Berlin 2024 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

QI: Fachverband Quanteninformation

QI 18: Poster II

QI 18.1: Poster

Wednesday, March 20, 2024, 11:00–14:30, Poster A

Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware — Johannes Weidenfeller^1,2, •Lucia Valor¹, Julien Gacon^1,3, Caroline Tornow^1,2, Luciano Bello¹, Stefan Woerner¹, and Daniel Egger¹ — ¹IBM Quantum, IBM Research Europe, Zurich, Switzerland — ²ETH, Zurich, Switzerland — ³EPFL, Lausanne, Switzerland

Quantum computers may provide good solutions to combinatorial optimization problems by leveraging the Quantum Approximate Optimization Algorithm (QAOA). The QAOA is often presented as an algorithm for noisy hardware. However, hardware constraints limit its applicability to problem instances that closely match the connectivity of the qubits. Furthermore, the QAOA must outpace classical solvers. In our work, we investigate and benchmark swap strategies used to map dense problems into linear, grid and heavy-hex coupling maps. Using known entropic arguments, we find that the required gate fidelity for dense problems lies deep below the fault tolerant threshold. We also provide a methodology to reason about the execution-time of QAOA. Finally, we execute the closed-loop optimization on cloud-based quantum computers, using Qiskit Runtime, with transpiler settings optimized for QAOA. This work highlights some obstacles to improve to make QAOA competitive, such as gate fidelity, gate speed, and the large number of shots needed. The QAOA Qiskit Runtime program used acts as a tool to investigate such issues at scale on noisy superconducting qubit hardware.

Keywords: QAOA; Swap strategies; Hardware requirements; Connectivity; Qiskit Runtime