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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

Harnessing the full power of nascent quantum processors requires the efficient management of a limited number of quantum bits with finite lifetime. Hybrid algorithms leveraging classical resources have demonstrated promising initial results…

Variational quantum eigensolver (VQE) for electronic structure calculations is believed to be one major potential application of near term quantum computing. Among all proposed VQE algorithms, the unitary coupled cluster singles and doubles…

Quantum Physics · Physics 2020-10-13 Rongxin Xia , Sabre Kais

The Adaptive Variational Quantum Eigensolver (ADAPT-VQE) is a promising approach for quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era, offering advantages over traditional VQE methods by reducing circuit depth and…

We perform a series of calculations using simulated QPUs, accelerated by the NVIDIA CUDA-Q platform, focusing on a molecular analog of an amine-functionalized metal-organic framework (MOF), a promising class of materials for CO$_2$ capture.…

Chemical Physics · Physics 2025-12-16 Jonathan R. Owens , Marwa H. Farag , Pooja Rao , Annarita Giani

In this work, we design a variational quantum eigensolver (VQE) suitable for investigating ground states and static string breaking in a $\mathbb{Z}_2$ lattice gauge theory (LGT). We consider a two-leg ladder lattice coupled to…

Quantum Physics · Physics 2025-11-12 Fariha Azad , Matteo Inajetovic , Stefan Kühn , Anna Pappa

We propose a scheme to restore spatial symmetry of Hamiltonian in the variational-quantum-eigensolver (VQE) algorithm for which the quantum circuit structures used usually break the Hamiltonian symmetry. The symmetry-adapted VQE scheme…

Quantum Physics · Physics 2020-06-01 Kazuhiro Seki , Tomonori Shirakawa , Seiji Yunoki

As the advances in quantum hardware bring us into the noisy intermediate-scale quantum (NISQ) era, one possible task we can perform without quantum error correction using NISQ machines is the variational quantum eigensolver (VQE) due to its…

Quantum Physics · Physics 2020-07-29 Zhenyu Cai

The characterization of quantum phase transitions is a fundamental task for the understanding of quantum phases of matter, with a number of potential applications in quantum technologies. In this work, we use digital quantum simulation as a…

Quantum Physics · Physics 2025-09-04 Alan Duriez , Andreia Saguia , Marcelo S. Sarandy

Variational Quantum Eigensolver (VQE) method is the way to derive the wave functions from their eigen energies using quantum computers. But, the methods to utilize the superposition states between them haven't been developed yet. The method…

Quantum Physics · Physics 2021-07-13 Hikaru Wakaura , Takao Tomono

The emerging field of quantum simulation of many-body systems is widely recognized as a very important application of quantum computing. A crucial step towards its realization in the context of many-electron systems requires a rigorous…

Atomic Physics · Physics 2021-08-23 Sumeet , V. S. Prasannaa , B. P. Das , B. K. Sahoo

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

Variational quantum eigensolver (VQE) optimizes parameterized eigenstates of a Hamiltonian on a quantum processor by updating parameters with a classical computer. Such a hybrid quantum-classical optimization serves as a practical way to…

Quantum Physics · Physics 2020-03-18 Dan-Bo Zhang , Tao Yin

Adaptive variational quantum eigensolvers (ADAPT-VQEs) are promising candidates for simulations of strongly correlated systems on near-term quantum hardware. To further improve the noise resilience of these algorithms, recent efforts have…

Quantum-enhanced computing methods are promising candidates to solve currently intractable problems. We consider here a variational quantum eigensolver (VQE), that delegates costly state preparations and measurements to quantum hardware,…

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

We use the Variational Quantum Eigensolver (VQE) as implemented in the Qiskit software package to compute the ground state energy of small molecules derived from water, H$_2$O, and hydrogen cyanide, HCN. The work aims to benchmark…

Quantum Physics · Physics 2021-07-07 P. Lolur , M. Rahm , M. Skogh , L. García-Álvarez , G. Wendin

Quantum chemical calculations have attracted much attention as a practical application of quantum computing. Quantum computers can prepare superpositions of electronic states with various numbers of electrons on qubits. This special feature…

Chemical Physics · Physics 2024-03-29 Soichi Shirai , Takahiro Horiba , Hirotoshi Hirai

Within the evolving domain of quantum computational chemistry, the Variational Quantum Eigensolver (VQE) has been developed to explore not only the ground state but also the excited states of molecules. In this study, we compare the…

Quantum Physics · Physics 2024-06-18 I-Chi Chen , Nouhaila Innan , Suman Kumar Roy , Jason Saroni

Simulation of chemical reactions on quantum computing platforms using quantum classical hybrid algorithms such as the Variational Quantum Eigensolver (VQE) is challenged by the need for a reaction consistent treatment of electron…

Chemical Physics · Physics 2026-03-16 L. Roy , M. Sarkar , M. Tewari , A. Kumar , M. Paranjothy
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