Accelerating Classical and Quantum Tensor PCA
Matthew B. Hastings

TL;DR
This paper presents quadratic speedups for both classical and quantum algorithms solving tensor PCA, maintaining a quartic separation, with implications for detection and potential recovery, and discusses recent bounds affecting these speedups.
Contribution
It introduces accelerated classical and quantum algorithms for tensor PCA that preserve a quartic speedup, with analysis of spectral properties influencing their effectiveness.
Findings
Classical algorithm is quadratically faster than the original.
Quantum algorithm is eight times faster than the classical.
Further modifications increase quantum speedup to six times over the classical.
Abstract
Spectral methods are a leading approach for tensor PCA with a ``spiked" Gaussian tensor. The methods use the spectrum of a linear operator in a vector space with exponentially high dimension and in Ref. 1 it was shown that quantum algorithms could then lead to an exponential space saving as well as a quartic speedup over classical. Here we show how to accelerate both classical and quantum algorithms quadratically, while maintaining the same quartic separation between them. That is, our classical algorithm here is quadratically faster than the original classical algorithm, while the quantum algorithm is eigth-power faster than the original classical algorithm. We then give a further modification of the quantum algorithm, increasing its speedup over the modified classical algorithm to the sixth power. We only prove these speedups for detection, rather than recovery, but we give a strong…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Tensor decomposition and applications · Quantum Information and Cryptography
