Scaling whole-chip QAOA for higher-order ising spin glass models on heavy-hex graphs

Journal Article · 05 November 2024 · npj Quantum Information

DOI: https://doi.org/10.1038/s41534-024-00906-w · OSTI ID:2476405

Pelofske, Elijah ; Bärtschi, Andreas ; Cincio, Lukasz; Golden, John ; Eidenbenz, Stephan

Abstract We show that the quantum approximate optimization algorithm (QAOA) for higher-order, random coefficient, heavy-hex compatible spin glass Ising models has strong parameter concentration across problem sizes from 16 up to 127 qubits for p  = 1 up to p  = 5, which allows for computationally efficient parameter transfer of QAOA angles. Matrix product state (MPS) simulation is used to compute noise-free QAOA performance. Hardware-compatible short-depth QAOA circuits are executed on ensembles of 100 higher-order Ising models on noisy IBM quantum superconducting processors with 16, 27, and 127 qubits using QAOA angles learned from a single 16-qubit instance using the JuliQAOA tool. We show that the best quantum processors find lower energy solutions up to p  = 2 or p  = 3, and find mean energies that are about a factor of two off from the noise-free distribution. We show that p  = 1 QAOA energy landscapes remain very similar as the problem size increases using NISQ hardware gridsearches with up to a 414 qubit processor.

• Sponsoring Organization: USDOE
• OSTI ID: 2476405
• Journal Information: npj Quantum Information, Journal Name: npj Quantum Information Journal Issue: 1 Vol. 10; ISSN 2056-6387
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English