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Quantum simulation-based optimization for cooling system design
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AI
DOI：10.1088/1751-8121/ae4c31.png)
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Abstract
En 中文
Engineering design processes involve iterative design evaluations requiring numerous computationally intensive numerical simulations. Quantum algorithms promise substantial speedups for specific tasks relevant to engineering simulations. However, these advantages quickly vanish when considering data input and output on quantum computers. The recently introduced quantum simulation-based optimization (QuSO) framework circumvents this limitation by treating simulations as subproblems within a larger optimization problem. Here we adapt and implement QuSO for a simplified cooling system design problem, validate correctness in statevector simulations, and present a detailed gate-level complexity analysis for a single QuSO iteration. We express the scaling in terms of problem parameters and quantum approximate optimization algorithm (QAOA) depth and iterations. We show that the cost function can be coherently computed over a superposition of exponentially many configurations using circuits of polynomial complexity. This does not yield a speedup for a single simulation instance, but it enables potential advantages arising from the subsequent QAOA-based search over configurations. The study serves as a proof-of-concept for integrating fault-tolerant quantum subroutines with simulation-based optimization in engineering workflows, clarifying both promise and practical limitations.
Keywords:
quantum simulation-based optimization (QuSO)
quantum approximate optimization algorithm (QAOA)
linear combination of unitaries (LCU)
quantum singular value transform (QSVT)
quantum amplitude estimation (QAE)
Journal
J
IF:
2.1
Papers:
255
Citations:
0