English
Related papers

Related papers: Constructing Compact ADAPT Unitary Coupled-Cluster…

200 papers

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

A family of Variational Quantum Eigensolver (VQE) methods is designed to maximize the resource of existing noisy intermediate-scale quantum (NISQ) devices. However, VQE approaches encounter various difficulties in simulating molecules of…

Chemical Physics · Physics 2022-08-16 M. D. Sapova , A. K. Fedorov

We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Tailored Ansatz Variational Quantum Eigensolver (ADAPT-VQE) framework for efficient quantum simulations of chemical systems on near-term…

Variational quantum eigensolver (VQE), aiming at determining the ground state energy of a quantum system described by a Hamiltonian on noisy intermediate scale quantum (NISQ) devices, is among the most significant applications of…

Quantum Physics · Physics 2024-04-12 Ze-Tong Li , Fan-Xu Meng , Han Zeng , Zai-Chen Zhang , Xu-Tao Yu

Approximating the ground states of strongly interacting electron systems in quantum chemistry and condensed matter physics is expected to be one of the earliest applications of quantum computers. In this paper, we prepare highly accurate…

Quantum Physics · Physics 2022-11-09 Gaurav Gyawali , Michael J. Lawler

Ab initio electronic excited state calculations are necessary for the quantitative study of photochemical reactions, but their accurate computation on classical computers is plagued by prohibitive scaling. The Variational Quantum Deflation…

Quantum chemistry is one of the most promising applications of quantum computers in the near future. For noisy intermediate-scale quantum devices, the quantum-classical hybrid framework based on the variational quantum eigensolver (VQE) has…

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

Calculations of molecular spectral properties, like photodissociation rates and absorption bands, rely on knowledge of the excited state energies of the molecule of interest. Protocols based on the variational quantum eigensolver (VQE) are…

Quantum Physics · Physics 2021-10-19 Yordan S. Yordanov , Crispin H. W. Barnes , David R. M. Arvidsson-Shukur

Reducing circuit depth is essential for implementing quantum simulations of electronic structure on near-term quantum devices. In this work, we propose a variational quantum eigensolver (VQE) based perturbation theory algorithm to…

Quantum Physics · Physics 2024-01-17 Jie Liu , Zhenyu Li , Jinlong Yang

Among variational quantum algorithms designed for NISQ devices, ADAPT-VQE stands out for its robustness against barren plateaus, particularly in estimating molecular ground states. On the other hand, counterdiabatic algorithms have shown…

Quantum Physics · Physics 2026-01-12 Diego Tancara , Herbert Díaz-Moraga , Dardo Goyeneche

Current noisy intermediate-scale quantum (NISQ) devices remain limited in their ability to perform accurate quantum chemistry simulations due to restricted numbers of high-fidelity qubits and short coherence times. To overcome these…

Quantum Physics · Physics 2026-04-23 Yuhan Zheng , Yibin Guo , Huili Zhang , Jie Liu , Xiongzhi Zeng , Xiaoxia Cai , Zhenyu Li , Jinlong Yang

The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues and eigenvalues of a Hamiltonian. VQE has been proposed as an alternative to fully quantum algorithms such…

Quantum Physics · Physics 2021-09-01 Dmitry A. Fedorov , Bo Peng , Niranjan Govind , Yuri Alexeev

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

Quantum chemistry has been identified as one of the most promising areas where quantum computing can have a tremendous impact. For current Noisy Intermediate-Scale Quantum (NISQ) devices, one of the best available methods to prepare…

Quantum Physics · Physics 2024-04-05 Marco Majland , Patrick Ettenhuber , Nikolaj Thomas Zinner

Finding the ground-state energy of molecules is an important and challenging computational problem for which quantum computing can potentially find efficient solutions. The variational quantum eigensolver (VQE) is a quantum algorithm that…

Quantum Physics · Physics 2023-02-15 Daniel Yoffe , Amir Natan , Adi Makmal

Computational chemistry is one of the most promising applications of quantum computing, mostly thanks to the development of the Variational Quantum Eigensolver (VQE) algorithm. VQE is being studied extensively and numerous optimisations of…

The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground state energy of a Hamiltonian, a problem that is central to quantum chemistry and condensed matter physics. Conventional computing methods are…

The variational quantum eigensolver (VQE) is an algorithm to compute ground and excited state energy of quantum many-body systems. A key component of the algorithm and an active research area is the construction of a parametrized trial…

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