Instance Independence of Single Layer Quantum Approximate Optimization Algorithm on Mixed-Spin Models at Infinite Size
arXiv:2102.12043 · doi:10.22331/q-2021-09-15-542
Abstract
This paper studies the application of the Quantum Approximate Optimization Algorithm (QAOA) to spin-glass models with random multi-body couplings in the limit of a large number of spins. We show that for such mixed-spin models the performance of depth QAOA is independent of the specific instance in the limit of infinite sized systems and we give an explicit formula for the expected performance. We also give explicit expressions for the higher moments of the expected energy, thereby proving that the expected performance of QAOA concentrates.
arXiv commentLatest version accepted in Quantum. 13 pages, 3 figures
References in corpus (6)
- Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices
- Quantum Approximate Optimization Algorithm for MaxCut: A Fermionic View
- The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size
- Near-optimal quantum circuit for Grover's unstructured search using a transverse field
- The Parisi formula for mixed -spin models
- The Sherrington-Kirkpatrick model: an overview
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