End-to-End Protocol for High-Quality QAOA Parameters with Few Shots

2024-08-01

arXiv.org

AI Summary

Addressing the challenge of parameter optimization for the Quantum Approximate Optimization Algorithm (QAOA) under few-shot constraints, this work introduces an end-to-end optimization protocol integrating multi-start initialization, a linear-response optimizer, and numerical pre-optimization, validated in closed-loop on real noisy hardware. For the first time, instance-level fine-tuning of a 5-layer QAOA is demonstrated on a 32-qubit trapped-ion processor—setting a record for two-qubit gate count. The linear-response optimizer is shown to be both computationally efficient and robust to noise at low shot budgets (<10⁴). Compared to conventional approaches, our method significantly reduces the number of shots required for convergence, enhances optimization stability, and improves solution quality. This establishes a scalable, hardware-efficient optimization paradigm for QAOA deployment on intermediate-scale noisy quantum processors.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizing QAOA parameters for combinatorial optimization problems
• Fine-tuning parameters with limited circuit executions (shots)
• Developing robust protocol for noisy quantum hardware

Innovation

• End-to-end protocol for QAOA parameters
• Optimizes parameters with limited shots
• Robust to hardware noise in trapped-ion