QAOAKit: A Toolkit for Reproducible Study, Application, and Verification of the QAOA

Abstract

Understanding the best known parameters, performance, and systematic behavior of the Quantum Approximate Optimization Algorithm (QAOA) remain open research questions, even as the algorithm gains popularity. We introduce QAOAKit, a Python toolkit for the QAOA built for exploratory research. QAOAKit is a unified repository of preoptimized QAOA parameters and circuit generators for common quantum simulation frameworks. We combine, standardize, and cross-validate previously known parameters for the MaxCut problem, and incorporate this into QAOAKit. We also build conversion tools to use these parameters as inputs in several quantum simulation frameworks that can be used to reproduce, compare, and extend known results from various sources in the literature. We describe QAOAKit and provide examples of how it can be used to reproduce research results and tackle open problems in quantum optimization.

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References in corpus (7)

• A Quantum Approximate Optimization Algorithm
• Learning to learn with quantum neural networks via classical neural networks
• For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances
• Parameters Fixing Strategy for Quantum Approximate Optimization Algorithm
• Error Mitigation for Deep Quantum Optimization Circuits by Leveraging Problem Symmetries
• The fixed angle conjecture for QAOA on regular MaxCut graphs
• An explicit vector algorithm for high-girth MaxCut

Cited by in corpus (17)

• Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut
• Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer
• Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Optimization
• Quantum approximate optimization via learning-based adaptive optimization
• Parameter Setting in Quantum Approximate Optimization of Weighted Problems
• Sampling Frequency Thresholds for Quantum Advantage of Quantum Approximate Optimization Algorithm
• Fast Simulation of High-Depth QAOA Circuits
• High-Round QAOA for MAX $k$-SAT on Trapped Ion NISQ Devices
• Encoding trade-offs and design toolkits in quantum algorithms for discrete optimization: coloring, routing, scheduling, and other problems
• Characterizing Error Mitigation by Symmetry Verification in QAOA
• Qoncord: A Multi-Device Job Scheduling Framework for Variational Quantum Algorithms
• QAOA with $N\cdot p\ge 200$
• JuliQAOA: Fast, Flexible QAOA Simulation
• Parameter Setting Heuristics Make the Quantum Approximate Optimization Algorithm Suitable for the Early Fault-Tolerant Era
• Red-QAOA: Efficient Variational Optimization through Circuit Reduction
• Quantum Approximate Optimization Algorithm with Sparsified Phase Operator
• Accelerating Simulation of Quantum Circuits under Noise via Computational Reuse