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Quantum Optimization Superconducting Qubits

Improved success probability with greater circuit depth for the quantum approximate optimization algorithm

arXiv

Authors: Andreas Bengtsson, Pontus Vikstål, Christopher Warren, Marika Svensson, Xiu Gu, Anton Frisk Kockum, Philip Krantz, Christian Križan, Daryoush Shiri, Ida-Maria Svensson, Giovanna Tancredi, Göran Johansson, Per Delsing, Giulia Ferrini, Jonas Bylander

Year

2019

Paper ID

39659

Status

Preprint

Abstract Read

~2 min

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Abstract

Present-day, noisy, small or intermediate-scale quantum processors---although far from fault-tolerant---support the execution of heuristic quantum algorithms, which might enable a quantum advantage, for example, when applied to combinatorial optimization problems. On small-scale quantum processors, validations of such algorithms serve as important technology demonstrators. We implement the quantum approximate optimization algorithm (QAOA) on our hardware platform, consisting of two superconducting transmon qubits and one parametrically modulated coupler. We solve small instances of the NP-complete exact-cover problem, with 96.6% success probability, by iterating the algorithm up to level two.

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This paper contributes to the Superconducting Qubits research area in the Quantum Articles archive.
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Present-day, noisy, small or intermediate-scale quantum processors---although far from fault-tolerant---support the execution of heuristic quantum algorithms, which might...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1912.10495 [2] arXiv https://arxiv.org/abs/1912.10495 [3] Publisher https://arxiv.org/abs/1912.10495

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