Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass models

• URL: http://arxiv.org/abs/2311.02151v1
• Date: Fri, 3 Nov 2023 18:00:00 GMT
• Title: Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass models
• Authors: Elisabeth Wybo and Martin Leib
• Abstract summary: parity mapping provides a geometrically local encoding of the Quantum Approximate Optimization Algorithm (QAOA) We benchmark the parity-encoded QAOA on spin-glass models. We show that for fixed number of parity-encoded QAOA layers, the performance drops as $N-1/2$.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The parity mapping provides a geometrically local encoding of the Quantum Approximate Optimization Algorithm (QAOA), at the expense of having a quadratic qubit overhead for all-to-all connected problems. In this work, we benchmark the parity-encoded QAOA on spin-glass models. We address open questions in the scaling of this algorithm, and show that for fixed number of parity-encoded QAOA layers, the performance drops as $N^{-1/2}$. We perform tensor-network calculations to confirm this result, and comment on the concentration of optimal QAOA parameters over problem instances.

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