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

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 questio...

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Main Authors: Elisabeth Wybo, Martin Leib
Format: Article
Language:English
Published: Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften 2024-12-01
Series:Quantum
Online Access:https://quantum-journal.org/papers/q-2024-12-10-1554/pdf/
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author Elisabeth Wybo
Martin Leib
author_facet Elisabeth Wybo
Martin Leib
author_sort Elisabeth Wybo
collection DOAJ
description 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. In particular, we show that for fixed number of parity-encoded QAOA layers, the performance or the output energy, vanishes towards zero (the value achieved by random guessing) with problem size $N$ as $N^{-1/2}$. Our results suggest that the parity-encoded QAOA does not have a promising scaling compared to the standard version of QAOA. We perform tensor-network calculations to confirm our results, and comment on the concentration of optimal QAOA parameters over problem instances.
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publisher Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
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spelling doaj-art-e54cbfdca94a404facb0ee737876dca02025-08-20T01:59:16ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2024-12-018155410.22331/q-2024-12-10-155410.22331/q-2024-12-10-1554Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass modelsElisabeth WyboMartin LeibThe 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. In particular, we show that for fixed number of parity-encoded QAOA layers, the performance or the output energy, vanishes towards zero (the value achieved by random guessing) with problem size $N$ as $N^{-1/2}$. Our results suggest that the parity-encoded QAOA does not have a promising scaling compared to the standard version of QAOA. We perform tensor-network calculations to confirm our results, and comment on the concentration of optimal QAOA parameters over problem instances.https://quantum-journal.org/papers/q-2024-12-10-1554/pdf/
spellingShingle Elisabeth Wybo
Martin Leib
Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass models
Quantum
title Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass models
title_full Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass models
title_fullStr Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass models
title_full_unstemmed Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass models
title_short Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass models
title_sort vanishing performance of the parity encoded quantum approximate optimization algorithm applied to spin glass models
url https://quantum-journal.org/papers/q-2024-12-10-1554/pdf/
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AT martinleib vanishingperformanceoftheparityencodedquantumapproximateoptimizationalgorithmappliedtospinglassmodels