Grant, E;
Wossnig, L;
Ostaszewski, M;
Benedetti, M;
(2019)
An initialization strategy for addressing barren plateaus in parametrized quantum circuits.
Quantum
, 3
, Article 214. 10.22331/q-2019-12-09-214.
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Abstract
Parametrized quantum circuits initialized with random initial parameter values are characterized by barren plateaus where the gradient becomes exponentially small in the number of qubits. In this technical note we theoretically motivate and empirically validate an initialization strategy which can resolve the barren plateau problem for practical applications. The technique involves randomly selecting some of the initial parameter values, then choosing the remaining values so that the circuit is a sequence of shallow blocks that each evaluates to the identity. This initialization limits the effective depth of the circuits used to calculate the first parameter update so that they cannot be stuck in a barren plateau at the start of training. In turn, this makes some of the most compact ans\"atze usable in practice, which was not possible before even for rather basic problems. We show empirically that variational quantum eigensolvers and quantum neural networks initialized using this strategy can be trained using a gradient based method.
Type: | Article |
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Title: | An initialization strategy for addressing barren plateaus in parametrized quantum circuits |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.22331/q-2019-12-09-214 |
Publisher version: | http://dx.doi.org/10.22331/q-2019-12-09-214 |
Language: | English |
Additional information: | © 2019. This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | Quantum Neural Networks, Variational Quantum Eigensolvers, Quantum Machine Learning |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10126773 |




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