Multi-reference Quantum Davidson Algorithm for Quantum Dynamics

TL;DR

This paper introduces a quantum Davidson algorithm-based Krylov subspace method that improves the efficiency and convergence of quantum simulations for many-body systems, suitable for noisy intermediate-scale quantum devices.

Contribution

It presents a novel iterative QKS method derived from the QDavidson algorithm, reducing circuit depth and iteration count for quantum simulations.

Findings

Faster convergence with fewer iterations.

Shallower quantum circuits required.

Enhanced suitability for noisy quantum devices.

Abstract

Simulating quantum systems is one of the most promising tasks where quantum computing can potentially outperform classical computing. However, the robustness needed for reliable simulations of medium to large systems is beyond the reach of existing quantum devices. To address this, Quantum Krylov Subspace (QKS) methods have been developed, enhancing the ability to perform accelerated simulations on noisy intermediate-scale quantum computers. In this study, we introduce and evaluate two QKS methods derived from the QDavidson algorithm, a novel approach for determining the ground and excited states of many-body systems. Unlike other QKS methods that pre-generate the Krylov subspace through real- or imaginary-time evolution, QDavidson iteratively adds basis vectors into the Krylov subspace. This iterative process enables faster convergence with fewer iterations and necessitates shallower circuit depths, marking a significant advancement in the field of quantum simulation.