Evaluating Quantum Approximate Optimization Algorithm: A Case Study
arXiv:1910.04881 · doi:10.1109/IGSC48788.2019.8957201
Abstract
Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising quantum algorithms for the Noisy Intermediate-Scale Quantum (NISQ) era. Quantifying the performance of QAOA in the near-term regime is of utmost importance. We perform a large-scale numerical study of the approximation ratios attainable by QAOA is the low- to medium-depth regime. To find good QAOA parameters we perform 990 million 10-qubit QAOA circuit evaluations. We find that the approximation ratio increases only marginally as the depth is increased, and the gains are offset by the increasing complexity of optimizing variational parameters. We observe a high variation in approximation ratios attained by QAOA, including high variations within the same class of problem instances. We observe that the difference in approximation ratios between problem instances increases as the similarity between instances decreases. We find that optimal QAOA parameters concentrate for instances in out benchmark, confirming the previous findings for a different class of problems.
References in corpus (2)
Cited by in corpus (18)
- Quantum Computing for Finance: State of the Art and Future Prospects
- Warm-starting quantum optimization
- Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut
- Classical symmetries and the Quantum Approximate Optimization Algorithm
- Empirical performance bounds for quantum approximate optimization
- Multi-block ADMM Heuristics for Mixed-Binary Optimization on Classical and Quantum Computers
- Parameter Setting in Quantum Approximate Optimization of Weighted Problems
- Exploiting Symmetry Reduces the Cost of Training QAOA
- Evaluation of QAOA based on the approximation ratio of individual samples
- Scaling Quantum Approximate Optimization on Near-term Hardware
- QAOAKit: A Toolkit for Reproducible Study, Application, and Verification of the QAOA
- Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
- Multi-Angle QAOA Does Not Always Need All Its Angles
- High-Round QAOA for MAX -SAT on Trapped Ion NISQ Devices
- Quantum Error Mitigation via Quantum-Noise-Effect Circuit Groups
- MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA
- Heuristic Time Complexity of NISQ Shortest-Vector-Problem Solvers
- Multiclass Portfolio Optimization via Variational Quantum Eigensolver with Dicke State Ansatz