Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer

• URL: http://arxiv.org/abs/2206.06290v2
• Date: Sat, 1 Oct 2022 15:29:28 GMT
• Title: Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer
• Authors: Pradeep Niroula, Ruslan Shaydulin, Romina Yalovetzky, Pierre Minssen, Dylan Herman, Shaohan Hu, Marco Pistoia
• Abstract summary: We show the largest-to-date execution of a quantum optimization algorithm that preserves constraints on quantum hardware. We execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 20 qubits and a two-qubit gate depth of up to 159. We discuss the respective trade-offs of the algorithms and implications for their execution on near-term quantum hardware.
• Score: 13.528362112761805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA) on trapped-ion quantum computer. We successfully execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 20 qubits and a two-qubit gate depth of up to 159. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the trade-off between the in-constraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this trade-off makes choosing good parameters difficult in general. We compare XY-QAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constant-depth circuit, and the Quantum Approximate Optimization Algorithm. We discuss the respective trade-offs of the algorithms and implications for their execution on near-term quantum hardware.

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