Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session A70: Machine Learning and Quantum Simulation
8:00 AM–11:00 AM,
Monday, March 6, 2023
Room: Room 409
Sponsoring
Unit:
DQI
Chair: Daoheng Niu, University of Texas at Austin
Abstract: A70.00006 : Variational learning algorithms for quantum query complexity*
9:00 AM–9:12 AM
Presenter:
Zipeng Wu
(The Hong Kong University of Science and)
Authors:
Zipeng Wu
(The Hong Kong University of Science and)
Bei Zeng
(Hong Kong University of Science and Technology)
ZHANG CHAO
(The Hong Kong University of Science and Technology)
Collaboration:
None
tures the most known quantum algorithms, such as search and period finding. A query algorithm
applies UtOx · · · U1OxU0 to some input state, where Ox is the oracle dependent on some input vari-
able x, and Uis are unitary operations that are independent of x, followed by some measurements
for readout. In this work, we develop variational learning algorithms to study quantum query com-
plexity, by formulating Uis as parameterized quantum circuits and introducing a loss function that
is directly given by the error probability of the query algorithm. We apply our method to ana-
lyze various cases of quantum query complexity, including a new algorithm solving the Hamming
modulo problem with 4 queries for the case of 5-bit modulo 5, answering an open question raised
in arXiv:2112.14682, and the result is further confirmed by a Semidefinite Programming (SDP)
algorithm. Compared with the SDP algorithm, our method can be readily implemented on the
near-term Noisy Intermediate-Scale Quantum (NISQ) devices and is more flexible to be adapted to
other cases such as the fractional query models.
*GRF grant No. 16305121
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