Enhancing quantum computations with the synergy of auxiliary field quantum Monte Carlo and computational basis tomography
Viktor Khinevich, Wataru Mizukami

TL;DR
This paper presents QC-CBT-AFQMC, a hybrid quantum-classical algorithm that improves the efficiency and accuracy of quantum chemistry calculations by integrating computational basis tomography with auxiliary-field quantum Monte Carlo, reducing computational costs.
Contribution
The paper introduces QC-CBT-AFQMC, a novel hybrid method that replaces classical shadows with CBT, significantly reducing two-qubit gate requirements and enabling practical quantum chemistry simulations.
Findings
Accurately models potential energy curves for molecules.
Reduces two-qubit gates by a quadratic factor.
Successfully estimates reaction barriers with high agreement.
Abstract
We introduce QC-CBT-AFQMC, a hybrid algorithm that incorporates computational basis tomography (CBT) into the quantum-classical auxiliary-field quantum Monte Carlo (QC-AFQMC) method proposed by Huggins et al. [Nature 603, 416-420 (2022)], replacing the use of classical shadows. While the original QC-AFQMC showed high accuracy for quantum chemistry calculations, it required exponentially costly post-processing. Subsequent work using Matchgate shadows [Commun. Math. Phys. 404, 629 (2023)] improved scalability, but still suffers from prohibitive computational requirements that limit practical applications. Our QC-CBT-AFQMC approach uses shallow Clifford circuits with a quadratic reduction of two-qubit gates over the original algorithm, significantly reducing computational requirements and enabling accurate calculations under limited measurement budgets. We demonstrate its effectiveness on…
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Taxonomy
TopicsAdvanced Chemical Physics Studies · Machine Learning in Materials Science · Quantum Computing Algorithms and Architecture
