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The opportunities afforded by near-term quantum computers to calculate the ground-state properties of small molecules depend on the structure of the computational ansatz as well as the errors induced by device noise. Here we investigate the…

Quantum-classical hybrid algorithms are emerging as promising candidates for near-term practical applications of quantum information processors in a wide variety of fields ranging from chemistry to physics and materials science. We report…

We investigate the possibility to calculate the ground-state energy of the atomic systems on a quantum computer. For this purpose we evaluate the lowest binding energy of the moscovium atom with the use of the iterative phase estimation and…

Quantum Physics · Physics 2023-09-26 V. A. Zaytsev , M. E. Groshev , I. A. Maltsev , A. V. Durova , V. M. Shabaev

As quantum computing approaches its first commercial implementations, quantum simulation emerges as a potentially ground-breaking technology for several domains, including Biology and Chemistry. However, taking advantage of quantum…

Quantum phase estimation is the workhorse behind any quantum algorithm and a promising method for determining ground state energies of strongly correlated quantum systems. Low-cost quantum phase estimation techniques make use of circuits…

Quantum Physics · Physics 2019-03-27 T. E. O'Brien , B. Tarasinski , B. M. Terhal

Variational algorithms are promising candidates to be implemented on near-term quantum computers. The variational quantum eigensolver (VQE) is a prominent example, where a parametrized trial state of the quantum mechanical wave function is…

We present a quantum information-inspired ansatz for the variational quantum eigensolver (VQE) and demonstrate its efficacy in calculating ground-state energies of atomic systems. Instead of adopting a heuristic approach, we start with an…

Quantum Physics · Physics 2025-08-19 Abdul Kalam , Prasenjit Deb , Akitada Sakurai , B. K. Sahoo , V. S. Prasannaa , B. P. Das

Hardware-efficient empirical variational ans\"atze for Variational Quantum Eigensolver simulations of Quantum Chemistry suffer from the lack of a direct connection to classical Quantum Chemistry methods. In the present work, we propose a…

Quantum Physics · Physics 2025-09-03 Davide Materia , Leonardo Ratini , Celestino Angeli , Leonardo Guidoni

Variational quantum algorithms are emerging as promising candidates for near-term practical applications of quantum information processors, in the field of quantum chemistry. We implement the variational quantum eigensolver algorithm to…

Quantum Physics · Physics 2024-07-02 Dileep Singh , Shashank Mehendale , Arvind , Kavita Dorai

We present and analyze large-scale simulation results of a hybrid quantum-classical variational method to calculate the ground state energy of the anti-ferromagnetic Heisenberg model. Using a massively parallel universal quantum computer…

Quantum Physics · Physics 2022-06-20 Manpreet Singh Jattana , Fengping Jin , Hans De Raedt , Kristel Michielsen

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.…

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

Imaginary time evolution is a powerful tool for studying quantum systems. While it is possible to simulate with a classical computer, the time and memory requirements generally scale exponentially with the system size. Conversely, quantum…

Quantum Physics · Physics 2019-09-17 Sam McArdle , Tyson Jones , Suguru Endo , Ying Li , Simon Benjamin , Xiao Yuan

Electronic ground states are of central importance in chemical simulations, but have remained beyond the reach of efficient classical algorithms except in cases of weak electron correlation or one-dimensional spatial geometry. We introduce…

Quantum Physics · Physics 2026-02-06 Oskar Leimkuhler , K. Birgitta Whaley

Variational algorithms for strongly correlated chemical and materials systems are one of the most promising applications of near-term quantum computers. We present an extension to the variational quantum eigensolver that approximates the…

Quantum Physics · Physics 2020-08-26 William J. Huggins , Joonho Lee , Unpil Baek , Bryan O'Gorman , K. Birgitta Whaley

Quantum simulators offer the potential to utilize the quantum nature of a physical system to study another physical system. In contrast to conventional simulation, which experiences an exponential increase in computational complexity,…

Quantum Physics · Physics 2024-07-24 Xuliang Du , Yang Shen , Zipeng Wu , Bei Zeng , Sen Yang

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…

Quantum computing presents a promising path toward precise quantum chemical simulations, particularly for systems that challenge classical methods. This work investigates the performance of the Variational Quantum Eigensolver (VQE) in…

Quantum Physics · Physics 2025-10-28 Zakaria Boutakka , Nouhaila Innan , Muhammed Shafique , Mohamed Bennai , Z. Sakhi

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…

Quantum simulation of chemical systems is one of the most promising near-term applications of quantum computers. The variational quantum eigensolver, a leading algorithm for molecular simulations on quantum hardware, has a serious…

Quantum Physics · Physics 2019-07-16 Harper R. Grimsley , Sophia E. Economou , Edwin Barnes , Nicholas J. Mayhall
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