Measurement-efficient quantum Krylov subspace diagonalisation

Abstract

The Krylov subspace methods, being one category of the most important classical numerical methods for linear algebra problems, can be much more powerful when generalised to quantum computing. However, quantum Krylov subspace algorithms are prone to errors due to inevitable statistical fluctuations in quantum measurements. To address this problem, we develop a general theoretical framework to analyse the statistical error and measurement cost. Based on the framework, we propose a quantum algorithm to construct the Hamiltonian-power Krylov subspace that can minimise the measurement cost. In our algorithm, the product of power and Gaussian functions of the Hamiltonian is expressed as an integral of the real-time evolution, such that it can be evaluated on a quantum computer. We compare our algorithm with other established quantum Krylov subspace algorithms in solving two prominent examples. To achieve an error comparable to that of the classical Lanczos algorithm at the same subspace dimension, our algorithm typically requires orders of magnitude fewer measurements than others. Such an improvement can be attributed to the reduced cost of composing projectors onto the ground state. These results show that our algorithm is exceptionally robust to statistical fluctuations and promising for practical applications.

29 pages, 9 figures

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Cited by in corpus (7)

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• Molecular Properties from Quantum Krylov Subspace Diagonalization
• Cheaper and more noise-resilient quantum state preparation using eigenvector continuation
• Variational Quantum Subspace Construction via Symmetry-Preserving Cost Functions
• Hierarchical Fusion Method for Scalable Quantum Eigenstate Preparation