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Calibrating the Classical Hardness of the Quantum Approximate Optimization Algorithm
M. Dupont, N. Didier, Mark Hodson, J. Moore, M. Reagor·June 13, 2022·DOI: 10.1103/PRXQuantum.3.040339
Physics
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Abstract
Trading fidelity for scale enables approximate classical simulators such as matrix product states (MPS) to run quantum circuits beyond exact methods. A control parameter, the so-called bond dimension 𝜒 for MPS, governs the allocated computational resources and the output fidelity. Here, we characterize the fidelity for the quantum approximate optimization algorithm by the expectation value of the cost function it seeks to minimize and find that it follows a scaling law F