanomaterials, Faculty of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Natalia Klyachko
- Laboratory of Chemical Design of Bionanomaterials, Faculty of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Alexander Kabanov
- Laboratory of Chemical Design of Bionanomaterials, Faculty of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russia; Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC, USA
| | - Timur I Madzhidov
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan 420008, Russia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
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12
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Eva B, Ried K, Müller T, Briegel HJ. How a Minimal Learning Agent can Infer the Existence of Unobserved Variables in a Complex Environment. Minds Mach (Dordr) 2022; 33:185-219. [PMID: 37041982 PMCID: PMC10082113 DOI: 10.1007/s11023-022-09619-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 12/13/2022] [Indexed: 12/30/2022]
Abstract
AbstractAccording to a mainstream position in contemporary cognitive science and philosophy, the use of abstract compositional concepts is amongst the most characteristic indicators of meaningful deliberative thought in an organism or agent. In this article, we show how the ability to develop and utilise abstract conceptual structures can be achieved by a particular kind of learning agent. More specifically, we provide and motivate a concrete operational definition of what it means for these agents to be in possession of abstract concepts, before presenting an explicit example of a minimal architecture that supports this capability. We then proceed to demonstrate how the existence of abstract conceptual structures can be operationally useful in the process of employing previously acquired knowledge in the face of new experiences, thereby vindicating the natural conjecture that the cognitive functions of abstraction and generalisation are closely related.
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13
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Cordier BA, Sawaya NPD, Guerreschi GG, McWeeney SK. Biology and medicine in the landscape of quantum advantages. J R Soc Interface 2022; 19:20220541. [PMID: 36448288 PMCID: PMC9709576 DOI: 10.1098/rsif.2022.0541] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.
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Affiliation(s)
- Benjamin A. Cordier
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97202, USA
| | | | | | - Shannon K. McWeeney
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97202, USA,Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97202, USA,Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97202, USA
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14
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Krenn M, Pollice R, Guo SY, Aldeghi M, Cervera-Lierta A, Friederich P, dos Passos Gomes G, Häse F, Jinich A, Nigam A, Yao Z, Aspuru-Guzik A. On scientific understanding with artificial intelligence. NATURE REVIEWS. PHYSICS 2022; 4:761-769. [PMID: 36247217 PMCID: PMC9552145 DOI: 10.1038/s42254-022-00518-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/30/2022] [Indexed: 05/27/2023]
Abstract
An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science. With the increase in the available computational power and advances in artificial intelligence, a natural question arises: how can advanced computational systems, and specifically artificial intelligence, contribute to new scientific understanding or gain it autonomously? Trying to answer this question, we adopted a definition of 'scientific understanding' from the philosophy of science that enabled us to overview the scattered literature on the topic and, combined with dozens of anecdotes from scientists, map out three dimensions of computer-assisted scientific understanding. For each dimension, we review the existing state of the art and discuss future developments. We hope that this Perspective will inspire and focus research directions in this multidisciplinary emerging field.
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Affiliation(s)
- Mario Krenn
- Max Planck Institute for the Science of Light (MPL), Erlangen, Germany
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
| | - Robert Pollice
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Si Yue Guo
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
| | - Matteo Aldeghi
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
| | - Alba Cervera-Lierta
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Pascal Friederich
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Gabriel dos Passos Gomes
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Florian Häse
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA
| | - Adrian Jinich
- Division of Infectious Diseases, Weill Department of Medicine, Weill Cornell Medical College, New York, USA
| | - AkshatKumar Nigam
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Zhenpeng Yao
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Center of Hydrogen Science, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
- Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow, Toronto, Ontario Canada
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15
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Yu R, Liu Y, Zhu L. Inverse design of high degree of freedom meta-atoms based on machine learning and genetic algorithm methods. OPTICS EXPRESS 2022; 30:35776-35791. [PMID: 36258521 DOI: 10.1364/oe.472280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/29/2022] [Indexed: 06/16/2023]
Abstract
Since inverse design is an ill-conditioned problem of mapping from low dimensions to high dimensions, inverse design is challenging, especially for design problems with many degrees of freedom (DOFs). Traditional deep learning methods and optimization methods cannot readily calculate the inverse design of meta-atoms with high DOFs. In this paper, a new method combining deep learning and genetic algorithm (GA) methods is proposed to realize the inverse design of meta-atoms with high DOFs. In this method, a predicting neural network (PNN) and a variational autoencoder (VAE) generation model are constructed and trained. The generative model is used to constrain and compress the large design space, so that the GA can jump out of the local optimal solution and find the global optimal solution. The predicting model is used to quickly evaluate the fitness value of each offspring in the GA. With the assistance of these two machine learning models, the GA can find the optimal design of meta-atoms. This approach can realize, on demand, inverse design of meta-atoms, and opens the way for the optimization of procedures in other fields.
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16
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Flam-Shepherd D, Wu TC, Gu X, Cervera-Lierta A, Krenn M, Aspuru-Guzik A. Learning interpretable representations of entanglement in quantum optics experiments using deep generative models. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00493-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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17
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Eckstein M, Horodecki P. Probing the limits of quantum theory with quantum information at subnuclear scales. Proc Math Phys Eng Sci 2022. [DOI: 10.1098/rspa.2021.0806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Modern quantum engineering techniques enabled successful foundational tests of quantum mechanics. Yet, the universal validity of quantum postulates is an open question. Here we propose a new theoretical framework of Q-data tests, which recognizes the established validity of quantum theory, but allows for more general—‘post-quantum’—scenarios in certain physical regimes. It can accommodate a large class of models with modified quantum wave dynamics, correlations beyond entanglement or general probabilistic postulates. We discuss its experimental implementation suited to probe the nature of strong nuclear interactions. In contrast to the present accelerator experiments, it shifts the focus from high-luminosity beam physics to individual particle coherent control.
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Affiliation(s)
- Michał Eckstein
- Institute of Theoretical Physics, Jagiellonian University, ul. Łojasiewicza 11, 30–348 Kraków, Poland
- Copernicus Center for Interdisciplinary Studies, ul. Szczepańska 1/5, 31-011 Kraków, Poland
| | - Paweł Horodecki
- International Centre for Theory of Quantum Technologies, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
- Faculty of Applied Physics and Mathematics, National Quantum Information Centre, Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland
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18
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Active-learning-based reconstruction of circuit model. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02700-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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19
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Hsieh HY, Chen YR, Wu HC, Chen HL, Ning J, Huang YC, Wu CM, Lee RK. Extract the Degradation Information in Squeezed States with Machine Learning. PHYSICAL REVIEW LETTERS 2022; 128:073604. [PMID: 35244420 DOI: 10.1103/physrevlett.128.073604] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/18/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
In order to leverage the full power of quantum noise squeezing with unavoidable decoherence, a complete understanding of the degradation in the purity of squeezed light is demanded. By implementing machine-learning architecture with a convolutional neural network, we illustrate a fast, robust, and precise quantum state tomography for continuous variables, through the experimentally measured data generated from the balanced homodyne detectors. Compared with the maximum likelihood estimation method, which suffers from time-consuming and overfitting problems, a well-trained machine fed with squeezed vacuum and squeezed thermal states can complete the task of reconstruction of the density matrix in less than one second. Moreover, the resulting fidelity remains as high as 0.99 even when the antisqueezing level is higher than 20 dB. Compared with the phase noise and loss mechanisms coupled from the environment and surrounding vacuum, experimentally, the degradation information is unveiled with machine learning for low and high noisy scenarios, i.e., with the antisqueezing levels at 12 dB and 18 dB, respectively. Our neural network enhanced quantum state tomography provides the metrics to give physical descriptions of every feature observed in the quantum state with a single scan measurement just by varying the local oscillator phase from 0 to 2π and paves a way of exploring large-scale quantum systems in real time.
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Affiliation(s)
- Hsien-Yi Hsieh
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Yi-Ru Chen
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Hsun-Chung Wu
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Hua Li Chen
- Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Jingyu Ning
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Yao-Chin Huang
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Chien-Ming Wu
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Ray-Kuang Lee
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
- Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
- Physics Division, National Center for Theoretical Sciences, Taipei 10617, Taiwan
- Center for Quantum Technology, Hsinchu 30013, Taiwan
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20
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Chen SYC, Huang CM, Hsing CW, Goan HS, Kao YJ. Variational quantum reinforcement learning via evolutionary optimization. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac4559] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Recent advances in classical reinforcement learning (RL) and quantum computation point to a promising direction for performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here, we present two frameworks for deep quantum RL tasks using gradient-free evolutionary optimization. First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using amplitude encoding. Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL in the MiniGrid environment with 147-dimensional inputs. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.
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21
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Stark spectral line broadening modeling by machine learning algorithms. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06763-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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22
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Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics. PHOTONICS 2022. [DOI: 10.3390/photonics9010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Machine learning in photonics has potential in many industries. However, research on patent portfolios is still lacking. The purpose of this study was to assess the status of machine learning in photonics technology and patent portfolios and investigate major assignees to generate a better understanding of the developmental trends of machine learning in photonics. This can provide governments and industry with a resource for planning strategic development. I used data-mining methods (correspondence analysis and K-means clustering) to explore competing technological and strategic-group relationships within the field of machine learning in photonics. The data were granted patents in the USPTO database from 2019 to 2020. The results reveal that patents were primarily in image data processing, electronic digital data processing, wireless communication networks, and healthcare informatics and diagnosis. I assessed the relative technological advantages of various assignees and propose policy recommendations for technology development.
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23
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24
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Ament S, Amsler M, Sutherland DR, Chang MC, Guevarra D, Connolly AB, Gregoire JM, Thompson MO, Gomes CP, van Dover RB. Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams. SCIENCE ADVANCES 2021; 7:eabg4930. [PMID: 34919429 PMCID: PMC8682983 DOI: 10.1126/sciadv.abg4930] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate accelerated exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis using lateral gradient laser spike annealing and optical characterization along with a hierarchy of AI methods to map out processing phase diagrams. Efficient exploration of the multidimensional parameter space is achieved with nested active learning cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments and end-to-end uncertainty quantification. We demonstrate SARA’s performance by autonomously mapping synthesis phase boundaries for the Bi2O3 system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for stabilizing δ-Bi2O3 at room temperature, a critical development for electrochemical technologies.
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Affiliation(s)
- Sebastian Ament
- Department of Computer Science, Cornell University, Ithaca, NY 14853, USA
| | - Maximilian Amsler
- Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14853, USA
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, CH-3012 Bern, Switzerland
- Corresponding author. (M.A.); (C.P.G.)
| | - Duncan R. Sutherland
- Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Ming-Chiang Chang
- Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Dan Guevarra
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA
| | - Aine B. Connolly
- Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14853, USA
| | - John M. Gregoire
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA
| | - Michael O. Thompson
- Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Carla P. Gomes
- Department of Computer Science, Cornell University, Ithaca, NY 14853, USA
- Corresponding author. (M.A.); (C.P.G.)
| | - R. Bruce van Dover
- Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14853, USA
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25
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Abstract
We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies, such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search, but is also an essential step towards the automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.
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26
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Yu S, Gao Y, Chen BB, Li W. Learning the Effective Spin Hamiltonian of a Quantum Magnet. CHINESE PHYSICS LETTERS 2021; 38:097502. [DOI: 10.1088/0256-307x/38/9/097502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
To understand the intriguing many-body states and effects in the correlated quantum materials, inference of the microscopic effective Hamiltonian from experiments constitutes an important yet very challenging inverse problem. Here we propose an unbiased and efficient approach learning the effective Hamiltonian through the many-body analysis of the measured thermal data. Our approach combines the strategies including the automatic gradient and Bayesian optimization with the thermodynamics many-body solvers including the exact diagonalization and the tensor renormalization group methods. We showcase the accuracy and powerfulness of the Hamiltonian learning by applying it firstly to the thermal data generated from a given spin model, and then to realistic experimental data measured in the spin-chain compound copper nitrate and triangular-lattice magnet TmMgGaO4. The present automatic approach constitutes a unified framework of many-body thermal data analysis in the studies of quantum magnets and strongly correlated materials in general.
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27
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Hamann A, Dunjko V, Wölk S. Quantum-accessible reinforcement learning beyond strictly epochal environments. QUANTUM MACHINE INTELLIGENCE 2021; 3:22. [PMID: 34723097 PMCID: PMC8550166 DOI: 10.1007/s42484-021-00049-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 05/07/2021] [Indexed: 06/13/2023]
Abstract
In recent years, quantum-enhanced machine learning has emerged as a particularly fruitful application of quantum algorithms, covering aspects of supervised, unsupervised and reinforcement learning. Reinforcement learning offers numerous options of how quantum theory can be applied, and is arguably the least explored, from a quantum perspective. Here, an agent explores an environment and tries to find a behavior optimizing some figure of merit. Some of the first approaches investigated settings where this exploration can be sped-up, by considering quantum analogs of classical environments, which can then be queried in superposition. If the environments have a strict periodic structure in time (i.e. are strictly episodic), such environments can be effectively converted to conventional oracles encountered in quantum information. However, in general environments, we obtain scenarios that generalize standard oracle tasks. In this work, we consider one such generalization, where the environment is not strictly episodic, which is mapped to an oracle identification setting with a changing oracle. We analyze this case and show that standard amplitude-amplification techniques can, with minor modifications, still be applied to achieve quadratic speed-ups. In addition, we prove that an algorithm based on Grover iterations is optimal for oracle identification even if the oracle changes over time in a way that the "rewarded space" is monotonically increasing. This result constitutes one of the first generalizations of quantum-accessible reinforcement learning.
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Affiliation(s)
- A. Hamann
- Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21a, 6020 Innsbruck, Austria
| | - V. Dunjko
- LIACS, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - S. Wölk
- Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21a, 6020 Innsbruck, Austria
- Present Address: Institute of Quantum Technologies, German Aerospace Center (DLR), D-89077 Ulm, Germany
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28
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Huang HY, Kueng R, Preskill J. Information-Theoretic Bounds on Quantum Advantage in Machine Learning. PHYSICAL REVIEW LETTERS 2021; 126:190505. [PMID: 34047595 DOI: 10.1103/physrevlett.126.190505] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/17/2021] [Accepted: 04/02/2021] [Indexed: 06/12/2023]
Abstract
We study the performance of classical and quantum machine learning (ML) models in predicting outcomes of physical experiments. The experiments depend on an input parameter x and involve execution of a (possibly unknown) quantum process E. Our figure of merit is the number of runs of E required to achieve a desired prediction performance. We consider classical ML models that perform a measurement and record the classical outcome after each run of E, and quantum ML models that can access E coherently to acquire quantum data; the classical or quantum data are then used to predict the outcomes of future experiments. We prove that for any input distribution D(x), a classical ML model can provide accurate predictions on average by accessing E a number of times comparable to the optimal quantum ML model. In contrast, for achieving an accurate prediction on all inputs, we prove that the exponential quantum advantage is possible. For example, to predict the expectations of all Pauli observables in an n-qubit system ρ, classical ML models require 2^{Ω(n)} copies of ρ, but we present a quantum ML model using only O(n) copies. Our results clarify where the quantum advantage is possible and highlight the potential for classical ML models to address challenging quantum problems in physics and chemistry.
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Affiliation(s)
- Hsin-Yuan Huang
- Institute for Quantum Information and Matter, Caltech, Pasadena, California 91125, USA
- Department of Computing and Mathematical Sciences, Caltech, Pasadena, California 91125, USA
| | - Richard Kueng
- Institute for Integrated Circuits, Johannes Kepler University Linz, Linz 4040, Austria
| | - John Preskill
- Institute for Quantum Information and Matter, Caltech, Pasadena, California 91125, USA
- Department of Computing and Mathematical Sciences, Caltech, Pasadena, California 91125, USA
- Walter Burke Institute for Theoretical Physics, Caltech, Pasadena, California 91125, USA
- AWS Center for Quantum Computing, Pasadena, California 91125, USA
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29
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Experimental quantum speed-up in reinforcement learning agents. Nature 2021; 591:229-233. [PMID: 33692560 PMCID: PMC7612051 DOI: 10.1038/s41586-021-03242-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 01/15/2021] [Indexed: 11/10/2022]
Abstract
As the field of artificial intelligence advances, the demand for
algorithms that can learn quickly and efficiently increases. An important
paradigm within artificial intelligence is reinforcement learning [1], where decision-making entities called
agents interact with environments and learn by updating their behaviour based on
obtained feedback. The crucial question for practical applications is how fast
agents learn [2]. While various works have
made use of quantum mechanics to speed up the agent’s decision-making
process [3, 4], a reduction in learning time has not been demonstrated yet.
Here, we present a reinforcement learning experiment where the learning process
of an agent is sped up by utilizing a quantum communication channel with the
environment. We further show that combining this scenario with classical
communication enables the evaluation of such an improvement, and additionally
allows for optimal control of the learning progress. We implement this learning
protocol on a compact and fully tuneable integrated nanophotonic processor. The
device interfaces with telecom-wavelength photons and features a fast active
feedback mechanism, allowing us to demonstrate the agent’s systematic
quantum ad-vantage in a setup that could be readily integrated within future
large-scale quantum communication networks.
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30
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Kang M, Lau KM, Yung TK, Du S, Tam WY, Li J. Tailor-made unitary operations using dielectric metasurfaces. OPTICS EXPRESS 2021; 29:5677-5686. [PMID: 33726102 DOI: 10.1364/oe.411467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/28/2020] [Indexed: 06/12/2023]
Abstract
Qubit operation belonging to unitary transformation is the fundamental operation to realize quantum computing and information processing. Here, we show that the complex and flexible light-matter interaction between dielectric metasurfaces and incident light can be used to perform arbitrary U(2) operations. By incorporating both coherent spatial-mode operation together with two polarizations on a single metasurface, we further extend the discussion to single-photon two-qubit U(4) operations. We believe the efficient usage of metasurfaces as a potential compact platform can simplify optical qubit operation from bulky systems into conceptually subwavelength elements.
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31
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Abstract
Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.
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32
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Sgroi P, Palma GM, Paternostro M. Reinforcement Learning Approach to Nonequilibrium Quantum Thermodynamics. PHYSICAL REVIEW LETTERS 2021; 126:020601. [PMID: 33512184 DOI: 10.1103/physrevlett.126.020601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 12/18/2020] [Indexed: 06/12/2023]
Abstract
We use a reinforcement learning approach to reduce entropy production in a closed quantum system brought out of equilibrium. Our strategy makes use of an external control Hamiltonian and a policy gradient technique. Our approach bears no dependence on the quantitative tool chosen to characterize the degree of thermodynamic irreversibility induced by the dynamical process being considered, requires little knowledge of the dynamics itself, and does not need the tracking of the quantum state of the system during the evolution, thus embodying an experimentally nondemanding approach to the control of nonequilibrium quantum thermodynamics. We successfully apply our methods to the case of single- and two-particle systems subjected to time-dependent driving potentials.
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Affiliation(s)
- Pierpaolo Sgroi
- Dipartimento di Fisica e Chimica-Emilio Segré, Università degli Studi di Palermo, via Archirafi 36, I-90123 Palermo, Italy
- Centre for Theoretical Atomic, Molecular and Optical Physics, School of Mathematics and Physics, Queen's University Belfast, Belfast BT7 1NN, United Kingdom
| | - G Massimo Palma
- Dipartimento di Fisica e Chimica-Emilio Segré, Università degli Studi di Palermo, via Archirafi 36, I-90123 Palermo, Italy
- NEST, Istituto Nanoscienze-CNR, Piazza S. Silvestro 12, 56127 Pisa, Italy
| | - Mauro Paternostro
- Centre for Theoretical Atomic, Molecular and Optical Physics, School of Mathematics and Physics, Queen's University Belfast, Belfast BT7 1NN, United Kingdom
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33
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Dogu O, Plehiers PP, Van de Vijver R, D’hooge DR, Van Steenberge PHM, Van Geem KM. Distribution Changes during Thermal Degradation of Poly(styrene peroxide) by Pairing Tree-Based Kinetic Monte Carlo and Artificial Intelligence Tools. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.0c05414] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Onur Dogu
- Laboratory for Chemical Technology (LCT), Ghent University, Technologiepark 125, B-9052 Zwijnaarde, Belgium
| | - Pieter P. Plehiers
- Laboratory for Chemical Technology (LCT), Ghent University, Technologiepark 125, B-9052 Zwijnaarde, Belgium
| | - Ruben Van de Vijver
- Laboratory for Chemical Technology (LCT), Ghent University, Technologiepark 125, B-9052 Zwijnaarde, Belgium
| | - Dagmar R. D’hooge
- Laboratory for Chemical Technology (LCT), Ghent University, Technologiepark 125, B-9052 Zwijnaarde, Belgium
- Centre for Textile Science and Engineering (CTSE), Technologiepark 70a, B-9052 Zwijnaarde, Belgium
| | - Paul H. M. Van Steenberge
- Laboratory for Chemical Technology (LCT), Ghent University, Technologiepark 125, B-9052 Zwijnaarde, Belgium
| | - Kevin M. Van Geem
- Laboratory for Chemical Technology (LCT), Ghent University, Technologiepark 125, B-9052 Zwijnaarde, Belgium
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34
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Mathew RS, O'Donnell R, Pizzey D, Hughes IG. The Raspberry Pi auto-aligner: Machine learning for automated alignment of laser beams. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2021; 92:015117. [PMID: 33514190 DOI: 10.1063/5.0032588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 12/22/2020] [Indexed: 06/12/2023]
Abstract
We present a novel solution to automated beam alignment optimization. This device is based on a Raspberry Pi computer, stepper motors, commercial optomechanics and electronic devices, and the open-source machine learning algorithm M-LOOP. We provide schematic drawings for the custom hardware necessary to operate the device and discuss diagnostic techniques to determine the performance. The beam auto-aligning device has been used to improve the alignment of a laser beam into a single-mode optical fiber from manually optimized fiber alignment, with an iteration time of typically 20 minutes. We present example data of one such measurement to illustrate device performance.
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Affiliation(s)
- Renju S Mathew
- Joint Quantum Centre (JQC) Durham-Newcastle, Department of Physics, Durham University, South Road, Durham DH1 3LE, United Kingdom
| | - Roshan O'Donnell
- Joint Quantum Centre (JQC) Durham-Newcastle, Department of Physics, Durham University, South Road, Durham DH1 3LE, United Kingdom
| | - Danielle Pizzey
- Joint Quantum Centre (JQC) Durham-Newcastle, Department of Physics, Durham University, South Road, Durham DH1 3LE, United Kingdom
| | - Ifan G Hughes
- Joint Quantum Centre (JQC) Durham-Newcastle, Department of Physics, Durham University, South Road, Durham DH1 3LE, United Kingdom
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35
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Piccinotti D, MacDonald KF, A Gregory S, Youngs I, Zheludev NI. Artificial intelligence for photonics and photonic materials. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2021; 84:012401. [PMID: 33355315 DOI: 10.1088/1361-6633/abb4c7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Artificial intelligence (AI) is the most important new methodology in scientific research since the adoption of quantum mechanics and it is providing exciting results in numerous fields of science and technology. In this review we summarize research and discuss future opportunities for AI in the domains of photonics, nanophotonics, plasmonics and photonic materials discovery, including metamaterials.
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Affiliation(s)
- Davide Piccinotti
- Optoelectronics Research Centre and Centre for Photonic Metamaterials, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Kevin F MacDonald
- Optoelectronics Research Centre and Centre for Photonic Metamaterials, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Simon A Gregory
- Defence Science and Technology Laboratory, Salisbury, SP4 0JQ, United Kingdom
| | - Ian Youngs
- Defence Science and Technology Laboratory, Salisbury, SP4 0JQ, United Kingdom
| | - Nikolay I Zheludev
- Optoelectronics Research Centre and Centre for Photonic Metamaterials, University of Southampton, Southampton, SO17 1BJ, United Kingdom
- Centre for Disruptive Photonic Technologies, The Photonics Institute, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore
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36
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Sweke R, Kesselring MS, van Nieuwenburg EPL, Eisert J. Reinforcement learning decoders for fault-tolerant quantum computation. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/abc609] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Abstract
Topological error correcting codes, and particularly the surface code, currently provide the most feasible road-map towards large-scale fault-tolerant quantum computation. As such, obtaining fast and flexible decoding algorithms for these codes, within the experimentally realistic and challenging context of faulty syndrome measurements, without requiring any final read-out of the physical qubits, is of critical importance. In this work, we show that the problem of decoding such codes can be naturally reformulated as a process of repeated interactions between a decoding agent and a code environment, to which the machinery of reinforcement learning can be applied to obtain decoding agents. While in principle this framework can be instantiated with environments modelling circuit level noise, we take a first step towards this goal by using deepQ learning to obtain decoding agents for a variety of simplified phenomenological noise models, which yield faulty syndrome measurements without including the propagation of errors which arise in full circuit level noise models.
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37
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Guleva V, Shikov E, Bochenina K, Kovalchuk S, Alodjants A, Boukhanovsky A. Emerging Complexity in Distributed Intelligent Systems. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1437. [PMID: 33352754 PMCID: PMC7766450 DOI: 10.3390/e22121437] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 12/31/2022]
Abstract
Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents' classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners' preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions.
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Affiliation(s)
| | | | | | - Sergey Kovalchuk
- National Center for Cognitive Research, ITMO University, 197101 Saint Petersburg, Russia; (V.G.); (E.S.); (K.B.); (A.A.); (A.B.)
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38
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Cedeno-Mieles V, Hu Z, Ren Y, Deng X, Contractor N, Ekanayake S, Epstein JM, Goode BJ, Korkmaz G, Kuhlman CJ, Machi D, Macy M, Marathe MV, Ramakrishnan N, Saraf P, Self N. Data analysis and modeling pipelines for controlled networked social science experiments. PLoS One 2020; 15:e0242453. [PMID: 33232347 PMCID: PMC7685486 DOI: 10.1371/journal.pone.0242453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 11/03/2020] [Indexed: 11/19/2022] Open
Abstract
There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.
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Affiliation(s)
- Vanessa Cedeno-Mieles
- Department of Computer Science, Virginia Tech, Blacksburg, VA, United States of America
- Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
| | - Zhihao Hu
- Department of Statistics, Virginia Tech, Blacksburg, VA, United States of America
| | - Yihui Ren
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, United States of America
| | - Xinwei Deng
- Department of Statistics, Virginia Tech, Blacksburg, VA, United States of America
| | - Noshir Contractor
- Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, United States of America
| | - Saliya Ekanayake
- Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Joshua M. Epstein
- Department of Epidemiology, New York University, New York, NY, United States of America
| | - Brian J. Goode
- Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States of America
| | - Gizem Korkmaz
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, United States of America
| | - Chris J. Kuhlman
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, United States of America
| | - Dustin Machi
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, United States of America
| | - Michael Macy
- Department of Sociology, Cornell University, Ithaca, NY, United States of America
| | - Madhav V. Marathe
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, United States of America
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States of America
| | - Naren Ramakrishnan
- Department of Computer Science, Virginia Tech, Blacksburg, VA, United States of America
- Discovery Analytics Center, Virginia Tech, Blacksburg, VA, United States of America
| | - Parang Saraf
- Discovery Analytics Center, Virginia Tech, Blacksburg, VA, United States of America
| | - Nathan Self
- Discovery Analytics Center, Virginia Tech, Blacksburg, VA, United States of America
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39
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Boyajian WL, Clausen J, Trenkwalder LM, Dunjko V, Briegel HJ. On the convergence of projective-simulation-based reinforcement learning in Markov decision processes. QUANTUM MACHINE INTELLIGENCE 2020; 2:13. [PMID: 33184611 PMCID: PMC7644479 DOI: 10.1007/s42484-020-00023-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 07/02/2020] [Indexed: 06/11/2023]
Abstract
In recent years, the interest in leveraging quantum effects for enhancing machine learning tasks has significantly increased. Many algorithms speeding up supervised and unsupervised learning were established. The first framework in which ways to exploit quantum resources specifically for the broader context of reinforcement learning were found is projective simulation. Projective simulation presents an agent-based reinforcement learning approach designed in a manner which may support quantum walk-based speedups. Although classical variants of projective simulation have been benchmarked against common reinforcement learning algorithms, very few formal theoretical analyses have been provided for its performance in standard learning scenarios. In this paper, we provide a detailed formal discussion of the properties of this model. Specifically, we prove that one version of the projective simulation model, understood as a reinforcement learning approach, converges to optimal behavior in a large class of Markov decision processes. This proof shows that a physically inspired approach to reinforcement learning can guarantee to converge.
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Affiliation(s)
- W. L. Boyajian
- Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria
| | - J. Clausen
- Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria
| | - L. M. Trenkwalder
- Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria
| | - V. Dunjko
- Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria
- LIACS, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - H. J. Briegel
- Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria
- Department of Philosophy, University of Konstanz, 78457 Konstanz, Germany
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40
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Melnikov AA, Sekatski P, Sangouard N. Setting Up Experimental Bell Tests with Reinforcement Learning. PHYSICAL REVIEW LETTERS 2020; 125:160401. [PMID: 33124877 DOI: 10.1103/physrevlett.125.160401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 09/08/2020] [Indexed: 06/11/2023]
Abstract
Finding optical setups producing measurement results with a targeted probability distribution is hard, as a priori the number of possible experimental implementations grows exponentially with the number of modes and the number of devices. To tackle this complexity, we introduce a method combining reinforcement learning and simulated annealing enabling the automated design of optical experiments producing results with the desired probability distributions. We illustrate the relevance of our method by applying it to a probability distribution favouring high violations of the Bell-Clauser-Horne-Shimony-Holt (CHSH) inequality. As a result, we propose new unintuitive experiments leading to higher Bell-CHSH inequality violations than the best currently known setups. Our method might positively impact the usefulness of photonic experiments for device-independent quantum information processing.
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Affiliation(s)
- Alexey A Melnikov
- Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland
| | - Pavel Sekatski
- Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland
| | - Nicolas Sangouard
- Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland
- Institut de Physique Théorique, Université Paris Saclay, CEA, CNRS, F-91191 Gif-sur-Yvette, France
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41
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Cimini V, Barbieri M, Treps N, Walschaers M, Parigi V. Neural Networks for Detecting Multimode Wigner Negativity. PHYSICAL REVIEW LETTERS 2020; 125:160504. [PMID: 33124838 DOI: 10.1103/physrevlett.125.160504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/20/2020] [Accepted: 09/16/2020] [Indexed: 06/11/2023]
Abstract
The characterization of quantum features in large Hilbert spaces is a crucial requirement for testing quantum protocols. In the continuous variable encoding, quantum homodyne tomography requires an amount of measurement that increases exponentially with the number of involved modes, which practically makes the protocol intractable even with few modes. Here, we introduce a new technique, based on a machine learning protocol with artificial neural networks, that allows us to directly detect negativity of the Wigner function for multimode quantum states. We test the procedure on a whole class of numerically simulated multimode quantum states for which the Wigner function is known analytically. We demonstrate that the method is fast, accurate, and more robust than conventional methods when limited amounts of data are available. Moreover, the method is applied to an experimental multimode quantum state, for which an additional test of resilience to losses is carried out.
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Affiliation(s)
- Valeria Cimini
- Dipartimento di Scienze, Università degli Studi Roma Tre, Via della Vasca Navale 84, 00146 Rome, Italy
| | - Marco Barbieri
- Dipartimento di Scienze, Università degli Studi Roma Tre, Via della Vasca Navale 84, 00146 Rome, Italy
| | - Nicolas Treps
- Laboratoire Kastler Brossel, Sorbonne Université, CNRS, ENS-PSL Research University, Collège de France, 4 place Jussieu, F-75252 Paris, France
| | - Mattia Walschaers
- Laboratoire Kastler Brossel, Sorbonne Université, CNRS, ENS-PSL Research University, Collège de France, 4 place Jussieu, F-75252 Paris, France
| | - Valentina Parigi
- Laboratoire Kastler Brossel, Sorbonne Université, CNRS, ENS-PSL Research University, Collège de France, 4 place Jussieu, F-75252 Paris, France
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42
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Wang X, Rai N, Merchel Piovesan Pereira B, Eetemadi A, Tagkopoulos I. Accelerated knowledge discovery from omics data by optimal experimental design. Nat Commun 2020; 11:5026. [PMID: 33024104 PMCID: PMC7538421 DOI: 10.1038/s41467-020-18785-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 08/27/2020] [Indexed: 12/15/2022] Open
Abstract
How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli’s populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection and 4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences. How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. Here, the authors present OPEX, an optimal experimental design method to identify informative omics experiments for both experimental space exploration and model training.
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Affiliation(s)
- Xiaokang Wang
- Department of Biomedical Engineering, University of California, Davis, CA, 95616, USA.,Genome Center, University of California, Davis, CA, 95616, USA
| | - Navneet Rai
- Genome Center, University of California, Davis, CA, 95616, USA.,Department of Computer Science, University of California, Davis, CA, 95616, USA
| | - Beatriz Merchel Piovesan Pereira
- Genome Center, University of California, Davis, CA, 95616, USA.,Microbiology Graduate Group, University of California, Davis, CA, 95616, USA
| | - Ameen Eetemadi
- Genome Center, University of California, Davis, CA, 95616, USA.,Department of Computer Science, University of California, Davis, CA, 95616, USA
| | - Ilias Tagkopoulos
- Genome Center, University of California, Davis, CA, 95616, USA. .,Department of Computer Science, University of California, Davis, CA, 95616, USA.
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43
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Plehiers PP, Coley CW, Gao H, Vermeire FH, Dobbelaere MR, Stevens CV, Van Geem KM, Green WH. Artificial Intelligence for Computer-Aided Synthesis In Flow: Analysis and Selection of Reaction Components. FRONTIERS IN CHEMICAL ENGINEERING 2020. [DOI: 10.3389/fceng.2020.00005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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44
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Burlak G, Medina-Ángel G. Applications of a neural network to detect the percolating transitions in a system with variable radius of defects. CHAOS (WOODBURY, N.Y.) 2020; 30:083145. [PMID: 32872808 DOI: 10.1063/5.0010904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 08/12/2020] [Indexed: 06/11/2023]
Abstract
We systematically study the percolation phase transition at the change of concentration of the chaotic defects (pores) in an extended system where the disordered defects additionally have a variable random radius, using the methods of a neural network (NN). Two important parameters appear in such a material: the average value and the variance of the random pore radius, which leads to significant change in the properties of the phase transition compared with conventional percolation. To train a network, we use the spatial structure of a disordered environment (feature class), and the output (label class) indicates the state of the percolation transition. We found high accuracy of the transition prediction (except the narrow threshold area) by the trained network already in the two-dimensional case. We have also employed such a technique for the extended three-dimensional (3D) percolation system. Our simulations showed the high accuracy of prediction in the percolation transition in 3D case too. The considered approach opens up interesting perspectives for using NN to identify the phase transitions in real percolating nanomaterials with a complex cluster structure.
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Affiliation(s)
- Gennadiy Burlak
- CIICAp, Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, Cuernavaca, Morelos 62210, México
| | - Gustavo Medina-Ángel
- CIICAp, Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, Cuernavaca, Morelos 62210, México
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45
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Gao X, Erhard M, Zeilinger A, Krenn M. Computer-Inspired Concept for High-Dimensional Multipartite Quantum Gates. PHYSICAL REVIEW LETTERS 2020. [PMID: 32794870 DOI: 10.1038/s42254-020-0230-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
An open question in quantum optics is how to manipulate and control complex quantum states in an experimentally feasible way. Here we present concepts for transformations of high-dimensional multiphotonic quantum systems. The proposals rely on two new ideas: (i) a novel high-dimensional quantum nondemolition measurement, (ii) the encoding and decoding of the entire quantum transformation in an ancillary state for sharing the necessary quantum information between the involved parties. Many solutions can readily be performed in laboratories around the world and thereby we identify important pathways for experimental research in the near future. The concepts have been found using the computer algorithm melvin for designing computer-inspired quantum experiments. As opposed to the field of machine learning, here the human learns new scientific concepts by interpreting and analyzing the results presented by the machine. This demonstrates that computer algorithms can inspire new ideas in science, which has a widely unexplored potential that goes far beyond experimental quantum information science.
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Affiliation(s)
- Xiaoqin Gao
- Faculty of Physics, University of Vienna, Vienna, 1190, Austria
- Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austrian Academy of Sciences, Vienna, 1190, Austria
- National Mobile Communications Research Laboratory and Quantum Information Research Center, Southeast University, Nanjing, 210096, China
| | - Manuel Erhard
- Faculty of Physics, University of Vienna, Vienna, 1190, Austria
- Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austrian Academy of Sciences, Vienna, 1190, Austria
| | - Anton Zeilinger
- Faculty of Physics, University of Vienna, Vienna, 1190, Austria
- Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austrian Academy of Sciences, Vienna, 1190, Austria
| | - Mario Krenn
- Faculty of Physics, University of Vienna, Vienna, 1190, Austria
- Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austrian Academy of Sciences, Vienna, 1190, Austria
- Department of Chemistry and Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario M5G 1M1, Canada
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46
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Lamata L. Quantum machine learning and quantum biomimetics: A perspective. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab9803] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Abstract
Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to employ machine learning techniques to better control quantum systems. Inside quantum machine learning, quantum reinforcement learning aims at developing ‘intelligent’ quantum agents that may interact with the outer world and adapt to it, with the strategy of achieving some final goal. Another paradigm inside quantum machine learning is that of quantum autoencoders, which may allow one for employing fewer resources in a quantum device via a training process. Moreover, the field of quantum biomimetics aims at establishing analogies between biological and quantum systems, to look for previously inadvertent connections that may enable useful applications. Two recent examples are the concepts of quantum artificial life, as well as of quantum memristors. In this Perspective, we give an overview of these topics, describing the related research carried out by the scientific community.
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47
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Sun K, Ding Z, Zhang Z. Fiber directional position sensor based on multimode interference imaging and machine learning. APPLIED OPTICS 2020; 59:5745-5751. [PMID: 32609700 DOI: 10.1364/ao.394280] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 05/26/2020] [Indexed: 06/11/2023]
Abstract
A fiber directional position sensor based on multimode interference and image processing by machine learning is presented. Upon single-mode injection, light in multimode fiber generates a multi-ring-shaped interference pattern at the end facet, which is susceptible to the amplitude and direction of the fiber distortions. The fiber is mounted on an automatic translation stage, with repeating movement in four directions. The images are captured from an infrared camera and fed to a machine-learning program to train, validate, and test the fiber conditions. As a result, accuracy over 97% is achieved in recognizing fiber positions in these four directions, each with 10 classes, totaling an 8 mm span. The number of images taken for each class is merely 320. Detailed investigation reveals that the system can achieve over 60% accuracy in recognizing positions on a 5 µm resolution with a larger dataset, approaching the limit of the chosen translation stage.
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48
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49
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Giordani T, Suprano A, Polino E, Acanfora F, Innocenti L, Ferraro A, Paternostro M, Spagnolo N, Sciarrino F. Machine Learning-Based Classification of Vector Vortex Beams. PHYSICAL REVIEW LETTERS 2020; 124:160401. [PMID: 32383956 DOI: 10.1103/physrevlett.124.160401] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 03/16/2020] [Indexed: 05/28/2023]
Abstract
Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the nontrivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods-namely, convolutional neural networks and principal component analysis-to recognize and classify specific polarization patterns. Our study demonstrates the significant advantages resulting from the use of machine learning-based protocols for the construction and characterization of high-dimensional resources for quantum protocols.
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Affiliation(s)
- Taira Giordani
- Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy
| | - Alessia Suprano
- Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy
| | - Emanuele Polino
- Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy
| | - Francesca Acanfora
- Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy
| | - Luca Innocenti
- Centre for Theoretical Atomic, Molecular, and Optical Physics, School of Mathematics and Physics, Queen's University Belfast, BT7 1NN Belfast, United Kingdom
| | - Alessandro Ferraro
- Centre for Theoretical Atomic, Molecular, and Optical Physics, School of Mathematics and Physics, Queen's University Belfast, BT7 1NN Belfast, United Kingdom
| | - Mauro Paternostro
- Centre for Theoretical Atomic, Molecular, and Optical Physics, School of Mathematics and Physics, Queen's University Belfast, BT7 1NN Belfast, United Kingdom
| | - Nicolò Spagnolo
- Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy
| | - Fabio Sciarrino
- Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy
- Consiglio Nazionale delle Ricerche, Istituto dei sistemi Complessi (CNR-ISC), Via dei Taurini 19, 00185 Roma, Italy
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50
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Emami N, Pakchin PS, Ferdousi R. Computational predictive approaches for interaction and structure of aptamers. J Theor Biol 2020; 497:110268. [PMID: 32311376 DOI: 10.1016/j.jtbi.2020.110268] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/27/2020] [Accepted: 04/02/2020] [Indexed: 02/07/2023]
Abstract
Aptamers are short single-strand sequences that can bind to their specific targets with high affinity and specificity. Usually, aptamers are selected experimentally via systematic evolution of ligands by exponential enrichment (SELEX), an evolutionary process that consists of multiple cycles of selection and amplification. The SELEX process is expensive, time-consuming, and its success rates are relatively low. To overcome these difficulties, in recent years, several computational techniques have been developed in aptamer sciences that bring together different disciplines and branches of technologies. In this paper, a complementary review on computational predictive approaches of the aptamer has been organized. Generally, the computational prediction approaches of aptamer have been proposed to carry out in two main categories: interaction-based prediction and structure-based predictions. Furthermore, the available software packages and toolkits in this scope were reviewed. The aim of describing computational methods and tools in aptamer science is that aptamer scientists might take advantage of these computational techniques to develop more accurate and more sensitive aptamers.
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Affiliation(s)
- Neda Emami
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Parvin Samadi Pakchin
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran; Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
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