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.
Quantum Computing for nonlinear differential equations and turbulence
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abstract
A large spectrum of problems in classical physics and engineering, such as turbulence, is governed by nonlinear differential equations, which typically require high-performance computing to be solved. Over the past decade, however, the growth of classical computing power has slowed down because the miniaturisation of chips has been approaching the atomic scale. This is marking an end to Moore's law, which calls for a new computing paradigm: Quantum computing is a prime candidate. In this paper, we offer a perspective on the current challenges that need to be overcome in order to use quantum computing for the simulation of nonlinear dynamics. We review and discuss progress in the development of both quantum algorithms for nonlinear equations and quantum hardware. We propose pairings between quantum algorithms for nonlinear equations and quantum hardware concepts. These avenues open new opportunities for the simulation of nonlinear systems and turbulence.
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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.