Scaling Quantum Simulation-Based Optimization: Demonstrating Efficient Power Grid Management with Deep QAOA Circuits

• URL: http://arxiv.org/abs/2505.16444v1
• Date: Thu, 22 May 2025 09:33:29 GMT
• Title: Scaling Quantum Simulation-Based Optimization: Demonstrating Efficient Power Grid Management with Deep QAOA Circuits
• Authors: Maximilian Adler, Jonas Stein, Michael Lachner,
• Abstract summary: This work extends initial theoretical results that proved an up-to-exponential speedup for the simulation component of the QAOA-based QuSO solver.<n>We develop a very efficient classical quantum circuit simulation that bypasses costly ancillary qubit requirements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum Simulation-based Optimization (QuSO) is a recently proposed class of optimization problems that entails industrially relevant problems characterized by cost functions or constraints that depend on summary statistic information about the simulation of a physical system or process. This work extends initial theoretical results that proved an up-to-exponential speedup for the simulation component of the QAOA-based QuSO solver proposed by Stein et al. for the unit commitment problem by an empirical evaluation of the optimization component using a standard benchmark dataset, the IEEE 57-bus system. Exploiting clever classical pre-computation, we develop a very efficient classical quantum circuit simulation that bypasses costly ancillary qubit requirements by the original algorithm, allowing for large-scale experiments. Utilizing more than 1000 QAOA layers and up to 20 qubits, our experiments complete a proof of concept implementation for the proposed QuSO solver, showing that it can achieve both highly competitive performance and efficiency in its optimization component compared to a standard classical baseline, i.e., simulated annealing.

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