24–29 Nov 2024
University of Melbourne
Australia/Melbourne timezone

Learning shallow quantum circuits with many-qubit gates

26 Nov 2024, 11:35
15m
Carrillo Gantner Theatre (University of Melbourne)

Carrillo Gantner Theatre

University of Melbourne

Sidney Myer Asia Centre, Swanston St, University of Melbourne VIC 3010, Australia
Short Oral Presentation Quantum learning theory

Speaker

Francisca Vasconcelos (UC Berkeley)

Description

Shallow quantum circuits lie at the forefront of modern experimental capabilities and theoretical understanding. In this work, we present the first computationally-efficient algorithm for average-case learning of a class of shallow circuits with many-qubit gates. Namely, we provide a quasipolynomial sample and time complexity algorithm for learning 1/poly(n)-approximate unitaries of QAC^0 circuits, i.e., constant-depth circuits with arbitrary single-qubit gates and polynomially many CZ gates of un-bounded width. Note that QAC^0 implements circuits requiring linear-depth in the 1D geometry and
logarithmic-depth in the all-to-all-connected geometry of constant-width gates. Furthermore, leveraging our learned QAC0 unitaries, we provide an efficient algorithm for circuit synthesis of poly-logarithmic depth QAC circuits, making progress towards a quasipolynomial proper-learning algorithm for QAC0.

Primary authors

Francisca Vasconcelos (UC Berkeley) Hsin-Yuan Huang (California Institute of Technology; Google Quantum AI; Massachusetts Institute of Technology)

Presentation materials