1
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Pal S, Bhattacharya M, Dash S, Lee SS, Chakraborty C. Future Potential of Quantum Computing and Simulations in Biological Science. Mol Biotechnol 2024; 66:2201-2218. [PMID: 37717248 DOI: 10.1007/s12033-023-00863-3] [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: 06/14/2023] [Accepted: 08/16/2023] [Indexed: 09/19/2023]
Abstract
The review article presents the recent progress in quantum computing and simulation within the field of biological sciences. The article is designed mainly into two portions: quantum computing and quantum simulation. In the first part, significant aspects of quantum computing was illustrated, such as quantum hardware, quantum RAM and big data, modern quantum processors, qubit, superposition effect in quantum computation, quantum interference, quantum entanglement, and quantum logic gates. Simultaneously, in the second part, vital features of the quantum simulation was illustrated, such as the quantum simulator, algorithms used in quantum simulations, and the use of quantum simulation in biological science. Finally, the review provides exceptional views to future researchers about different aspects of quantum simulation in biological science.
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Affiliation(s)
- Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, 756020, India
| | - Snehasish Dash
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do, 24252, Republic of Korea
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, 700126, India.
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2
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Durant TJS, Knight E, Nelson B, Dudgeon S, Lee SJ, Walliman D, Young HP, Ohno-Machado L, Schulz WL. A primer for quantum computing and its applications to healthcare and biomedical research. J Am Med Inform Assoc 2024; 31:1774-1784. [PMID: 38934288 PMCID: PMC11258415 DOI: 10.1093/jamia/ocae149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVES To introduce quantum computing technologies as a tool for biomedical research and highlight future applications within healthcare, focusing on its capabilities, benefits, and limitations. TARGET AUDIENCE Investigators seeking to explore quantum computing and create quantum-based applications for healthcare and biomedical research. SCOPE Quantum computing requires specialized hardware, known as quantum processing units, that use quantum bits (qubits) instead of classical bits to perform computations. This article will cover (1) proposed applications where quantum computing offers advantages to classical computing in biomedicine; (2) an introduction to how quantum computers operate, tailored for biomedical researchers; (3) recent progress that has expanded access to quantum computing; and (4) challenges, opportunities, and proposed solutions to integrate quantum computing in biomedical applications.
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Affiliation(s)
- Thomas J S Durant
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06520, United States
- Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT 06510, United States
| | - Elizabeth Knight
- Yale School of Medicine, Yale University, New Haven, CT 06510, United States
| | - Brent Nelson
- Newport Healthcare, Minneapolis, MN 55435, United States
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55454, United States
| | - Sarah Dudgeon
- Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, United States
| | - Seung J Lee
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06520, United States
- Yale School of Medicine, Yale University, New Haven, CT 06510, United States
| | | | - Hobart P Young
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06520, United States
| | - Lucila Ohno-Machado
- Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT 06510, United States
| | - Wade L Schulz
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06520, United States
- Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT 06510, United States
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3
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Nałęcz-Charkiewicz K, Charkiewicz K, Nowak RM. Quantum computing in bioinformatics: a systematic review mapping. Brief Bioinform 2024; 25:bbae391. [PMID: 39140856 PMCID: PMC11323091 DOI: 10.1093/bib/bbae391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/05/2024] [Accepted: 07/26/2024] [Indexed: 08/15/2024] Open
Abstract
The field of quantum computing (QC) is expanding, with efforts being made to apply it to areas previously covered by classical algorithms and methods. Bioinformatics is one such domain that is developing in terms of QC. This article offers a broad mapping review of methods and algorithms of QC in bioinformatics, marking the first of its kind. It presents an overview of the domain and aids researchers in identifying further research directions in the early stages of this field of knowledge. The work presented here shows the current state-of-the-art solutions, focuses on general future directions, and highlights the limitations of current methods. The gathered data includes a comprehensive list of identified methods along with descriptions, classifications, and elaborations of their advantages and disadvantages. Results are presented not just in a descriptive table but also in an aggregated and visual format.
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Affiliation(s)
- Katarzyna Nałęcz-Charkiewicz
- Artificial Intelligence Division, Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
| | | | - Robert M Nowak
- Artificial Intelligence Division, Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
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4
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Misiewicz J, Evangelista FA. Implementation of the Projective Quantum Eigensolver on a Quantum Computer. J Phys Chem A 2024; 128:2220-2235. [PMID: 38452262 PMCID: PMC10961848 DOI: 10.1021/acs.jpca.3c07429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/29/2024] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
We study the performance of our previously proposed projective quantum eigensolver (PQE) on IBM's quantum hardware in conjunction with error mitigation techniques. For a single qubit model of H2, we find that we are able to obtain energies within 4 millihartree (2.5 kcal/mol) of the exact energy along the entire potential energy curve, with the accuracy limited by both the stochastic error and the inconsistent performance of the IBM devices. We find that an optimization algorithm using direct inversion of the iterative subspace can converge swiftly, even to excited states, but stochastic noise can prompt large parameter updates. For the 4-site transverse-field Ising model at its critical point, PQE with an appropriate application of qubit tapering can recover 99% of the correlation energy, even after discarding several parameters. The large number of CNOT gates needed for the additional parameters introduces a concomitant error that, on the IBM devices, results in a loss of accuracy despite the increased expressivity of the trial state. Error extrapolation techniques and tapering or postselection are recommended to mitigate errors in PQE hardware experiments.
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Affiliation(s)
| | - Francesco A. Evangelista
- Department of Chemistry and
Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
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5
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Chae E, Choi J, Kim J. An elementary review on basic principles and developments of qubits for quantum computing. NANO CONVERGENCE 2024; 11:11. [PMID: 38498068 PMCID: PMC10948723 DOI: 10.1186/s40580-024-00418-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/04/2024] [Indexed: 03/19/2024]
Abstract
An elementary review on principles of qubits and their prospects for quantum computing is provided. Due to its rapid development, quantum computing has attracted considerable attention as a core technology for the next generation and has demonstrated its potential in simulations of exotic materials, molecular structures, and theoretical computer science. To achieve fully error-corrected quantum computers, building a logical qubit from multiple physical qubits is crucial. The number of physical qubits needed depends on their error rates, making error reduction in physical qubits vital. Numerous efforts to reduce errors are ongoing in both existing and emerging quantum systems. Here, the principle and development of qubits, as well as the current status of the field, are reviewed to provide information to researchers from various fields and give insights into this promising technology.
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Affiliation(s)
- Eunmi Chae
- Department of Physics, Korea University, Seoul , 02841, Republic of Korea.
| | - Joonhee Choi
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA.
| | - Junki Kim
- SKKU Advanced Institute of Nanotechnology (SAINT) & Department of Nano Science and Technology, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
- Department of Nano Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
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6
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Pal S, Bhattacharya M, Lee SS, Chakraborty C. Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics. Mol Biotechnol 2024; 66:163-178. [PMID: 37244882 PMCID: PMC10224669 DOI: 10.1007/s12033-023-00765-4] [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: 03/14/2023] [Accepted: 05/04/2023] [Indexed: 05/29/2023]
Abstract
Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology.
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Affiliation(s)
- Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, 756020, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do, 24252, Republic of Korea
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, 700126, India.
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7
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Bonde B, Patil P, Choubey B. The Future of Drug Development with Quantum Computing. Methods Mol Biol 2024; 2716:153-179. [PMID: 37702939 DOI: 10.1007/978-1-0716-3449-3_7] [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: 09/14/2023]
Abstract
Novel medication development is a time-consuming and expensive multistage procedure. Recent technology developments have lowered timeframes, complexity, and cost dramatically. Current research projects are driven by AI and machine learning computational models. This chapter will introduce quantum computing (QC) to drug development issues and provide an in-depth discussion of how quantum computing may be used to solve various drug discovery problems. We will first discuss the fundamentals of QC, a review of known Hamiltonians, how to apply Hamiltonians to drug discovery challenges, and what the noisy intermediate-scale quantum (NISQ) era methods and their limitations are.We will further discuss how these NISQ era techniques can aid with specific drug discovery challenges, including protein folding, molecular docking, AI-/ML-based optimization, and novel modalities for small molecules and RNA secondary structures. Consequently, we will discuss the latest QC landscape's opportunities and challenges.
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Affiliation(s)
- Bhushan Bonde
- Evotec (UK) Ltd., Oxfordshire, UK.
- Digital Futures Institute, University of Suffolk, Ipswich, UK.
| | - Pratik Patil
- Evotec (UK) Ltd., Oxfordshire, UK
- Digital Futures Institute, University of Suffolk, Ipswich, UK
| | - Bhaskar Choubey
- Digital Futures Institute, University of Suffolk, Ipswich, UK
- Chair of Analogue Circuits and Image Sensors, Siegen University, Siegen, Germany
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8
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Matarèse BFE, Rusin A, Seymour C, Mothersill C. Quantum Biology and the Potential Role of Entanglement and Tunneling in Non-Targeted Effects of Ionizing Radiation: A Review and Proposed Model. Int J Mol Sci 2023; 24:16464. [PMID: 38003655 PMCID: PMC10671017 DOI: 10.3390/ijms242216464] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/01/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
It is well established that cells, tissues, and organisms exposed to low doses of ionizing radiation can induce effects in non-irradiated neighbors (non-targeted effects or NTE), but the mechanisms remain unclear. This is especially true of the initial steps leading to the release of signaling molecules contained in exosomes. Voltage-gated ion channels, photon emissions, and calcium fluxes are all involved but the precise sequence of events is not yet known. We identified what may be a quantum entanglement type of effect and this prompted us to consider whether aspects of quantum biology such as tunneling and entanglement may underlie the initial events leading to NTE. We review the field where it may be relevant to ionizing radiation processes. These include NTE, low-dose hyper-radiosensitivity, hormesis, and the adaptive response. Finally, we present a possible quantum biological-based model for NTE.
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Affiliation(s)
- Bruno F. E. Matarèse
- Department of Haematology, University of Cambridge, Cambridge CB2 1TN, UK;
- Department of Physics, University of Cambridge, Cambridge CB2 1TN, UK
| | - Andrej Rusin
- Department of Biology, McMaster University, Hamilton, ON L8S 4L8, Canada; (A.R.); (C.S.)
| | - Colin Seymour
- Department of Biology, McMaster University, Hamilton, ON L8S 4L8, Canada; (A.R.); (C.S.)
| | - Carmel Mothersill
- Department of Biology, McMaster University, Hamilton, ON L8S 4L8, Canada; (A.R.); (C.S.)
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9
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Buonaiuto G, Gargiulo F, De Pietro G, Esposito M, Pota M. Best practices for portfolio optimization by quantum computing, experimented on real quantum devices. Sci Rep 2023; 13:19434. [PMID: 37940680 PMCID: PMC10632408 DOI: 10.1038/s41598-023-45392-w] [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: 02/18/2023] [Accepted: 10/19/2023] [Indexed: 11/10/2023] Open
Abstract
In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.
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Affiliation(s)
- Giuseppe Buonaiuto
- Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), 80131, Naples, Italy
| | - Francesco Gargiulo
- Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), 80131, Naples, Italy
| | - Giuseppe De Pietro
- Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), 80131, Naples, Italy
| | - Massimo Esposito
- Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), 80131, Naples, Italy
| | - Marco Pota
- Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), 80131, Naples, Italy.
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10
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Chakraborty C, Bhattacharya M, Dhama K, Lee SS. Quantum computing on nucleic acid research: Approaching towards next-generation computing. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 33:53-56. [PMID: 37449046 PMCID: PMC10336077 DOI: 10.1016/j.omtn.2023.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha 756020, India
| | - Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243122, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si, Gangwon-do 24252, Republic of Korea
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11
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Esposito M, Baravalle L. The machine-organism relation revisited. HISTORY AND PHILOSOPHY OF THE LIFE SCIENCES 2023; 45:34. [PMID: 37439889 DOI: 10.1007/s40656-023-00587-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 06/03/2023] [Indexed: 07/14/2023]
Abstract
This article addresses some crucial assumptions that are rarely acknowledged when organisms and machines are compared. We begin by presenting a short historical reconstruction of the concept of "machine." We show that there has never been a unique and widely accepted definition of "machine" and that the extant definitions are based on specific technologies. Then we argue that, despite the concept's ambiguity, we can still defend a more robust, specific, and useful notion of machine analogy that accounts for successful strategies in connecting specific devices (or mechanisms) with particular living phenomena. For that purpose, we distinguish between what we call "generic identity" and proper "machine analogy." We suggest that "generic identity"-which, roughly stated, presumes that some sort of vague similarity might exist between organisms and machines-is a source of the confusion haunting many persistent disagreements and that, accordingly, it should be dismissed. Instead, we endorse a particular form of "machine analogy" where the relation between organic phenomena and mechanical devices is not generic but specific and grounded on the identification of shared "invariants." We propose that the machine analogy is a kind of analogy as proportion and we elucidate how this is used or might be used in scientific practices. We finally argue that while organisms are not machines in a generic sense, they might share many robust "invariants," which justify the scientists' use of machine analogies for grasping living phenomena.
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Affiliation(s)
- Maurizio Esposito
- University of Lisbon (Centro Interuniversitário de História das Ciências e da Tecnologia), 1749-016, Lisbon, Portugal.
| | - Lorenzo Baravalle
- University of Lisbon (Centro de Filosofia das Ciências da Universidade de Lisboa), 1749-016, Lisbon, Portugal
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12
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Motta M, Jones GO, Rice JE, Gujarati TP, Sakuma R, Liepuoniute I, Garcia JM, Ohnishi YY. Quantum chemistry simulation of ground- and excited-state properties of the sulfonium cation on a superconducting quantum processor. Chem Sci 2023; 14:2915-2927. [PMID: 36937596 PMCID: PMC10016331 DOI: 10.1039/d2sc06019a] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/14/2023] [Indexed: 02/17/2023] Open
Abstract
The computational description of correlated electronic structure, and particularly of excited states of many-electron systems, is an anticipated application for quantum devices. An important ramification is to determine the dominant molecular fragmentation pathways in photo-dissociation experiments of light-sensitive compounds, like sulfonium-based photo-acid generators used in photolithography. Here we simulate the static and dynamical electronic structure of the H3S+ molecule, taken as a minimal model of a triply-bonded sulfur cation, on a superconducting quantum processor of the IBM Falcon architecture. To this end, we generalize a qubit reduction technique termed entanglement forging or EF [A. Eddins et al., Phys. Rev. X Quantum, 2022, 3, 010309], currently restricted to the evaluation of ground-state energies, to the treatment of molecular properties. While in a conventional quantum simulation a qubit represents a spin-orbital, within EF a qubit represents a spatial orbital, reducing the number of required qubits by half. We combine the generalized EF with quantum subspace expansion [W. Colless et al., Phys. Rev. X, 2018, 8, 011021], a technique used to project the time-independent Schrodinger equation for ground- and excited-states in a subspace. To enable experimental demonstration of this algorithmic workflow, we deploy a sequence of error-mitigation techniques. We compute dipole structure factors and partial atomic charges along ground- and excited-state potential energy curves, revealing the occurrence of homo- and heterolytic fragmentation. This study is an important step towards the computational description of photo-dissociation on near-term quantum devices, as it can be generalized to other photodissociation processes and naturally extended in different ways to achieve more realistic simulations.
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Affiliation(s)
- Mario Motta
- IBM Quantum, IBM Research - Almaden 650 Harry Road San Jose 95120 CA USA
| | - Gavin O Jones
- IBM Quantum, IBM Research - Almaden 650 Harry Road San Jose 95120 CA USA
| | - Julia E Rice
- IBM Quantum, IBM Research - Almaden 650 Harry Road San Jose 95120 CA USA
| | - Tanvi P Gujarati
- IBM Quantum, IBM Research - Almaden 650 Harry Road San Jose 95120 CA USA
| | - Rei Sakuma
- Materials Informatics Initiative, RD Technology & Digital Transformation Center, JSR Corporation 3-103-9, Tonomachi, Kawasaki-ku Kawasaki 210-0821 Kanagawa Japan
| | - Ieva Liepuoniute
- IBM Quantum, IBM Research - Almaden 650 Harry Road San Jose 95120 CA USA
| | - Jeannette M Garcia
- IBM Quantum, IBM Research - Almaden 650 Harry Road San Jose 95120 CA USA
| | - Yu-Ya Ohnishi
- Materials Informatics Initiative, RD Technology & Digital Transformation Center, JSR Corporation 3-103-9, Tonomachi, Kawasaki-ku Kawasaki 210-0821 Kanagawa Japan
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13
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Marchetti L, Nifosì R, Martelli PL, Da Pozzo E, Cappello V, Banterle F, Trincavelli ML, Martini C, D’Elia M. Quantum computing algorithms: getting closer to critical problems in computational biology. Brief Bioinform 2022; 23:bbac437. [PMID: 36220772 PMCID: PMC9677474 DOI: 10.1093/bib/bbac437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/15/2022] [Accepted: 09/08/2022] [Indexed: 12/14/2022] Open
Abstract
The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.
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Affiliation(s)
- Laura Marchetti
- University of Pisa, Department of Pharmacy, via Bonanno 6, 56126 Pisa Italy
| | - Riccardo Nifosì
- NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore, P.zza San Silvestro 12, 56127 Pisa Italy
| | - Pier Luigi Martelli
- University of Bologna, Department of Pharmacy and Biotechnology, via San Giacomo 9/2, 40126 Bologna Italy
| | - Eleonora Da Pozzo
- University of Pisa, Department of Pharmacy, via Bonanno 6, 56126 Pisa Italy
| | - Valentina Cappello
- Italian Institute of Technology, Center for Materials Interfaces, Viale Rinaldo Piaggio 34, 56025 Pontedera (PI), Italy
| | | | | | - Claudia Martini
- University of Pisa, Department of Pharmacy, via Bonanno 6, 56126 Pisa Italy
| | - Massimo D’Elia
- University of Pisa, Department of Physics, Largo Bruno Pontecorvo 3, 56127, Pisa Italy
- INFN, Sezione di Pisa, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy
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14
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Arsiccio A, Pisano R, Shea JE. A New Transfer Free Energy Based Implicit Solvation Model for the Description of Disordered and Folded Proteins. J Phys Chem B 2022; 126:6180-6190. [PMID: 35968960 DOI: 10.1021/acs.jpcb.2c03980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Most biological events occur on time scales that are difficult to access using conventional all-atom molecular dynamics simulations in explicit solvent. Implicit solvent techniques offer a promising solution to this problem, alleviating the computational cost associated with the simulation of large systems and accelerating the sampling compared to explicit solvent models. The substitution of water molecules by a mean field, however, introduces simplifications that may penalize accuracy and impede the prediction of certain physical properties. We demonstrate that existing implicit solvent models developed using a transfer free energy approach, while satisfactory at reproducing the folding behavior of globular proteins, fare less well in characterizing the conformational properties of intrinsically disordered proteins. We develop a new implicit solvent model that maximizes the degree of accuracy for both disordered and folded proteins. We show, by comparing the simulation outputs to experimental data, that in combination with the a99SB-disp force field, the implicit solvent model can describe both disordered (aβ40, PaaA2, and drkN SH3) and folded ((AAQAA)3, CLN025, Trp-cage, and GTT) peptides. Our implicit solvent model permits a computationally efficient investigation of proteins containing both ordered and disordered regions, as well as the study of the transition between ordered and disordered protein states.
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Affiliation(s)
- Andrea Arsiccio
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, United States
| | - Roberto Pisano
- Molecular Engineering Laboratory, Department of Applied Science and Technology, Politecnico di Torino, 24 corso Duca degli Abruzzi, Torino 10129, Italy
| | - Joan-Emma Shea
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, United States.,Department of Physics, University of California, Santa Barbara, California 93106, United States
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Andersson MP, Jones MN, Mikkelsen KV, You F, Mansouri SS. Quantum computing for chemical and biomolecular product design. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2021.100754] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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16
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Pivoluska M, Plesch M. Implementation of quantum compression on IBM quantum computers. Sci Rep 2022; 12:5841. [PMID: 35393490 PMCID: PMC8991190 DOI: 10.1038/s41598-022-09881-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 03/28/2022] [Indexed: 11/09/2022] Open
Abstract
Advances in development of quantum computing processors brought ample opportunities to test the performance of various quantum algorithms with practical implementations. In this paper we report on implementations of quantum compression algorithm that can efficiently compress unknown quantum information. We restricted ourselves to compression of three pure qubits into two qubits, as the complexity of even such a simple implementation is barely within the reach of today's quantum processors. We implemented the algorithm on IBM quantum processors with two different topological layouts-a fully connected triangle processor and a partially connected line processor. It turns out that the incomplete connectivity of the line processor affects the performance only minimally. On the other hand, it turns out that the transpilation, i.e. compilation of the circuit into gates physically available to the quantum processor, crucially influences the result. We also have seen that the compression followed by immediate decompression is, even for such a simple case, on the edge or even beyond the capabilities of currently available quantum processors.
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Affiliation(s)
- Matej Pivoluska
- Institute of Physics, Slovak Academy of Sciences, Dúbravská cesta 9, 841 04, Bratislava, Slovak Republic.,Institute of Computer Science, Masaryk University, Šumavská 416, 602 00, Brno, Czech Republic
| | - Martin Plesch
- Institute of Physics, Slovak Academy of Sciences, Dúbravská cesta 9, 841 04, Bratislava, Slovak Republic. .,Institute of Computer Science, Masaryk University, Šumavská 416, 602 00, Brno, Czech Republic.
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RNA folding using quantum computers. PLoS Comput Biol 2022; 18:e1010032. [PMID: 35404931 PMCID: PMC9022793 DOI: 10.1371/journal.pcbi.1010032] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 04/21/2022] [Accepted: 03/18/2022] [Indexed: 11/19/2022] Open
Abstract
The 3-dimensional fold of an RNA molecule is largely determined by patterns of intramolecular hydrogen bonds between bases. Predicting the base pairing network from the sequence, also referred to as RNA secondary structure prediction or RNA folding, is a nondeterministic polynomial-time (NP)-complete computational problem. The structure of the molecule is strongly predictive of its functions and biochemical properties, and therefore the ability to accurately predict the structure is a crucial tool for biochemists. Many methods have been proposed to efficiently sample possible secondary structure patterns. Classic approaches employ dynamic programming, and recent studies have explored approaches inspired by evolutionary and machine learning algorithms. This work demonstrates leveraging quantum computing hardware to predict the secondary structure of RNA. A Hamiltonian written in the form of a Binary Quadratic Model (BQM) is derived to drive the system toward maximizing the number of consecutive base pairs while jointly maximizing the average length of the stems. A Quantum Annealer (QA) is compared to a Replica Exchange Monte Carlo (REMC) algorithm programmed with the same objective function, with the QA being shown to be highly competitive at rapidly identifying low energy solutions. The method proposed in this study was compared to three algorithms from literature and, despite its simplicity, was found to be competitive on a test set containing known structures with pseudoknots. The recent FDA approval of mRNA-based vaccines has increased public interest in synthetically designed RNA molecules. RNA molecules fold into complex secondary structures which determine their molecular properties and in part their efficacy. Determining the folded structure of an RNA molecule is a computationally challenging task with exponential scaling that is intractable to solve exactly, and therefore approximate methods are used. Quantum computing technology offers a new approach to finding approximate solutions to problems with exponential scaling. We formulate a simplistic, yet effective, model of RNA folding that can easily be mapped to quantum computers and we show that currently available quantum computing hardware is competitive with classical methods.
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Mallow GM, Hornung A, Barajas JN, Rudisill SS, An HS, Samartzis D. Quantum Computing: The Future of Big Data and Artificial Intelligence in Spine. Spine Surg Relat Res 2022; 6:93-98. [PMID: 35478980 PMCID: PMC8995124 DOI: 10.22603/ssrr.2021-0251] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/23/2021] [Indexed: 11/05/2022] Open
Affiliation(s)
- Greg Michael Mallow
- Department of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center
| | - Alexander Hornung
- Department of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center
| | - Juan Nicolas Barajas
- Department of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center
| | - Samuel S. Rudisill
- Department of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center
| | - Howard S. An
- Department of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center
| | - Dino Samartzis
- The International Spine Research and Innovation Initiative, Rush University Medical Center
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Toward the institutionalization of quantum computing in pharmaceutical research. Drug Discov Today 2021; 27:378-383. [PMID: 34688911 DOI: 10.1016/j.drudis.2021.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/13/2021] [Accepted: 10/15/2021] [Indexed: 12/24/2022]
Abstract
Innovative pharmaceutical companies have started to explore quantum computing (QC). In this article, we provide a collective industry perspective from QC domain leaders at leading pharmaceutical companies. There are immediate nonfinancial benefits in engaging with QC, some likely financial returns in the short term in drug development, manufacturing, and supply chain, and potentially large scientific benefits in drug discovery long term. We discuss the required activities for institutionalizing QC: how to create an understanding of QC among researchers and management, which and how to deploy external resources, and how to identify the problems to be addressed with QC. If (and once) deployable, QC will likely have a similar trajectory to that of computer-aided drug design (CADD) and artificial intelligence (AI) during the 1990s and 2010s, respectively.
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