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Quantum Computing for Materials Simulation

Edited by Eric Bylaska (Pacific Northwestern National Laboratory, USA) and Karol Kowalski (Pacific Northwestern National Laboratory, USA)

With the advent of real quantum computer (QC) simulations with demonstrated quantum supremacy, it is expected that many materials systems, beyond the hydrogen molecule, will become possible for the first time. Although QC can reshape the landscape of molecular/materials simulations, many issues of QC related to the decoherence time or
quantum gate depth of existing algorithms preclude purely quantum algorithms from being applied to realistic size systems. To overcome these limitations new approaches, which combine the maturity of conventional computing with the power of quantum hardware (hybrid-QC), are being developed. This collection in Materials Theory covers manuscripts that focus on the development and use of QC and hybrid-QC algorithms for materials modeling. Topics of interest include, but are not limited to, techniques such as down-folding to reduce the complexity of many-body Hamiltonians, domain decomposition/embedding methods, the use of ML to improve the use of NISQ (Noisy Intermediate-Scale Quantum) devices, and application benchmarks and analysis of algorithms for QC. We are also interested manuscripts  focused on long term challenges, especially related to new methods for simulating ground and excited state Hamiltonians for materials systems on hybrid quantum/classical hardware.

Articles will undergo all of the journal's standard peer review and editorial processes outlined in its submission guidelines

  1. A procedure for defining virtual spaces, and the periodic one-electron and two-electron integrals, for plane-wave second quantized Hamiltonians has been developed, and it was validated using full configuration...

    Authors: Duo Song, Nicholas P. Bauman, Guen Prawiroatmodjo, Bo Peng, Cassandra Granade, Kevin M. Rosso, Guang Hao Low, Martin Roetteler, Karol Kowalski and Eric J. Bylaska
    Citation: Materials Theory 2023 7:2
  2. 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...

    Authors: Jerimiah Wright, Meenambika Gowrishankar, Daniel Claudino, Phillip C. Lotshaw, Thien Nguyen, Alexander J. McCaskey and Travis S. Humble
    Citation: Materials Theory 2022 6:18
  3. The recently introduced coupled cluster (CC) downfolding techniques for reducing the dimensionality of quantum many-body problems recast the CC formalism in the form of the renormalization procedure allowing, ...

    Authors: Nicholas P. Bauman and Karol Kowalski
    Citation: Materials Theory 2022 6:17
  4. Dynamic simulation of materials is a promising application for near-term quantum computers. Current algorithms for Hamiltonian simulation, however, produce circuits that grow in depth with increasing simulatio...

    Authors: Lindsay Bassman Oftelie, Roel Van Beeumen, Ed Younis, Ethan Smith, Costin Iancu and Wibe A. de Jong
    Citation: Materials Theory 2022 6:13

    The Correction to this article has been published in Materials Theory 2022 6:19

  5. Molecular science is governed by the dynamics of electrons and atomic nuclei, and by their interactions with electromagnetic fields. A faithful physicochemical understanding of these processes is crucial for t...

    Authors: Hongbin Liu, Guang Hao Low, Damian S. Steiger, Thomas Häner, Markus Reiher and Matthias Troyer
    Citation: Materials Theory 2022 6:11
  6. The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues of a Hamiltonian. VQE has been proposed as an alternative to fully quantum ...

    Authors: Dmitry A. Fedorov, Bo Peng, Niranjan Govind and Yuri Alexeev
    Citation: Materials Theory 2022 6:2
  7. We present a quantum algorithm for data classification based on the nearest-neighbor learning algorithm. The classification algorithm is divided into two steps: Firstly, data in the same class is divided into ...

    Authors: Junxu Li and Sabre Kais
    Citation: Materials Theory 2021 5:6