Synergistic pretraining of parametrized quantum circuits via tensor networks - INSPIRE

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Synergistic pretraining of parametrized quantum circuits via tensor networks

Manuel S. Rudolph
(
- Unlisted, CA
)
,
Jacob Miller
(
- Unlisted, US and
- Unlisted, CA
)
,
Danial Motlagh
(
- Unlisted, CA
)
,
Jing Chen
(
- Unlisted, US and
- Unlisted, US, MA
)
,
Atithi Acharya
(
- Unlisted, US and
- Unlisted, US, MA and
- Rutgers U., Piscataway
)

Aug 29, 2022

10 pages

Published in:

Nature Commun. 14 (2023) 1, 8367

Published: Dec 15, 2023

e-Print:

2208.13673 [quant-ph]

DOI:

10.1038/s41467-023-43908-6

View in:

ADS Abstract Service

reference search79 citations

Citations per year

Abstract: (Springer)

Parametrized quantum circuits (PQCs) represent a promising framework for using present-day quantum hardware to solve diverse problems in materials science, quantum chemistry, and machine learning. We introduce a “synergistic” approach that addresses two prominent issues with these models: the prevalence of barren plateaus in PQC optimization landscapes, and the difficulty to outperform state-of-the-art classical algorithms. This framework first uses classical resources to compute a tensor network encoding a high-quality solution, and then converts this classical output into a PQC which can be further improved using quantum resources. We provide numerical evidence that this framework effectively mitigates barren plateaus in systems of up to 100 qubits using only moderate classical resources, with overall performance improving as more classical or quantum resources are employed. We believe our results highlight that classical simulation methods are not an obstacle to overcome in demonstrating practically useful quantum advantage, but rather can help quantum methods find their way.

Note:

15 pages, 7 figures

network: tensor
chemistry: quantum
quantum circuit
quantum advantage
noisy intermediate-scale quantum
optimization
performance
qubit
quantum state
quantum simulation

References(71)

Figures(7)

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