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The variational quantum eigensolver (VQE) is an attracting possible application of near-term quantum computers. Originally, the aim of the VQE is to find a ground state for a given specific Hamiltonian. It is achieved by minimizing the…

Quantum Physics · Physics 2019-04-30 Kosuke Mitarai , Tennin Yan , Keisuke Fujii

In order to answer the problem of Quantum Phase Estimation Algorithm been not suitable for NISQ devices, and allows one to outperform classical computers, Variational Quantum Algorithms (VQAs) were designed. Our subject of interest is the…

Quantum Physics · Physics 2022-10-28 Max Alteg , Baptiste Chevalier , Octave Mestoudjian , Johan-Luca Rossi

The contextual subspace variational quantum eigensolver (CS-VQE) is a hybrid quantum-classical algorithm that approximates the ground-state energy of a given qubit Hamiltonian. It achieves this by separating the Hamiltonian into contextual…

Quantum Physics · Physics 2023-02-14 Alexis Ralli , Tim Weaving , Andrew Tranter , William M. Kirby , Peter J. Love , Peter V. Coveney

Quantum computers have an exponential speed-up advantage over classical computers. One of the most prominent utilities of quantum computers is their ability to study complex quantum systems in various fields using quantum computational…

Quantum Physics · Physics 2022-01-03 Ritu Dhaulakhandi , Bikash K. Behera

We present a novel method for improving the quantum simulation of the ground state energy of molecules. We perform a pre-processing step classically, which reduces the dimensionality of the problem by generating a custom mapping which…

Quantum Physics · Physics 2023-03-03 Kaur Kristjuhan , Mark Nicholas Jones

We introduce the Qumode Subspace Variational Quantum Eigensolver (QSS-VQE), a hybrid quantum-classical algorithm for computing molecular excited states using the Fock basis of bosonic qumodes in circuit quantum electrodynamics (cQED)…

In this work we integrate the variational quantum eigensolver (VQE) with the adiabatic connection (AC) method for efficient simulations of chemical problems on near-term quantum computers. Orbital optimized VQE methods are employed to…

Quantum Physics · Physics 2023-10-10 Mikuláš Matoušek , Katarzyna Pernal , Fabijan Pavošević , Libor Veis

Recent advances in quantum computing devices have brought attention to hybrid quantum-classical algorithms like the Variational Quantum Eigensolver (VQE) as a potential route to practical quantum advantage in chemistry. However, it is not…

The variational quantum eigensolver is one of the most promising approaches for performing chemistry simulations using noisy intermediate-scale quantum (NISQ) processors. The efficiency of this algorithm depends crucially on the ability to…

Variational Quantum optimization algorithms, such as the Variational Quantum Eigensolver (VQE) or the Quantum Approximate Optimization Algorithm (QAOA), are among the most studied quantum algorithms. In our work, we evaluate and improve an…

Quantum Physics · Physics 2022-10-24 David Winderl , Nicola Franco , Jeanette Miriam Lorenz

The Variational Quantum Eigensolver (VQE) is a method of choice to solve the electronic structure problem for molecules on near-term gate-based quantum computers. However, the circuit depth is expected to grow significantly with problem…

The Variational Quantum Eigensolver (VQE) is a leading hybrid quantum-classical algorithm for simulating many-body systems in the NISQ era. Its effectiveness, however, depends on the faithful preparation of eigenstates, which becomes…

Quantum Physics · Physics 2026-02-20 Ashutosh P. Tripathi , Nilmani Mathur , Vikram Tripathi

We present calculations of the ground and excited state energies of spin defects in solids carried out on a quantum computer, using a hybrid classical/quantum protocol. We focus on the negatively charged nitrogen vacancy center in diamond…

Quantum Physics · Physics 2025-07-01 Benchen Huang , Marco Govoni , Giulia Galli

Quantum chemistry and materials is one of the most promising applications of quantum computing. Yet much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them. Most…

Quantum computing technology has the potential to revolutionize the simulation of materials and molecules in the near future. A primary challenge in achieving near-term quantum advantage is effectively mitigating the noise effects inherent…

Quantum Physics · Physics 2024-01-17 Tao Jiang , John Rogers , Marius S. Frank , Ove Christiansen , Yong-Xin Yao , Nicola Lanatà

The Variational Quantum Eigensolver (VQE), as a hybrid quantum-classical algorithm, is an important tool for effective quantum computing in the current noisy intermediate-scale quantum (NISQ) era. However, the traditional hardware-efficient…

Strongly Correlated Electrons · Physics 2025-11-20 Shaohui Yao , Wenyu Wang

Quantum chemistry is a promising application for noisy intermediate-scale quantum (NISQ) devices. However, quantum computers have thus far not succeeded in providing solutions to problems of real scientific significance, with algorithmic…

Quantum Physics · Physics 2023-03-02 Tim Weaving , Alexis Ralli , William M. Kirby , Andrew Tranter , Peter J. Love , Peter V. Coveney

The Adaptive Derivative-Assembled Pseudo-Trotter Variational Quantum Eigensolver (ADAPT-VQE) has emerged as a pivotal promising approach for electronic structure challenges in quantum chemistry with noisy quantum devices. Nevertheless, to…

The development of Fault-Tolerant Quantum Computer (FTQC) gradually raises a possibility to implement the Quantum Phase Estimation (QPE) algorithm. However, QPE works only for normalized systems. This requires the minimum and maximum of…

Quantum Physics · Physics 2021-11-19 Hikaru Wakaura , Andriyan B. Suksmono

Non-Hermitian operators naturally arise in the description of open quantum systems, which exhibit features such as resonances and decay processes, where the associated eigenvalues are complex. Standard quantum algorithms, including the…

Quantum Physics · Physics 2026-04-01 Durgesh Pandey , Ankit Kumar Das , P. Arumugam