English
Related papers

Related papers: Variational Quantum Algorithm for Non-equilibrium …

200 papers

Dynamical Mean Field Theory (DMFT) is one of the powerful computational approaches to study electron correlation effects in solid-state materials and molecules. Its practical applicability is, however, limited by the quantity of numerical…

Strongly Correlated Electrons · Physics 2024-12-23 Jannis Ehrlich , Daniel Urban , Christian Elsässer

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

Variational quantum eigensolvers (VQEs) combine classical optimization with efficient cost function evaluations on quantum computers. We propose a new approach to VQEs using the principles of measurement-based quantum computation. This…

We explore a non-variational quantum state preparation approach combined with the ADAPT operator selection strategy in the application of preparing the ground state of a desired target Hamiltonian. In this algorithm, energy gradient…

The variational quantum eigensolver (VQE) algorithm combines the ability of quantum computers to efficiently compute expectation values with a classical optimization routine in order to approximate ground state energies of quantum systems.…

Variational hybrid quantum-classical algorithms are promising candidates for near-term implementation on quantum computers. In these algorithms, a quantum computer evaluates the cost of a gate sequence (with speedup over classical cost…

Quantum Physics · Physics 2019-06-27 Ryan LaRose , Arkin Tikku , Étude O'Neel-Judy , Lukasz Cincio , Patrick J. Coles

Modern Cloud/Edge architectures need to orchestrate multiple layers of heterogeneous computing nodes, including pervasive sensors/actuators, distributed Edge/Fog nodes, centralized data centers and quantum devices. The optimal assignment…

Quantum Physics · Physics 2024-05-27 Carlo Mastroianni , Francesco Plastina , Jacopo Settino , Andrea Vinci

Variational quantum eigensolver (VQE) is an appealing candidate for the application of near-term quantum computers. A technique introduced in [Higgot et al., Quantum 3, 156 (2019)], which is named variational quantum deflation (VQD), has…

We present and benchmark a type of variational quantum eigensolver (VQE), which we denote $\sigma$-VQE. It is designed to target mid-spectrum eigenstates and prepare quantum many-body scar states. The approach leverages the fact that noisy…

Quantum Physics · Physics 2026-05-18 Eoin Carolan , Nathan Keenan , Gabriele Cenedese , Giuliano Benenti

The quantum-classical hybrid Variational Quantum Eigensolver (VQE) algorithm is recognized to be the most suitable approach to obtain ground state energies of quantum many-body systems in the noisy intermediate scale quantum era. In this…

Atomic Physics · Physics 2023-04-12 K. R. Swain , V. S. Prasannaa , Kenji Sugisaki , B. P. Das

Mapping out phase diagrams of quantum systems using classical simulations can be challenging or intractable due to the computational resources required to simulate even small quantum systems far away from the thermodynamic limit. We…

Quantum Physics · Physics 2024-06-05 Jan Lukas Bosse , Raul Santos , Ashley Montanaro

Variational quantum eigensolver (VQE) is a promising algorithm suitable for near-term quantum machines. VQE aims to approximate the lowest eigenvalue of an exponentially sized matrix in polynomial time. It minimizes quantum resource…

Variational quantum eigensolver (VQE) is an efficient classical-quantum hybrid method to take advantage of quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era. In this work we test the performance of VQE by studying the…

Strongly Correlated Electrons · Physics 2023-06-09 Yuheng Guo , Mingpu Qin

We propose a momentum-space based variational quantum eigensolver (VQE) framework for simulating quasiparticle excitations in interacting quantum many-body systems on near-term quantum devices. Leveraging translational invariance and other…

Strongly Correlated Electrons · Physics 2025-11-24 Saavanth Velury , Yuxuan Wang

Quantum simulation of non-Markovian open quantum dynamics is essential but challenging for standard quantum computers due to their non-Hermitian nature, leading to non-unitary evolution, and the limitations of available quantum resources.…

Quantum Physics · Physics 2026-01-12 Yukai Guo , Xing Gao

Variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for noisy intermediate-scale quantum (NISQ) computers. It is promising for quantum chemical calculations (QCC) because it can calculate the ground-state…

The cascaded variational quantum eigensolver (CVQE) circumvents the need for iterative communication between the quantum and classical processing units that is necessary in the conventional VQE algorithm. While CVQE offers complete freedom…

Quantum Physics · Physics 2026-05-04 Yi-Hua Lai , John P. T. Stenger , Gloria Bazargan , Igor V. Schweigert , Daniel Gunlycke

We present a novel generic framework to approximate the non-equilibrium steady states of dissipative quantum many-body systems. It is based on the variational minimization of a suitable norm of the quantum master equation describing the…

Quantum Physics · Physics 2015-02-20 Hendrik Weimer

The Variational Quantum Eigensolver (VQE) is widely regarded as a promising algorithm for calculating ground states of quantum systems that are intractable for classical computers. This promise is typically motivated by the hope of…

Quantum Physics · Physics 2026-04-13 Manuel Hagelueken , David A. Kreplin , Florian Wieland , Marco F. Huber , Marco Roth

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