Quantum Annealing vs. QAOA: 127 Qubit Higher-Order Ising Problems on NISQ Computers

• URL: http://arxiv.org/abs/2301.00520v2
• Date: Sat, 18 Mar 2023 07:23:41 GMT
• Title: Quantum Annealing vs. QAOA: 127 Qubit Higher-Order Ising Problems on NISQ Computers
• Authors: Elijah Pelofske, Andreas B\"artschi, Stephan Eidenbenz
• Abstract summary: Quantum Alternating Operator Ansatz (QAOA) are both quantum algorithms intended for optimal solutions of optimization problems. We implement a rigorous comparison between QA on D-Wave hardware and QAOA on IBMQ hardware. We find that QA outperforms QAOA on all problem instances.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum annealing (QA) and Quantum Alternating Operator Ansatz (QAOA) are both heuristic quantum algorithms intended for sampling optimal solutions of combinatorial optimization problems. In this article we implement a rigorous direct comparison between QA on D-Wave hardware and QAOA on IBMQ hardware. These two quantum algorithms are also compared against classical simulated annealing. The studied problems are instances of a class of Ising models, with variable assignments of $+1$ or $-1$, that contain cubic $ZZZ$ interactions (higher order terms) and match both the native connectivity of the Pegasus topology D-Wave chips and the heavy hexagonal lattice of the IBMQ chips. The novel QAOA implementation on the heavy hexagonal lattice has a CNOT depth of $6$ per round and allows for usage of an entire heavy hexagonal lattice. Experimentally, QAOA is executed on an ensemble of randomly generated Ising instances with a grid search over $1$ and $2$ round angles using all 127 programmable superconducting transmon qubits of ibm_washington. The error suppression technique digital dynamical decoupling is also tested on all QAOA circuits. QA is executed on the same Ising instances with the programmable superconducting flux qubit devices D-Wave Advantage_system4.1 and Advantage_system6.1 using modified annealing schedules with pauses. We find that QA outperforms QAOA on all problem instances. We also find that dynamical decoupling enables 2-round QAOA to marginally outperform 1-round QAOA, which is not the case without dynamical decoupling.

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