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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…

The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for current and near-term quantum devices. Despite its initial success, there is a lack of understanding involving several of its key aspects. There…

Quantum Physics · Physics 2023-03-22 Manpreet Singh Jattana , Fengping Jin , Hans De Raedt , Kristel Michielsen

Quantum computing offers a potential for algorithmic speedups for applications, such as large-scale simulations in chemistry and physics. However, these speedups must yield results that are sufficiently accurate to predict realistic…

Quantum Physics · Physics 2025-01-15 Meenambika Gowrishankar , Daniel Claudino , Jerimiah Wright , Travis Humble

Quantum variational algorithms (QVAs) are increasingly potent tools for simulating quantum many-body systems on noisy intermediate-scale quantum (NISQ) devices. This work examines the application of the Variational Quantum Eigensolver (VQE)…

Nuclear Theory · Physics 2026-01-28 Dhritimalya Roy , Somnath Nag

We propose a modification of the Variational Quantum Eigensolver algorithm for electronic structure optimization using quantum computers, named non-unitary Variational Quantum Eigensolver (nu-VQE), in which a non-unitary operator is…

In the lead up to fault tolerance, the utility of quantum computing will be determined by how adequately the effects of noise can be circumvented in quantum algorithms. Hybrid quantum-classical algorithms such as the variational quantum…

While numerical simulations are presented in most papers introducing new methods to enhance the VQE performance, comprehensive, comparative, and applied studies remain relatively rare. We present a comprehensive, yet concise guide for the…

As a crossover frontier of physics and mechanics, quantum computing is showing its great potential in computational mechanics. However, quantum hardware noise remains a critical barrier to achieving accurate simulation results due to the…

Computational Engineering, Finance, and Science · Computer Science 2024-11-25 Zengtao Kuang , Yongchun Xu , Qun Huang , Jie Yang , Chafik El Kihal , Heng Hu

Quantum computers in the NISQ era are prone to noise. A range of quantum error mitigation techniques has been proposed to address this issue. Zero-noise extrapolation (ZNE) stands out as a promising one. ZNE involves increasing the noise…

Quantum Physics · Physics 2024-02-01 Hrushikesh Patil , Peiyi Li , Ji Liu , Huiyang Zhou

Some of the computational limitations in solving the nuclear many-body problem could be overcome by utilizing quantum computers. The nuclear shell-model calculations providing deeper insights into the properties of atomic nuclei, is one…

Quantum Physics · Physics 2026-05-25 Nifeeya Singh , Pooja Siwach , P. Arumugam

We compare the performance of different methodologies for finding the ground state of the molecule BeH2. We implement adaptive, tetris-adaptive variational quantum eigensolver (VQE), and entanglement forging to reduce computational resource…

Quantum Physics · Physics 2024-07-24 Tushar Pandey , Jason Saroni , Abdullah Kazi , Kartik Sharma

We present a new optimization method for small-to-intermediate scale variational algorithms on noisy near-term quantum processors which uses a Gaussian process surrogate model equipped with a classically-evaluated quantum kernel.…

Quantum Physics · Physics 2023-08-16 Alistair W. R. Smith , A. J. Paige , M. S. Kim

The rapid development of noisy intermediate-scale quantum (NISQ) devices has raised the question of whether or not these devices will find commercial use. Unfortunately, a major shortcoming of many proposed NISQ-amenable algorithms, such as…

Quantum Physics · Physics 2021-10-22 Amara Katabarwa , Alex Kunitsa , Borja Peropadre , Peter Johnson

The variational quantum eigensolver (VQE) is generally regarded as a promising quantum algorithm for near-term noisy quantum computers. However, when implemented with the deep circuits that are in principle required for achieving a…

Quantum Physics · Physics 2025-06-05 Simone Cantori , Andrea Mari , David Vitali , Sebastiano Pilati

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

Quantum computers have the potential to deliver speed-ups for solving certain important problems that are intractable for classical counterparts, making them a promising avenue for advancing modern computation. However, many quantum…

Quantum Physics · Physics 2025-12-23 Kang-Min Hu , Min Namkung , Hyang-Tag Lim

Near term quantum processors operate in a noise dominated regime, motivating error mitigation techniques that recover accurate expectation values without full fault tolerance. Zero Noise Extrapolation (ZNE) is a widely used but biased error…

Quantum Physics · Physics 2026-03-17 Debarthi Pal , Yogesh Simmhan

Quantum computing brings a promise of new approaches into computational quantum chemistry. While universal, fault-tolerant quantum computers are still not available, we want to utilize today's noisy quantum processors. One of their flagship…

Variational quantum algorithms are promising candidates for delivering practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware. However, optimizing the noisy cost functions associated with these algorithms is…

Quantum Physics · Physics 2024-03-06 Andy C. Y. Li , Imanol Hernandez

The number of measurements demanded by hybrid quantum-classical algorithms such as the variational quantum eigensolver (VQE) is prohibitively high for many problems of practical value. For such problems, realizing quantum advantage will…

Quantum Physics · Physics 2021-03-24 Guoming Wang , Dax Enshan Koh , Peter D. Johnson , Yudong Cao