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We present quantum algorithms, for Hamiltonians of linear combinations of local unitary operators, for Hamiltonian matrix-vector products and for preconditioning with the inverse of shifted reduced Hamiltonian operator that contributes to…

Quantum Physics · Physics 2020-09-09 Zhiyong Zhang

Accurate computation of non-covalent, intermolecular interaction energies is important to understand various chemical phenomena, and quantum computers are anticipated to accelerate it. Although the state-of-the-art quantum computers are…

Quantum Physics · Physics 2025-12-29 Yuhei Tachi , Akihiko Arakawa , Taisei Osawa , Masayoshi Terabe , Kenji Sugisaki

Quantum-selected configuration interaction (QSCI) is a promising hybrid quantum-classical approach in which a quantum device generates configurations for subsequent classical diagonalization. Here, we analyze the performance of QSCI…

Although quantum computing offers a promising solution for strongly correlated system simulation, existing algorithms face significant bottlenecks on current noisy intermediate-scale quantum (NISQ) devices. Here, we introduce…

Selected configuration interaction (SCI) methods are currently enjoying a resurgence due to several recent developments which improve either the overall computational efficiency or the compactness of the resulting SCI vector. These recent…

Strongly Correlated Electrons · Physics 2020-12-18 Vibin Abraham , Nicholas J. Mayhall

We present a quantum computational framework using Hamiltonian Truncation (HT) for simulating real-time scattering processes in $(1+1)$-dimensional scalar $\phi^4$ theory. Unlike traditional lattice discretisation methods, HT approximates…

Quantum Physics · Physics 2025-05-08 James Ingoldby , Michael Spannowsky , Timur Sypchenko , Simon Williams , Matthew Wingate

In the near future, material and drug design may be aided by quantum computer assisted simulations. These have the potential to target chemical systems intractable by the most powerful classical computers. However, the resources offered by…

Although a universal quantum computer is still far from reach, the tremendous advances in controllable quantum devices, in particular with solid-state systems, make it possible to physically implement "quantum simulators". Quantum…

Quantum Physics · Physics 2016-06-08 Zhixin Wang , Xiu Gu , Lian-Ao Wu , Yu-xi Liu

Recently, a new distributed implementation of the full configuration interaction (FCI) method has been reported [Gao et al. J. Chem Theory Comput. 2024, 20, 1185]. Thanks to a hybrid parallelization scheme, the authors were able to compute…

Over the last century, a large number of physical and mathematical developments paired with rapidly advancing technology have allowed the field of quantum chemistry to advance dramatically. However, the lack of computationally efficient…

Quantum Physics · Physics 2011-03-08 James D. Whitfield , Jacob Biamonte , Alán Aspuru-Guzik

We propose a natural application of Quantum Linear Systems Problem (QLSP) solvers such as the HHL algorithm to efficiently prepare highly excited interior eigenstates of physical Hamiltonians in a variational and targeted manner. This is…

Quantum Physics · Physics 2023-10-13 Shao-Hen Chiew , Leong-Chuan Kwek

It is difficult to calculate the energy levels and eigenstates of a large physical system on a classical computer because of the exponentially growing size of the Hilbert space. In this work, we experimentally demonstrate a quantum…

Quantum Physics · Physics 2019-03-12 Zhaokai Li , Xiaomei Liu , Hefeng Wang , Sahel Ashhab , Jiangyu Cui , Hongwei Chen , Xinhua Peng , Jiangfeng Du

As the search continues for useful applications of noisy intermediate scale quantum devices, variational simulations of fermionic systems remain one of the most promising directions. Here, we perform a series of quantum simulations of…

We propose a computational protocol for quantum simulations of Fermionic Hamiltonians on a quantum computer, enabling calculations which were previously not feasible with conventional encoding and ansatses of variational quantum…

Quantum Physics · Physics 2023-03-15 Benchen Huang , Nan Sheng , Marco Govoni , Giulia Galli

The performance of quantum algorithms for eigenvalue problems, such as computing Hamiltonian spectra, depends strongly on the overlap of the initial wavefunction and the target eigenvector. In a basis of Slater determinants, the…

Quantum Physics · Physics 2025-03-03 Daniel Marti-Dafcik , Hugh G. A. Burton , David P. Tew

We present a quantum algorithm for simulating rovibrational Hamiltonians on fault-tolerant quantum computers. The method integrates exact curvilinear kinetic energy operators and general-form potential energy surfaces expressed in a hybrid…

Quantum Physics · Physics 2026-04-07 Michał Szczepanik , Ákos Nagy , Emil Żak

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

Quantum computing is increasingly offering concrete solutions toward the simulation of nuclear structure, with the potential to overcome the exponential scaling that limits classical diagonalization methods in large spaces. A particularly…

Nuclear Theory · Physics 2026-04-14 Emanuele Costa , Javier Menendez

Quantum computing is being extensively used in quantum chemistry, especially in simulating simple molecules and evaluating properties like the ground state energy, dipole moment, etc. The transformation of a molecular Hamiltonian from the…

Quantum Physics · Physics 2024-01-18 Harshdeep Singh , Sabyashachi Mishra , Sonjoy Majumder

Computationally efficient and accurate quantum mechanical approximations to solve the many-electron Schr\"odinger equation are at the heart of computational materials science. In that respect the coupled cluster hierarchy of methods plays a…

Materials Science · Physics 2022-01-11 Tina N. Mihm , Tobias Schäfer , Sai Kumar Ramadugu , Andreas Grüneis , James J. Shepherd