An adaptive estimator based on empirical Bernstein stopping reduces the number of measurements needed to estimate ground-state energies with rigorous error bounds, by up to an order of magnitude in numerical benchmarks.
Emerging quantum computing algorithms for quantum chemistry
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Digital quantum computers provide a computational framework for solving the Schr\"{o}dinger equation for a variety of many-particle systems. Quantum computing algorithms for the quantum simulation of these systems have recently witnessed remarkable growth, notwithstanding the limitations of existing quantum hardware, especially as a tool for electronic structure computations in molecules. In this review, we provide a self-contained introduction to emerging algorithms for the simulation of Hamiltonian dynamics and eigenstates, with emphasis on their applications to the electronic structure in molecular systems. Theoretical foundations and implementation details of the method are discussed, and their strengths, limitations, and recent advances are presented.
citation-role summary
citation-polarity summary
fields
quant-ph 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Reducing the sampling complexity of energy estimation in quantum many-body systems using empirical variance information
An adaptive estimator based on empirical Bernstein stopping reduces the number of measurements needed to estimate ground-state energies with rigorous error bounds, by up to an order of magnitude in numerical benchmarks.