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Bibliographic Details
Main Authors: Vasconcelos, Francisca, Huang, Hsin-Yuan
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2410.16693
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author Vasconcelos, Francisca
Huang, Hsin-Yuan
author_facet Vasconcelos, Francisca
Huang, Hsin-Yuan
contents We present the first computationally-efficient algorithm for average-case learning of shallow quantum circuits with many-qubit gates. Specifically, we provide a quasi-polynomial time and sample complexity algorithm for learning unknown QAC$^0$ circuits -- constant-depth circuits with arbitrary single-qubit gates and polynomially many $CZ$ gates of unbounded width -- with at most logarithmic ancilla, up to inverse-polynomially small error. Furthermore, we show that the learned unitary can be efficiently synthesized in poly-logarithmic depth. This work expands the family of efficiently learnable quantum circuits, notably since in finite-dimensional circuit geometries, QAC$^0$ circuits require polynomial depth to implement.
format Preprint
id arxiv_https___arxiv_org_abs_2410_16693
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Learning shallow quantum circuits with many-qubit gates
Vasconcelos, Francisca
Huang, Hsin-Yuan
Quantum Physics
We present the first computationally-efficient algorithm for average-case learning of shallow quantum circuits with many-qubit gates. Specifically, we provide a quasi-polynomial time and sample complexity algorithm for learning unknown QAC$^0$ circuits -- constant-depth circuits with arbitrary single-qubit gates and polynomially many $CZ$ gates of unbounded width -- with at most logarithmic ancilla, up to inverse-polynomially small error. Furthermore, we show that the learned unitary can be efficiently synthesized in poly-logarithmic depth. This work expands the family of efficiently learnable quantum circuits, notably since in finite-dimensional circuit geometries, QAC$^0$ circuits require polynomial depth to implement.
title Learning shallow quantum circuits with many-qubit gates
topic Quantum Physics
url https://arxiv.org/abs/2410.16693