TL;DR
This paper provides a comprehensive tutorial on quantum singular value transformation (QSVT), unifying major quantum algorithms like search, phase estimation, and Hamiltonian simulation into a single framework, highlighting their interconnectedness.
Contribution
It introduces a pedagogical overview of QSVT, demonstrating how it unifies key quantum algorithms and extends quantum signal processing to a broader eigenvalue transform.
Findings
QSVT encompasses major quantum algorithms
Quantum algorithms for search, phase estimation, and simulation are unified under QSVT
QSVT offers a single framework for diverse quantum computational tasks
Abstract
Quantum algorithms offer significant speedups over their classical counterparts for a variety of problems. The strongest arguments for this advantage are borne by algorithms for quantum search, quantum phase estimation, and Hamiltonian simulation, which appear as subroutines for large families of composite quantum algorithms. A number of these quantum algorithms were recently tied together by a novel technique known as the quantum singular value transformation (QSVT), which enables one to perform a polynomial transformation of the singular values of a linear operator embedded in a unitary matrix. In the seminal GSLW'19 paper on QSVT [Gily\'en, Su, Low, and Wiebe, ACM STOC 2019], many algorithms are encompassed, including amplitude amplification, methods for the quantum linear systems problem, and quantum simulation. Here, we provide a pedagogical tutorial through these developments,…
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