Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference on Quantum Information

Simulating Quantum Systems in Biology, Chemistry, and Physics

Andrew G. White

Published Jan 1, 2011• 0 citations
Publisher

Abstract

In principle, it is possible to model any physical system exactly using quantum mechanics; in practice, it quickly becomes infeasible. Recognising this, Richard Feynman suggested that quantum systems be used to model quantum problems [1]. For example, the fundamental problem faced in quantum chemistry is the calculation of molecular properties, which are of practical importance in fields ranging from materials science to biochemistry. Within chemical precision, the total energy of a molecule as well as most other properties, can be calculated by solving the Schrödinger equation. However, the computational resources required to obtain exact solutions on a conventional computer generally increase exponentially with the number of atoms involved [1, 2]. In the late 1990’s an efficient algorithm was proposed to enable a quantum processor to calculate molecular energies using resources that increase only polynomially in the molecular size [2–4]. Despite the many different physical architectures that have been explored experimentally since that time—including ions, atoms, superconducting circuits, and photons—this appealing algorithm has not been demonstrated to date.

DOI
10.1364/icqi.2011.qwa1

Related papers

A variational eigenvalue solver on a photonic quantum processor

Alberto Peruzzo, Jarrod R. McClean, Peter Shadbolt +5 more

Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer. For quantum systems, where the physical dimension grows exponentially, finding the eigenvalues of certain operators is one such intractable problem and remains a fundamental challenge. The quantum phase estimation algorithm efficiently finds the eigenvalue of a given eigenvector but requires fully coherent evolution. Here we present an alternative approach that greatly reduces the requirements for coherent evolution and combine this method with a new approach to state preparation based on ansätze and classical optimization. We implement the algorithm by combining a highly reconfigurable photonic quantum processor with a conventional computer. We experimentally demonstrate the feasibility of this approach with an example from quantum chemistry—calculating the ground-state molecular energy for He–H+. The proposed approach drastically reduces the coherence time requirements, enhancing the potential of quantum resources available today and in the near future. Quantum computers promise to efficiently solve problems that would be practically impossible with a normal computer. Peruzzo et al. develop a variational computation approach that uses any available quantum resources and, with a photonic quantum processing unit, find the ground-state molecular energy of He–H+.

arXiv:1304.3061•Nature Communications•Jul 23, 2014•4,552 citations

Simulating quantum systems on quantum computers

Sean A. Fischer, C. Stephen Hellberg, Marco O. Lanzagorta +1 more

Bulletin of the American Physical Society•Mar 3, 2020•0 citations

Solid-state qubits

M. Fernando González-Zalba

The quantum world is fascinating. It presents a description of nature that defies our most rooted concepts about what reality is. For example, quantum objects possess \lq\lq spooky\rq\rq\ properties that allow them to be in multiple places at the same time, to move in different directions simultaneously, or to exist and not to exist. In other words, to live several parallel stories. Making use of these parallel stories to compute is the realm of quantum computation, a world in which information is processed using the laws of quantum physics. Nowadays, quantum computers are an object of an extensive experimental and theoretical research, since it is known that, on paper, these can solve many of the current computational challenges. These are the problems that even the most sophisticated supercomputers of today, or tomorrow, will not be able to solve.

arXiv:1801.06722•Jan 20, 2018•1 citation

Symulacje chemicznych układów kwantowych z wykorzystaniem komputerów kwantowych – przegląd algorytmów i ich eksperymentalna weryfikacja

Konrad Wojciechowski, Krzysztof Kurowski, Cezary Mazurek

Computer simulations using ever-increasing computing power and machine learning techniques allow advanced molecular modelling, molecular dynamics simulations and studies of intermolecular interactions. However, due to the complexity of biological systems and chemical processes at the molecular level, their accurate representation using classical computer models and techniques has faced a number of significant limitations for many years. A new and promising direction for the development of computational science and its potential applications in biochemistry is quantum computing and its integration with classical high-performance supercomputing systems. This article responds to the growing interest in the use of available quantum computers in exemplary applications. In this paper, we aim to provide an overview of the basic notions involved in the development of quantum algorithms and simulations related to issues at the interface of quantum chemistry and biochemistry. In addition, the article introduces the basic principles of performing simulations using the state-of-the-art quantum computers in the era of Noisy Intermediate-Scale Quantum (NISQ). Experimental results of the classical-quantum algorithm Variational Quantum Eigensolver (VQE) for example molecules H2 and CH+ are also presented. Despite the many shortcomings of currently available quantum computers, the analysed VQE algorithm proved to be effective in approximating the ground state of molecules using a minimal functional basis.

Postępy Biochemii•Jun 20, 2024•2 citations

Simulation of quantum systems on a realistic quantum computer

LEVI, Benjamin, Bertrand Georgeot

•Jan 1, 2004•0 citations

Ground-state-energy calculation for the water molecule on a superconducting quantum processor

Michael A. Jones, Harish J. Vallury, Lloyd C. L. Hollenberg

The accurate computation of properties of large molecular systems is classically infeasible and is one of the applications in which it is hoped that quantum computers will demonstrate an advantage over classical devices. However, due to the limitations of present-day quantum hardware, variational-hybrid algorithms introduced to tackle these problems struggle to meet the accuracy and precision requirements of chemical applications. Here, we apply the quantum computed moments (QCM) approach combined with a variety of noise-mitigation techniques to an eight qubit and spin-orbital representation of the water molecule (${\text{H}}_{2}\text{O}$). A noise-stable improvement on the variational result for a four-excitation trial state (circuit depth 25, 22 cz gates) was obtained, with the ground-state energy computed to be within $1.0\ifmmode\pm\else\textpm\fi{}0.8$ mHa of exact diagonalization in the 14 spin-orbital basis. Thus, the QCM approach, despite an increased number of measurements and noisy quantum hardware (cz error rates c.0.3% corresponding to expected error rates on the trial-state circuit of order 7%), is able to determine the ground-state energy of a nontrivial molecular system at the required accuracy (c.0.05%). To the best of our knowledge, these results are the largest calculations performed on a physical quantum computer to date to achieve millihartree agreement with exact diagonalization, and a promising indicator of how such hybrid approaches might scale to problems of interest in the low-error and fault-tolerant regimes as quantum computers develop.

arXiv:2311.02533•Physical Review Applied•Jun 7, 2024•8 citations
In principle, it is possible to model any physical system exactly using quantum mechanics; in practice, it quickly becomes infeasible. Recognising this, Richard Feynman suggested that quantum systems be used to model quantum problems [1]. For example, the fundamental problem faced in quantum chemistry is the calculation of molecular properties, which are of practical importance in fields ranging from materials science to biochemistry. Within chemical precision, the total energy of a molecule as well as most other properties, can be calculated by solving the Schrödinger equation. However, the computational resources required to obtain exact solutions on a conventional computer generally increase exponentially with the number of atoms involved [1, 2]. In the late 1990’s an efficient algorithm was proposed to enable a quantum processor to calculate molecular energies using resources that increase only polynomially in the molecular size [2–4]. Despite the many different physical architectures that have been explored experimentally since that time—including ions, atoms, superconducting circuits, and photons—this appealing algorithm has not been demonstrated to date.
Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer. For quantum systems, where the physical dimension grows exponentially, finding the eigenvalues of certain operators is one such intractable problem and remains a fundamental challenge. The quantum phase estimation algorithm efficiently finds the eigenvalue of a given eigenvector but requires fully coherent evolution. Here we present an alternative approach that greatly reduces the requirements for coherent evolution and combine this method with a new approach to state preparation based on ansätze and classical optimization. We implement the algorithm by combining a highly reconfigurable photonic quantum processor with a conventional computer. We experimentally demonstrate the feasibility of this approach with an example from quantum chemistry—calculating the ground-state molecular energy for He–H+. The proposed approach drastically reduces the coherence time requirements, enhancing the potential of quantum resources available today and in the near future. Quantum computers promise to efficiently solve problems that would be practically impossible with a normal computer. Peruzzo et al. develop a variational computation approach that uses any available quantum resources and, with a photonic quantum processing unit, find the ground-state molecular energy of He–H+.
The quantum world is fascinating. It presents a description of nature that defies our most rooted concepts about what reality is. For example, quantum objects possess \lq\lq spooky\rq\rq\ properties that allow them to be in multiple places at the same time, to move in different directions simultaneously, or to exist and not to exist. In other words, to live several parallel stories. Making use of these parallel stories to compute is the realm of quantum computation, a world in which information is processed using the laws of quantum physics. Nowadays, quantum computers are an object of an extensive experimental and theoretical research, since it is known that, on paper, these can solve many of the current computational challenges. These are the problems that even the most sophisticated supercomputers of today, or tomorrow, will not be able to solve.
Computer simulations using ever-increasing computing power and machine learning techniques allow advanced molecular modelling, molecular dynamics simulations and studies of intermolecular interactions. However, due to the complexity of biological systems and chemical processes at the molecular level, their accurate representation using classical computer models and techniques has faced a number of significant limitations for many years. A new and promising direction for the development of computational science and its potential applications in biochemistry is quantum computing and its integration with classical high-performance supercomputing systems. This article responds to the growing interest in the use of available quantum computers in exemplary applications. In this paper, we aim to provide an overview of the basic notions involved in the development of quantum algorithms and simulations related to issues at the interface of quantum chemistry and biochemistry. In addition, the article introduces the basic principles of performing simulations using the state-of-the-art quantum computers in the era of Noisy Intermediate-Scale Quantum (NISQ). Experimental results of the classical-quantum algorithm Variational Quantum Eigensolver (VQE) for example molecules H2 and CH+ are also presented. Despite the many shortcomings of currently available quantum computers, the analysed VQE algorithm proved to be effective in approximating the ground state of molecules using a minimal functional basis.
The accurate computation of properties of large molecular systems is classically infeasible and is one of the applications in which it is hoped that quantum computers will demonstrate an advantage over classical devices. However, due to the limitations of present-day quantum hardware, variational-hybrid algorithms introduced to tackle these problems struggle to meet the accuracy and precision requirements of chemical applications. Here, we apply the quantum computed moments (QCM) approach combined with a variety of noise-mitigation techniques to an eight qubit and spin-orbital representation of the water molecule (H2O{\text{H}}_{2}\text{O}H2​O). A noise-stable improvement on the variational result for a four-excitation trial state (circuit depth 25, 22 cz gates) was obtained, with the ground-state energy computed to be within $1.0\ifmmode\pm\else\textpm\fi{}0.8$ mHa of exact diagonalization in the 14 spin-orbital basis. Thus, the QCM approach, despite an increased number of measurements and noisy quantum hardware (cz error rates c.0.3% corresponding to expected error rates on the trial-state circuit of order 7%), is able to determine the ground-state energy of a nontrivial molecular system at the required accuracy (c.0.05%). To the best of our knowledge, these results are the largest calculations performed on a physical quantum computer to date to achieve millihartree agreement with exact diagonalization, and a promising indicator of how such hybrid approaches might scale to problems of interest in the low-error and fault-tolerant regimes as quantum computers develop.