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The variational quantum eigensolver (VQE) is one of the most promising algorithms for low-lying eigenstates calculation on Noisy Intermediate-Scale Quantum (NISQ) computers. Specifically, VQE has achieved great success for ground state…

Numerical Analysis · Mathematics 2025-12-19 Hengzhun Chen , Yingzhou Li , Bichen Lu , Jianfeng Lu

We simulate a long-range extended Ising model in one dimension using a hybrid quantum algorithm, namely Variational Quantum Eigensolver (VQE). In this quantum simulation, we investigate how quantum resources scale with system size and…

Quantum Physics · Physics 2026-04-21 Tanya Keshari , Debasis Sadhukhan

Finding accurate ground state energy of a many-body system has been a major challenge in quantum chemistry. The integration of classic and quantum computers has shed new light on resolving this outstanding problem. Here we propose…

The variational quantum eigensolver (VQE) is one of the most promising algorithms to find eigenvalues and eigenvectors of a given Hamiltonian on noisy intermediate-scale quantum (NISQ) devices. A particular application is to obtain ground…

Variational quantum algorithms have been a promising candidate to utilize near-term quantum devices to solve real-world problems. The powerfulness of variational quantum algorithms is ultimately determined by the expressiveness of the…

Quantum Physics · Physics 2023-05-23 Xiaokai Hou , Qingyu Li , Man-Hong Yung , Xusheng Xu , Zizhu Wang , Chu Guo , Xiaoting Wang

The accurate prediction and understanding of molecular energy and chemical reactivity are fundamental pursuits in the field of molecular quantum chemistry. With the limitations of the current noisy intermediate scale quantum computer (NISQ)…

Chemical Physics · Physics 2023-04-04 Abdul Kalam , Ashok Kumar , Prasanta K. Panigrahi

The variational quantum eigensolver (VQE) is a hybrid quantum-classical variational algorithm that produces an upper-bound estimate of the ground-state energy of a Hamiltonian. As quantum computers become more powerful and go beyond the…

Molecular quantum-dot Cellular Automata (QCA) may provide low-power, high-speed computational hardware for processing classical information. Simulation and modeling play an important role in the design of QCA circuits because fully-coherent…

Quantum Physics · Physics 2025-10-15 Nischal Binod Gautam , Enrique P. Blair

The performance of the Variational Quantum Eigensolver (VQE) is promising compared to other quantum algorithms, but also depends significantly on the appropriate design of the underlying quantum circuit. Recent research by Bowles, Ahmend \&…

We present a novel quantum algorithm for approximating the ground-state in quantum many-body systems, particularly suited for Noisy Intermediate-Scale Quantum (NISQ) devices. Our approach integrates Variational Quantum Eigensolvers (VQE)…

Quantum Physics · Physics 2024-03-18 Yihao Liu , Min-Quan He , Z. D. Wang

We study the exact ground states of the Su--Schrieffer--Heeger open chain and of the Kitaev open chain, using the Variational Quantum Eigensolver (VQE) algorithm. These models host symmetry-protected topological phases, characterized by…

Variational quantum eigensolver (VQE) is an efficient computational method promising chemical accuracy in electronic structure calculations on a universal-gate quantum computer. However, such a simple task as computing the electronic energy…

Chemical Physics · Physics 2018-06-04 Ilya G. Ryabinkin , Scott N. Genin , Artur F. Izmaylov

Current quantum computers are limited in the number of qubits and coherence time, constraining the algorithms executable with sufficient fidelity. The variational quantum eigensolver (VQE) is an algorithm to find an approximate ground state…

Quantum Physics · Physics 2023-01-24 Luca Erhart , Kosuke Mitarai , Wataru Mizukami , Keisuke Fujii

We find the ground-state energy of the Ising model using the Cascaded Variational Quantum Eigensolver (CVQE) algorithm with the Guided-Sampling Ansatz (GSA) using up to 63 qubits on a quantum computer. We study a heavy-hex lattice to match…

Quantum Physics · Physics 2026-04-29 John P. T. Stenger , C. Stephen Hellberg , Daniel Gunlycke

The variational quantum eigensolver (VQE) and its variants, which is a method for finding eigenstates and eigenenergies of a given Hamiltonian, are appealing applications of near-term quantum computers. Although the eigenenergies are…

Quantum Physics · Physics 2020-02-12 Kosuke Mitarai , Yuya O. Nakagawa , Wataru Mizukami

The still-maturing noisy intermediate-scale quantum (NISQ) technology faces strict limitations on the algorithms that can be implemented efficiently. In quantum chemistry, the variational quantum eigensolver (VQE) algorithm has become…

Quantum Physics · Physics 2021-06-29 Daniel Claudino , Bo Peng , Nicholas P. Bauman , Karol Kowalski , Travis S. Humble

The prediction of electronic structure for strongly correlated molecules represents a promising application for near-term quantum computers. Significant attention has been paid to ground state wavefunctions, but excited states of molecules…

Quantum Physics · Physics 2025-01-08 Harper R. Grimsley , Francesco A. Evangelista

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

Variational quantum eigensolver (VQE), which attracts attention as a promising application of noisy intermediate-scale quantum devices, finds a ground state of a given Hamiltonian by variationally optimizing the parameters of quantum…

Quantum Physics · Physics 2022-05-12 Fumiyoshi Kobayashi , Kosuke Mitarai , Keisuke Fujii

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