End-to-End Protocol for High-Quality QAOA Parameters with Few Shots
arXiv:2408.00557 · doi:10.1103/24gg-7p8z
Abstract
The quantum approximate optimization algorithm (QAOA) is a quantum heuristic for combinatorial optimization that has been demonstrated to scale better than state-of-the-art classical solvers for some problems. For a given problem instance, QAOA performance depends crucially on the choice of the parameters. While average-case optimal parameters are available in many cases, meaningful performance gains can be obtained by fine-tuning these parameters for a given instance. This task is especially challenging, however, when the number of circuit executions (shots) is limited. In this work, we develop an end-to-end protocol that combines multiple parameter settings and fine-tuning techniques. We use large-scale numerical experiments to optimize the protocol for the shot-limited setting and observe that optimizers with the simplest internal model (linear) perform best. We implement the optimized pipeline on a trapped-ion processor using up to 32 qubits and 5 QAOA layers, and we demonstrate that the pipeline is robust to small amounts of hardware noise. To the best of our knowledge, these are the largest demonstrations of QAOA parameter fine-tuning on a trapped-ion processor in terms of 2-qubit gate count.
13+2 pages, 11+3 figures, accepted by Physical Review Research
References in corpus (21)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- Logical quantum processor based on reconfigurable atom arrays
- From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
- Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
- Derivative-free optimization methods
- A Race Track Trapped-Ion Quantum Processor
- Quantum Simulations of Classical Annealing Processes
- An Adaptive Optimizer for Measurement-Frugal Variational Algorithms
- Speed-up via Quantum Sampling
- Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem
- Parameter Concentration in Quantum Approximate Optimization
- Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut
- Using models to improve optimizers for variational quantum algorithms
- Quantum speedup of branch-and-bound algorithms
- Quantum approximate optimization via learning-based adaptive optimization
- Challenges of variational quantum optimization with measurement shot noise
- Parameter Setting in Quantum Approximate Optimization of Weighted Problems
- The computational power of random quantum circuits in arbitrary geometries
- Latency considerations for stochastic optimizers in variational quantum algorithms
- Resource frugal optimizer for quantum machine learning
- A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms