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Yao Q, Ji Q, Li X, Zhang Y, Chen X, Ju MG, Liu J, Wang J. Machine Learning Accelerates Precise Excited-State Potential Energy Surface Calculations on a Quantum Computer. J Phys Chem Lett 2024:7061-7068. [PMID: 38950102 DOI: 10.1021/acs.jpclett.4c01445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Electronically excited-state problems represent a crucial research field in quantum chemistry, closely related to numerous practical applications in photophysics and photochemistry. The emerging of quantum computing provides a promising computational paradigm to solve the Schrödinger equation for predicting potential energy surfaces (PESs). Here, we present a deep neural network model to predict parameters of the quantum circuits within the framework of variational quantum deflation and subspace search variational quantum eigensolver, which are two popular excited-state algorithms to implement on a quantum computer. The new machine learning-assisted algorithm is employed to study the excited-state PESs of small molecules, achieving highly accurate predictions. We then apply this algorithm to study the excited-state properties of the ArF system, which is essential to a gas laser. Through this study, we believe that with future advancements in hardware capabilities, quantum computing could be harnessed to solve excited-state problems for a broad range of systems.
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Affiliation(s)
- Qianjun Yao
- Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, China
| | - Qun Ji
- Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, China
| | - Xiaopeng Li
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Yehui Zhang
- Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, China
| | - Xinyu Chen
- Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, China
| | - Ming-Gang Ju
- Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, China
| | - Jie Liu
- Hefei National Laboratory, Hefei 230088, China
| | - Jinlan Wang
- Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, China
- Suzhou Laboratory, Suzhou 215009, China
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2
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Li J, Gao X. Quantum circuit for high order perturbation theory corrections. Sci Rep 2024; 14:13963. [PMID: 38886483 PMCID: PMC11183151 DOI: 10.1038/s41598-024-64854-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
Perturbation theory (PT) might be one of the most powerful and fruitful tools for both physicists and chemists, which has led to a wide variety of applications. Over the past decades, advances in quantum computing provide opportunities for alternatives to classical methods. Recently, a general quantum circuit estimating the low order PT corrections has been proposed. In this article, we revisit the quantum circuits for PT calculations, and develop the methods for higher order PT corrections of eigenenergy, especially the 3rd and 4th order corrections. We present the feasible quantum circuit to estimate each term in these PT corrections. There are two the fundamental operations in the proposed circuit. One approximates the perturbation terms, the other approximates the inverse of unperturbed energy difference. The proposed method can be generalized to higher order PT corrections.
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Affiliation(s)
- Junxu Li
- Department of Physics, College of Science, Northeastern University, Shenyang, 110819, China.
| | - Xingyu Gao
- Department of Physics and Astronomy, Purdue University, West Lafayette, IN, 47907, United States
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3
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Smaldone AM, Batista VS. Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction. J Chem Theory Comput 2024; 20:4901-4908. [PMID: 38795030 DOI: 10.1021/acs.jctc.4c00432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2024]
Abstract
Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled with the underlying O ( n 3 ) matrix multiplication means these efforts quickly become computationally demanding. To remedy this, we present a hybrid quantum-classical neural network for predicting drug toxicity utilizing a quantum circuit design that mimics classical neural behavior by explicitly calculating matrix products with complexity O ( n 2 ) . Leveraging the Hadamard test for efficient inner product estimation rather than the conventionally used swap test, we reduce the number of qubits by half and remove the need for quantum phase estimation. Directly computing matrix products quantum mechanically allows for learnable weights to be transferred from a quantum to a classical device for further training. We apply our framework to the Tox21 data set and show that it achieves commensurate predictive accuracy to the model's fully classical O ( n 3 ) analogue. Additionally, we demonstrate that the model continues to learn, without disruption, once transferred to a fully classical architecture. We believe that combining the quantum advantage of reduced complexity and the classical advantage of noise-free calculation will pave the way for more scalable machine learning models.
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Affiliation(s)
- Anthony M Smaldone
- Department of Chemistry, Yale University, New Haven 06511, Connecticut, United States
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven 06511, Connecticut, United States
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4
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Kale SS, Kais S. Simulation of Chemical Reactions on a Quantum Computer. J Phys Chem Lett 2024; 15:5633-5642. [PMID: 38759104 DOI: 10.1021/acs.jpclett.4c01100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
Abstract
Studying chemical reactions, particularly in the gas phase, relies heavily on computing scattering matrix elements. These elements are essential for characterizing molecular reactions and accurately determining reaction probabilities. However, the intricate nature of quantum interactions poses challenges, necessitating the use of advanced mathematical models and computational approaches to tackle the inherent complexities. In this study, we develop and apply a quantum computing algorithm for the calculation of scattering matrix elements. In our approach, we employ the time-dependent method based on the Møller operator formulation where the S-matrix element between the respective reactant and product channels is determined through the time correlation function of the reactant and product Møller wavepackets. We successfully apply our quantum algorithm to calculate scattering matrix elements for 1D semi-infinite square well potential and on the colinear hydrogen exchange reaction. As we navigate the complexities of quantum interactions, this quantum algorithm is general and emerges as a promising avenue, shedding light on new possibilities for simulating chemical reactions on quantum computers.
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Affiliation(s)
- Sumit Suresh Kale
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Sabre Kais
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
- Department of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907, United States
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5
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Gallegos M, Vassilev-Galindo V, Poltavsky I, Martín Pendás Á, Tkatchenko A. Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors. Nat Commun 2024; 15:4345. [PMID: 38773090 DOI: 10.1038/s41467-024-48567-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 04/24/2024] [Indexed: 05/23/2024] Open
Abstract
Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to interpret. Explainable AI (XAI) tools can be used to analyze complex models, but they are highly dependent on the AI technique and the origin of the reference data. Alternatively, interpretable real-space tools can be employed directly, but they are often expensive to compute. To address this dilemma between explainability and accuracy, we developed SchNet4AIM, a SchNet-based architecture capable of dealing with local one-body (atomic) and two-body (interatomic) descriptors. The performance of SchNet4AIM is tested by predicting a wide collection of real-space quantities ranging from atomic charges and delocalization indices to pairwise interaction energies. The accuracy and speed of SchNet4AIM breaks the bottleneck that has prevented the use of real-space chemical descriptors in complex systems. We show that the group delocalization indices, arising from our physically rigorous atomistic predictions, provide reliable indicators of supramolecular binding events, thus contributing to the development of Explainable Chemical Artificial Intelligence (XCAI) models.
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Affiliation(s)
- Miguel Gallegos
- Department of Analytical and Physical Chemistry, University of Oviedo, E-33006, Oviedo, Spain
| | | | - Igor Poltavsky
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
| | - Ángel Martín Pendás
- Department of Analytical and Physical Chemistry, University of Oviedo, E-33006, Oviedo, Spain.
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg.
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6
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Liu S. Harvesting Chemical Understanding with Machine Learning and Quantum Computers. ACS PHYSICAL CHEMISTRY AU 2024; 4:135-142. [PMID: 38560751 PMCID: PMC10979482 DOI: 10.1021/acsphyschemau.3c00067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 04/04/2024]
Abstract
It is tenable to argue that nobody can predict the future with certainty, yet one can learn from the past and make informed projections for the years ahead. In this Perspective, we overview the status of how theory and computation can be exploited to obtain chemical understanding from wave function theory and density functional theory, and then outlook the likely impact of machine learning (ML) and quantum computers (QC) to appreciate traditional chemical concepts in decades to come. It is maintained that the development and maturation of ML and QC methods in theoretical and computational chemistry represent two paradigm shifts about how the Schrödinger equation can be solved. New chemical understanding can be harnessed in these two new paradigms by making respective use of ML features and QC qubits. Before that happens, however, we still have hurdles to face and obstacles to overcome in both ML and QC arenas. Possible pathways to tackle these challenges are proposed. We anticipate that hierarchical modeling, in contrast to multiscale modeling, will emerge and thrive, becoming the workhorse of in silico simulations in the next few decades.
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Nicolle A, Deng S, Ihme M, Kuzhagaliyeva N, Ibrahim EA, Farooq A. Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview. J Chem Inf Model 2024; 64:597-620. [PMID: 38284618 DOI: 10.1021/acs.jcim.3c01633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.
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Affiliation(s)
- Andre Nicolle
- Aramco Fuel Research Center, Rueil-Malmaison 92852, France
| | - Sili Deng
- Massachusetts Institute of Technology, Cambridge 02139, Massachusetts, United States
| | - Matthias Ihme
- Stanford University, Stanford 94305, California, United States
| | | | - Emad Al Ibrahim
- King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Aamir Farooq
- King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
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8
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Wang Z, Chen A, Tao K, Han Y, Li J. MatGPT: A Vane of Materials Informatics from Past, Present, to Future. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306733. [PMID: 37813548 DOI: 10.1002/adma.202306733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/05/2023] [Indexed: 10/17/2023]
Abstract
Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads to think more about materials informatics. Here, a comprehensive discussion of AI models and materials infrastructures is provided, and the advances in the discovery and design of new materials are reviewed. With the rise of new research paradigms triggered by "AI for Science", the vane of materials informatics: "MatGPT", is proposed and the technical path planning from the aspects of data, descriptors, generative models, pretraining models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics, is carried out. Finally, the challenges and constraints faced by materials informatics are discussed, in order to achieve a more digital, intelligent, and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists.
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Affiliation(s)
- Zhilong Wang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - An Chen
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Kehao Tao
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanqiang Han
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jinjin Li
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
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9
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Contreras-Fortes J, Rodríguez-García MI, Sales DL, Sánchez-Miranda R, Almagro JF, Turias I. A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels. MATERIALS (BASEL, SWITZERLAND) 2023; 17:147. [PMID: 38204001 PMCID: PMC10779456 DOI: 10.3390/ma17010147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024]
Abstract
Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (Rm), yield strength (Rp), hardness (H), and elongation (A) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes.
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Affiliation(s)
- Julia Contreras-Fortes
- Laboratory and Research Section, Technical Department Acerinox Europa S.A.U., 11379 Los Barrios, Spain; (R.S.-M.); (J.F.A.)
- Department of Materials Science Metallurgical Engineering and Inorganic Chemistry, Algeciras School of Engineering and Technology, Universidad de Cádiz, INNANOMAT, IMEYMAT, Ramón Puyol Ave., 11202 Algeciras, Spain;
| | - M. Inmaculada Rodríguez-García
- MIS Group, Department of Computer Science Engineering, Algeciras School of Engineering and Technology, University of Cádiz, Ramón Puyol Ave., 11202 Algeciras, Spain; (M.I.R.-G.); (I.T.)
| | - David L. Sales
- Department of Materials Science Metallurgical Engineering and Inorganic Chemistry, Algeciras School of Engineering and Technology, Universidad de Cádiz, INNANOMAT, IMEYMAT, Ramón Puyol Ave., 11202 Algeciras, Spain;
| | - Rocío Sánchez-Miranda
- Laboratory and Research Section, Technical Department Acerinox Europa S.A.U., 11379 Los Barrios, Spain; (R.S.-M.); (J.F.A.)
| | - Juan F. Almagro
- Laboratory and Research Section, Technical Department Acerinox Europa S.A.U., 11379 Los Barrios, Spain; (R.S.-M.); (J.F.A.)
| | - Ignacio Turias
- MIS Group, Department of Computer Science Engineering, Algeciras School of Engineering and Technology, University of Cádiz, Ramón Puyol Ave., 11202 Algeciras, Spain; (M.I.R.-G.); (I.T.)
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10
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Bhatia AS, Saggi MK, Kais S. Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery. J Chem Inf Model 2023; 63:6476-6486. [PMID: 37603536 DOI: 10.1021/acs.jcim.3c01079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
In the drug discovery paradigm, the evaluation of absorption, distribution, metabolism, and excretion (ADME) and toxicity properties of new chemical entities is one of the most critical issues, which is a time-consuming process, immensely expensive, and poses formidable challenges in pharmaceutical R&D. In recent years, emerging technologies like artificial intelligence (AI), big data, and cloud technologies have garnered great attention to predict the ADME and toxicity of molecules. Currently, the blend of quantum computation and machine learning has attracted considerable attention in almost every field ranging from chemistry to biomedicine and several engineering disciplines as well. Quantum computers have the potential to bring advances in high-throughput experimental techniques and in screening billions of molecules by reducing development costs and time associated with the drug discovery process. Motivated by the efficiency of quantum kernel methods, we proposed a quantum machine learning (QML) framework consisting of a classical support vector classifier algorithm with a kernel-based quantum classifier. To demonstrate the feasibility of the proposed QML framework, the simplified molecular input line entry system (SMILES) notation-based string kernel, combined with a quantum support vector classifier, is used for the evaluation of chemical/drug ADME-Tox properties. The proposed quantum machine learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the quantum model achieved the best performance as compared to classical counterparts in terms of the area under the curve of the receiver operating characteristic curve (AUC ROC; 0.80-0.95) for predicting outcomes on ADME-Tox data sets for small molecules, with a different number of features. The deployment of the proposed framework in the pharmaceutical industry would be extremely valuable in making the best decisions possible.
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Affiliation(s)
- Amandeep Singh Bhatia
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Mandeep Kaur Saggi
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Sabre Kais
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
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11
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Sajjan M, Gupta R, Kale SS, Singh V, Kumaran K, Kais S. Physics-Inspired Quantum Simulation of Resonating Valence Bond States─A Prototypical Template for a Spin-Liquid Ground State. J Phys Chem A 2023; 127:8751-8764. [PMID: 37795926 DOI: 10.1021/acs.jpca.3c05172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Spin liquids─an emergent, exotic collective phase of matter─have garnered enormous attention in recent years. While experimentally many prospective candidates have been proposed and realized, theoretically modeling real materials that display such behavior may pose serious challenges due to the inherently high correlation content of such phases. Over the last few decades, the second-quantum revolution has been the harbinger of a novel computational paradigm capable of initiating a foundational evolution in computational physics. In this report, we strive to use the power of the latter to study a prototypical model, a spin-1/2-unit cell of a Kagome antiferromagnet. Extended lattices of such unit cells are known to possess a magnetically disordered spin-liquid ground state. We employ robust classical numerical techniques such as the density-matrix renormalization group (DMRG) to identify the nature of the ground state through a matrix-product state (MPS) formulation. We subsequently use the gained insight to construct an auxiliary Hamiltonian with reduced measurables and also design an ansatz that is modular and gate-efficient. With robust error-mitigation strategies, we are able to demonstrate that the said ansatz is capable of accurately representing the target ground state even on a real IBMQ backend within 1% accuracy in energy. Since the protocol is linearly scaling O(n) in the number of unit cells, gate requirements, and the number of measurements, it is straightforwardly extendable to larger Kagome lattices that can pave the way for efficient construction of spin-liquid ground states on a quantum device.
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Affiliation(s)
- Manas Sajjan
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, United States
| | - Rishabh Gupta
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, United States
| | - Sumit Suresh Kale
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, United States
| | - Vinit Singh
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, United States
| | - Keerthi Kumaran
- Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana 47907, United States
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, United States
| | - Sabre Kais
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, United States
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12
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Lyu N, Miano A, Tsioutsios I, Cortiñas RG, Jung K, Wang Y, Hu Z, Geva E, Kais S, Batista VS. Mapping Molecular Hamiltonians into Hamiltonians of Modular cQED Processors. J Chem Theory Comput 2023; 19:6564-6576. [PMID: 37733472 DOI: 10.1021/acs.jctc.3c00620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
We introduce a general method based on the operators of the Dyson-Masleev transformation to map the Hamiltonian of an arbitrary model system into the Hamiltonian of a circuit Quantum Electrodynamics (cQED) processor. Furthermore, we introduce a modular approach to programming a cQED processor with components corresponding to the mapping Hamiltonian. The method is illustrated as applied to quantum dynamics simulations of the Fenna-Matthews-Olson (FMO) complex and the spin-boson model of charge transfer. Beyond applications to molecular Hamiltonians, the mapping provides a general approach to implement any unitary operator in terms of a sequence of unitary transformations corresponding to powers of creation and annihilation operators of a single bosonic mode in a cQED processor.
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Affiliation(s)
- Ningyi Lyu
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Alessandro Miano
- Department of Applied Physics, Yale University, New Haven, Connecticut 06520, United States
- Department of Physics, Yale University, New Haven, Connecticut 06520, United States
- Yale Quantum Institute, Yale University, New Haven, Connecticut 06511, United States
| | - Ioannis Tsioutsios
- Department of Applied Physics, Yale University, New Haven, Connecticut 06520, United States
- Department of Physics, Yale University, New Haven, Connecticut 06520, United States
- Yale Quantum Institute, Yale University, New Haven, Connecticut 06511, United States
| | - Rodrigo G Cortiñas
- Department of Applied Physics, Yale University, New Haven, Connecticut 06520, United States
- Department of Physics, Yale University, New Haven, Connecticut 06520, United States
- Yale Quantum Institute, Yale University, New Haven, Connecticut 06511, United States
| | - Kenneth Jung
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Yuchen Wang
- Department of Chemistry, Department of Physics and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, United States
| | - Zixuan Hu
- Department of Chemistry, Department of Physics and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, United States
| | - Eitan Geva
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Sabre Kais
- Department of Chemistry, Department of Physics and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, United States
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
- Yale Quantum Institute, Yale University, New Haven, Connecticut 06511, United States
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13
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Pyrkov A, Aliper A, Bezrukov D, Lin YC, Polykovskiy D, Kamya P, Ren F, Zhavoronkov A. Quantum computing for near-term applications in generative chemistry and drug discovery. Drug Discov Today 2023; 28:103675. [PMID: 37331692 DOI: 10.1016/j.drudis.2023.103675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/22/2023] [Accepted: 06/13/2023] [Indexed: 06/20/2023]
Abstract
In recent years, drug discovery and life sciences have been revolutionized with machine learning and artificial intelligence (AI) methods. Quantum computing is touted to be the next most significant leap in technology; one of the main early practical applications for quantum computing solutions is predicted to be in quantum chemistry simulations. Here, we review the near-term applications of quantum computing and their advantages for generative chemistry and highlight the challenges that can be addressed with noisy intermediate-scale quantum (NISQ) devices. We also discuss the possible integration of generative systems running on quantum computers into established generative AI platforms.
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Affiliation(s)
- Alexey Pyrkov
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
| | - Alex Aliper
- Insilico Medicine AI Ltd, Masdar City, Abu Dhabi, United Arab Emirates
| | - Dmitry Bezrukov
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Yen-Chu Lin
- Insilico Medicine Taiwan Ltd, Taipei, Taiwan
| | | | | | - Feng Ren
- Insilico Medicine Shanghai Ltd, Shanghai, China
| | - Alex Zhavoronkov
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
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Li J, Jones BA, Kais S. Toward perturbation theory methods on a quantum computer. SCIENCE ADVANCES 2023; 9:eadg4576. [PMID: 37172088 PMCID: PMC10181180 DOI: 10.1126/sciadv.adg4576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Perturbation theory, used in a wide range of fields, is a powerful tool for approximate solutions to complex problems, starting from the exact solution of a related, simpler problem. Advances in quantum computing, especially over the past several years, provide opportunities for alternatives to classical methods. Here, we present a general quantum circuit estimating both the energy and eigenstates corrections that is far superior to the classical version when estimating second-order energy corrections. We demonstrate our approach as applied to the two-site extended Hubbard model. In addition to numerical simulations based on qiskit, results on IBM's quantum hardware are also presented. Our work offers a general approach to studying complex systems with quantum devices, with no training or optimization process needed to obtain the perturbative terms, which can be generalized to other Hamiltonian systems both in chemistry and physics.
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Affiliation(s)
- Junxu Li
- Department of Chemistry, Department of Physics and Astronomy, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA
- Department of Physics, College of Sciences, Northeastern University, Shenyang 110819, China
| | | | - Sabre Kais
- Department of Chemistry, Department of Physics and Astronomy, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA
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Fang X, Gu M, Wu J. Reliable emulation of complex functionals by active learning with error control. J Chem Phys 2022; 157:214109. [PMID: 36511540 DOI: 10.1063/5.0121805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
A statistical emulator can be used as a surrogate of complex physics-based calculations to drastically reduce the computational cost. Its successful implementation hinges on an accurate representation of the nonlinear response surface with a high-dimensional input space. Conventional "space-filling" designs, including random sampling and Latin hypercube sampling, become inefficient as the dimensionality of the input variables increases, and the predictive accuracy of the emulator can degrade substantially for a test input distant from the training input set. To address this fundamental challenge, we develop a reliable emulator for predicting complex functionals by active learning with error control (ALEC). The algorithm is applicable to infinite-dimensional mapping with high-fidelity predictions and a controlled predictive error. The computational efficiency has been demonstrated by emulating the classical density functional theory (cDFT) calculations, a statistical-mechanical method widely used in modeling the equilibrium properties of complex molecular systems. We show that ALEC is much more accurate than conventional emulators based on the Gaussian processes with "space-filling" designs and alternative active learning methods. In addition, it is computationally more efficient than direct cDFT calculations. ALEC can be a reliable building block for emulating expensive functionals owing to its minimal computational cost, controllable predictive error, and fully automatic features.
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Affiliation(s)
- Xinyi Fang
- Department of Statistics and Applied Probability, University of California, Santa Barbara, California 93106, USA
| | - Mengyang Gu
- Department of Statistics and Applied Probability, University of California, Santa Barbara, California 93106, USA
| | - Jianzhong Wu
- Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA
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Sager-Smith LM, Mazziotti DA. Reducing the Quantum Many-Electron Problem to Two Electrons with Machine Learning. J Am Chem Soc 2022; 144:18959-18966. [PMID: 36194786 DOI: 10.1021/jacs.2c07112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
An outstanding challenge in chemical computation is the many-electron problem where computational methodologies scale prohibitively with system size. The energy of any molecule can be expressed as a weighted sum of the energies of two-electron wave functions that are computable from only a two-electron calculation. Despite the physical elegance of this extended "aufbau" principle, the determination of the distribution of weights─geminal occupations─for general molecular systems has remained elusive. Here we introduce a new paradigm for electronic structure where approximate geminal-occupation distributions are "learned" via a convolutional neural network. We show that the neural network learns the N-representability conditions, constraints on the distribution for it to represent an N-electron system. By training on hydrocarbon isomers with only 2-7 carbon atoms, we are able to predict the energies for isomers of octane as well as hydrocarbons with 8-15 carbons. The present work demonstrates that machine learning can be used to reduce the many-electron problem to an effective two-electron problem, opening new opportunities for accurately predicting electronic structure.
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Affiliation(s)
- LeeAnn M Sager-Smith
- Department of Chemistry and The James Franck Institute, The University of Chicago, Chicago, Illinois60637, United States
| | - David A Mazziotti
- Department of Chemistry and The James Franck Institute, The University of Chicago, Chicago, Illinois60637, United States
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