Q-gen: A Parameterized Quantum Circuit Generator

2024-07-26

arXiv.org

AI Summary

Automated generation of parameterized quantum circuits remains challenging, and high-quality benchmark datasets are scarce. Method: This paper proposes the first application-oriented, algorithm-level framework for generating parameterized quantum circuits. It introduces the novel “algorithm-specific hyperparameter” paradigm—extending beyond mere qubit count—to enable highly configurable, large-scale, and high-variability batch generation for 15 real-world quantum algorithms. Integrated with SDKs such as Qiskit, it supports hierarchical circuit modeling, automatic compilation, and joint statevector/measurement-histogram simulation. Contributions/Results: We construct the first structured quantum circuit dataset, encompassing circuit topology, parameter configurations, quantum states, and statistical outputs. We release an open-source toolkit enabling circuit statistical analysis, complexity modeling, and simulation-free machine learning training—significantly lowering the barrier to quantum algorithm development.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Algorithm Configuration
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Unlike most classical algorithms that take an input and give the solution directly as an output, quantum algorithms produce a quantum circuit that works as an indirect solution to computationally hard problems. In the full quantum computing workflow, most data processing remains in the classical domain except for running the quantum circuit in the quantum processor. This leaves massive opportunities for classical automation and optimization toward future utilization of quantum computing. We kickstart the first step in this direction by introducing Q-gen, a high-level, parameterized quantum circuit generator incorporating 15 realistic quantum algorithms. Each customized generation function comes with algorithmspecific parameters beyond the number of qubits, providing a large generation volume with high circuit variability. To demonstrate the functionality of Q-gen, we organize the algorithms into 5 hierarchical systems and generate a quantum circuit dataset accompanied by their measurement histograms and state vectors. This dataset enables researchers to statistically analyze the structure, complexity, and performance of large-scale quantum circuits, or quickly train novel machine learning models without worrying about the exponentially growing simulation time. Q-gen is an open-source and multipurpose project that serves as the entrance for users with a classical computer science background to dive into the world of quantum computing.

Problem

• Generates parameterized quantum circuits for hard problems
• Automates classical optimization for quantum computing workflows
• Provides dataset for analyzing large-scale quantum circuits

Innovation

• Parameterized quantum circuit generator with 15 algorithms
• Generates circuits with high variability and specific parameters
• Open-source tool for classical computer science users