Online Detection of Golden Circuit Cutting Points

• URL: http://arxiv.org/abs/2308.10153v1
• Date: Sun, 20 Aug 2023 03:56:31 GMT
• Title: Online Detection of Golden Circuit Cutting Points
• Authors: Daniel T. Chen and Ethan H. Hansen and Xinpeng Li and Aaron Orenstein and Vinooth Kulkarni and Vipin Chaudhary and Qiang Guan and Ji Liu and Yang Zhang and Shuai Xu
• Abstract summary: We introduce the concept of a golden cutting point, which identifies unnecessary basis components during reconstruction and avoids related down-stream computation. We demonstrate the applicability of our method on Qiskit's Aer simulator and observe a reduced wall time from identifying and avoiding obsolete measurements.
• Score: 12.76725337820984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum circuit cutting has emerged as a promising method for simulating large quantum circuits using a collection of small quantum machines. Running low-qubit "circuit fragments" not only overcomes the size limitation of near-term hardware, but it also increases the fidelity of the simulation. However, reconstructing measurement statistics requires computational resources - both classical and quantum - that grow exponentially with the number of cuts. In this manuscript, we introduce the concept of a golden cutting point, which identifies unnecessary basis components during reconstruction and avoids related down-stream computation. We propose a hypothesis-testing scheme for identifying golden cutting points, and provide robustness results in the case of the test failing with low probability. Lastly, we demonstrate the applicability of our method on Qiskit's Aer simulator and observe a reduced wall time from identifying and avoiding obsolete measurements.

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