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Quantum Machine Learning

Learning shallow quantum circuits with many-qubit gates

arXiv
Authors: Francisca Vasconcelos, Hsin-Yuan Huang

Year

2024

Paper ID

37816

Status

Preprint

Abstract Read

~2 min

Abstract Words

94

Citations

N/A

Abstract

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 QAC0 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, QAC0 circuits require polynomial depth to implement.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • We present the first computationally-efficient algorithm for average-case learning of shallow quantum circuits with many-qubit gates.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2410.16693 [2] arXiv https://arxiv.org/abs/2410.16693 [3] Publisher https://arxiv.org/abs/2410.16693

Local Citation Graph (Related-Paper Links)

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