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Quantum embedding methods enable the study of large, strongly correlated quantum systems by (usually self-consistent) decomposition into computationally manageable subproblems, in the spirit of divide-and-conquer methods. Among these,…

Strongly Correlated Electrons · Physics 2025-03-14 Alicia Negre , Fabian Faulstich , Raehyun Kim , Thomas Ayral , Lin Lin , Eric Cancès

Quantum chemistry is one of the most promising applications of quantum computers in the near future. For noisy intermediate-scale quantum devices, the quantum-classical hybrid framework based on the variational quantum eigensolver (VQE) has…

Accurately and efficiently predicting the equilibrium geometries of large molecules remains a central challenge in quantum computational chemistry, even with hybrid quantum-classical algorithms. Two major obstacles hinder progress: the…

Quantum Physics · Physics 2026-04-07 Yajie Hao , Qiming Ding , Xiaoting Wang , Xiao Yuan

Quantum computers (QC) could harbor the potential to significantly advance materials simulations, particularly at the atomistic scale involving strongly correlated fermionic systems where an accurate description of quantum many-body effects…

Density-functional theory (DFT) has revolutionized computer simulations in chemistry and material science. A faithful implementation of the theory requires self-consistent calculations. However, this effort involves repeatedly diagonalizing…

Quantum Physics · Physics 2023-07-17 Taehee Ko , Xiantao Li , Chunhao Wang

Using GPU-accelerated state-vector emulation, we propose to embed a quantum computing ansatz into density-functional theory via density-based basis-set corrections (DBBSC) to obtain quantitative quantum-chemistry results on molecules that…

Quantum computers hold promise to enable efficient simulations of the properties of molecules and materials; however, at present they only permit ab initio calculations of a few atoms, due to a limited number of qubits. In order to harness…

Materials Science · Physics 2020-07-07 He Ma , Marco Govoni , Giulia Galli

The Variational Quantum Eigensolver (VQE) algorithm, as applied to finding the ground state of a Hamiltonian, is particularly well-suited for deployment on noisy intermediate-scale quantum (NISQ) devices. Here we utilize the VQE algorithm…

Quantum Physics · Physics 2024-11-28 Younes Javanmard , Ugne Liaubaite , Tobias J. Osborne , Xusheng Xu , Man-Hong Yung

We develop and implement a novel pulse-based ansatz, which we call PANSATZ, for more efficient and accurate implementations of variational quantum algorithms (VQAs) on today's noisy intermediate-scale quantum (NISQ) computers. Our approach…

Quantum Physics · Physics 2024-01-03 Dekel Meirom , Steven H. Frankel

Variational quantum eigensolvers (VQEs) are a promising class of quantum algorithms for preparing approximate ground states in near-term quantum devices. Minimizing the error in such an approximation requires designing ansatzes using…

Quantum Physics · Physics 2021-12-06 Y. Herasymenko , T. E. O'Brien

The rapid progress of noisy intermediate-scale quantum (NISQ) computing underscores the need to test and evaluate new devices and applications. Quantum chemistry is a key application area for these devices, and therefore serves as an…

Adaptive variational quantum simulation algorithms use information from the quantum computer to dynamically create optimal trial wavefunctions for a given problem Hamiltonian. A key ingredient in these algorithms is a predefined operator…

Programmable quantum simulators such as superconducting quantum processors and ultracold atomic lattices represent rapidly developing emergent technology that may one day qualitatively outperform existing classical computers. Yet, apart…

Strongly Correlated Electrons · Physics 2025-03-11 Jin Ming Koh , Tommy Tai , Ching Hua Lee

We extend our density matrix embedding theory (DMET) [Phys. Rev. Lett. 109 186404 (2012)] from lattice models to the full chemical Hamiltonian. DMET allows the many-body embedding of arbitrary fragments of a quantum system, even when such…

Strongly Correlated Electrons · Physics 2013-11-15 Gerald Knizia , Garnet Kin-Lic Chan

Variational quantum algorithms are promising applications of noisy intermediate-scale quantum (NISQ) computers. These algorithms consist of a number of separate prepare-and-measure experiments that estimate terms in a Hamiltonian. The…

Quantum Physics · Physics 2020-06-25 Andrew Zhao , Andrew Tranter , William M. Kirby , Shu Fay Ung , Akimasa Miyake , Peter Love

Any quantum program on a realistic quantum device must be compiled into an executable form while taking into account the underlying hardware constraints. Stringent restrictions on architecture and control imposed by physical platforms make…

Quantum Physics · Physics 2023-10-16 Run-Hong He , Xu-Sheng Xu , Mark S. Byrd , Zhao-Ming 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

A quantitative description of the excited electronic states of point defects and impurities is crucial for understanding materials properties, and possible applications of defects in quantum technologies. This is a considerable challenge…

We present an exact ansatz for the eigenstate problem of mixed fermion-boson systems that can be implemented on quantum devices. Based on a generalization of the electronic contracted Schr\"odinger equation (CSE), our approach guides a…

Quantum Physics · Physics 2024-08-26 Samuel Warren , Yuchen Wang , Carlos L. Benavides-Riveros , David A. Mazziotti

The quantum circuit synthesis problem bridges quantum algorithm design and quantum hardware implementation in the Noisy Intermediate-Scale Quantum (NISQ) era. In quantum circuit synthesis problems, diagonal unitary synthesis plays a crucial…

Quantum Physics · Physics 2024-12-04 Wenqi Zhang , Jinyang Liu , Zixiang Zhou , Shuai Yang