A new QPCA algorithm replaces quantum phase estimation with a block-encoding and quantum power method, with complexity depending on the eigenvalue gap rather than the largest eigenvalue.
Improved Quantum Power Method and Numerical Integration Using Quantum Singular Value Transformation
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abstract
Quantum singular value transformation (QSVT) is a framework that has been shown to unify many primitives in quantum algorithms. In this work, we leverage the QSVT framework in two directions. We first show that the QSVT framework can accelerate one recently introduced quantum power method, which substantially improves its running time. Additionally, we incorporate several elementary numerical integration techniques, such as the rectangular method, Monte Carlo method, and quadrature method, into the QSVT framework, which results in polynomial speedup with respect to the size or the number of points of the grid. Our results thus provide further examples to demonstrate the potential of the QSVT and how it may enhance quantum algorithmic tasks.
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New Quantum Algorithm for Principal Component Analysis
A new QPCA algorithm replaces quantum phase estimation with a block-encoding and quantum power method, with complexity depending on the eigenvalue gap rather than the largest eigenvalue.