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arXiv · 1803.04114

Learning the quantum algorithm for state overlap

Lukasz Cincio·Yiğit Subaşı·Andrew T. Sornborger·Patrick J. Coles
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Abstract

Short-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing the overlap ${\rm Tr}(ρσ)$ between two quantum states $ρ$ and $σ$. The standard algorithm for this task, known as the Swap Test, is used in many applications such as quantum support vector machines, and, when specialized to $ρ= σ$, quantifies the Renyi entanglement. Here, we find algorithms that have shorter depths than the Swap Test, including one that has a constant depth (independent of problem size). Furthermore, we apply our approach to the hardware-specific connectivity and gate sets used by Rigetti's and IBM's quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error - compared to the Swap Test - on these computers.

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Lukasz Cincio, Yiğit Subaşı, Andrew T. Sornborger, Patrick J. Coles. 2018-11-16. Learning the quantum algorithm for state overlap. https://doi.org/10.1088/1367-2630%2Faae94a

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