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Quantum chemistry simulations of some industrially relevant molecules are reported, employing variational quantum algorithms for near-term quantum devices. The energies and dipole moments are calculated along the dissociation curves for…

The recent developments of quantum computing present potential novel pathways for quantum chemistry, as the increased computational power of quantum computers could be harnessed to naturally encode and solve electronic structure problems.…

Quantum Physics · Physics 2024-03-01 Lila Cadi Tazi , Alex J. W. Thom

The variational quantum eigensolver (VQE) is one of the most appealing quantum algorithms to simulate electronic structure properties of molecules on near-term noisy intermediate-scale quantum devices. In this work, we generalize the VQE…

Quantum Physics · Physics 2022-06-09 Jie Liu , Lingyun Wan , Zhenyu Li , Jinlong Yang

Quantum systems have historically been formidable to simulate using classical computational methods, particularly as the system size grows. In recent years, advancements in quantum computing technology have offered new opportunities for…

Quantum Physics · Physics 2023-09-06 Jinao Wang , Rimika Jaiswal

Quantum mechanics has introduced a new theoretical framework for the study of molecules, enabling the prediction of properties and dynamics through the solution of the Schr\"odinger equation applied to these systems. However, solving this…

Quantum computers can be used to address molecular structure, materials science and condensed matter physics problems, which currently stretch the limits of existing high-performance computing resources. Finding exact numerical solutions to…

For the variational quantum eigensolver we propose to generate trial wavefunctions from a small amount of selected Pauli terms of the problem Hamiltonian. Two different approaches, one inspired by the quantum approximate optimization…

Quantum Physics · Physics 2019-08-27 Gian Salis , Nikolaj Moll

We address the task of estimating the ground-state energy of Hamiltonians coming from chemistry. We study numerically the behavior of a digital-analog variational quantum eigensolver for the H2, LiH and BeH2 molecules, and we observe that…

Quantum Physics · Physics 2023-04-25 Antoine Michel , Sebastian Grijalva , Loïc Henriet , Christophe Domain , Antoine Browaeys

We develop an extension of the variational quantum eigensolver (VQE) algorithm - multistate, contracted VQE (MC-VQE) - that allows for the efficient computation of the transition energies between the ground state and several low-lying…

Quantum Physics · Physics 2019-06-19 Robert M. Parrish , Edward G. Hohenstein , Peter L. McMahon , Todd J. Martinez

Variational quantum eigensolvers (VQEs) are among the most promising quantum algorithms for solving electronic structure problems in quantum chemistry, particularly during the Noisy Intermediate-Scale Quantum (NISQ) era. In this study, we…

Quantum Physics · Physics 2026-05-07 Abel Carreras , David Casanova , Román Orús

Reflecting the increasing interest in quantum computing, the variational quantum eigensolver (VQE) has attracted much attentions as a possible application of near-term quantum computers. Although the VQE has often been applied to quantum…

Quantum Physics · Physics 2022-12-07 Yasutaka Nishida , Fumihiko Aiga

Electron-molecule collisions play a central role in both natural processes and modern technological applications, particularly in plasma processing. Conventional computational strategies such as the R-matrix method have been widely adopted…

Quantum Physics · Physics 2026-05-13 Dario Picozzi , Jonathan Tennyson , Vincent Graves , Jimena D. Gorfinkiel

Solving electronic structure problems is considered one of the most promising applications of quantum computing. However, due to limitations imposed by the coherence time of qubits in the Noisy Intermediate Scale Quantum (NISQ) era or the…

Quantum Physics · Physics 2025-03-20 Shuo Sun , Chandan Kumar , Kevin Shen , Elvira Shishenina , Christian B. Mendl

Variational quantum eigensolver(VQE) typically minimizes energy with hybrid quantum-classical optimization, which aims to find the ground state. Here, we propose a VQE by minimizing energy variance, which is called as variance-VQE(VVQE).…

Quantum Physics · Physics 2020-06-30 Dan-Bo Zhang , Zhan-Hao Yuan , Tao Yin

Variational algorithms for strongly correlated chemical and materials systems are one of the most promising applications of near-term quantum computers. We present an extension to the variational quantum eigensolver that approximates the…

Quantum Physics · Physics 2020-08-26 William J. Huggins , Joonho Lee , Unpil Baek , Bryan O'Gorman , K. Birgitta Whaley

Variational Quantum Eigensolvers (VQEs) represent a promising approach to computing molecular ground states and energies on modern quantum computers. These approaches use a classical computer to optimize the parameters of a trial wave…

Quantum computing has recently exhibited great potentials in predicting chemical properties for various applications in drug discovery, material design, and catalyst optimization. Progress has been made in simulating small molecules, such…

This work studies the variational quantum eigensolver algorithm, designed to determine the ground state of a quantum mechanical system by combining classical and quantum hardware. Methods of reducing the number of required qubit…

Quantum Physics · Physics 2022-03-01 R. J. P. T. de Keijzer , V. E. Colussi , B. Škorić , S. J. J. M. F. Kokkelmans

Variational quantum eigensolver (VQE) is a hybrid quantum-classical technique that leverages noisy intermediate scale quantum (NISQ) hardware to obtain the minimum eigenvalue of a model Hamiltonian. VQE has so far been used to simulate…

Quantum Physics · Physics 2022-08-18 Marwa H. Farag , Joydip Ghosh

Computing excited-state properties of molecules and solids is considered one of the most important near-term applications of quantum computers. While many of the current excited-state quantum algorithms differ in circuit architecture,…

Quantum Physics · Physics 2024-03-19 Carlos L. Benavides-Riveros , Yuchen Wang , Samuel Warren , David A. Mazziotti