C2|Q>: A Robust Framework for Bridging Classical and Quantum Software Development

2025-10-03

AI Summary

Quantum software engineering (QSE) poses a significant barrier for classical developers due to the steep learning curve associated with low-level quantum details—including qubit encoding, quantum circuit construction, and hardware-specific optimization. Method: This paper introduces the first hardware-agnostic quantum software development framework, enabling end-to-end automated translation from high-level classical specifications (Python/JSON) to executable quantum programs via a modular architecture. It features a novel problem classification and Quantum-Compatible Format (QCF) generation mechanism, integrates a multi-dimensional hardware evaluation model (assessing fidelity, latency, and cost), provides intelligent hardware recommendations, and supports automatic result decoding. Contribution/Results: Experiments demonstrate 93.8% and 100% processing success rates on 434 Python snippets and 100 JSON inputs, respectively. When deployed on real NISQ devices, the framework achieves nearly 40× improvement in development efficiency, substantially lowering the entry barrier for classical engineers into quantum computing.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Quantum Software Engineering (QSE) is emerging as a critical discipline to make quantum computing accessible to a broader developer community; however, most quantum development environments still require developers to engage with low-level details across the software stack - including problem encoding, circuit construction, algorithm configuration, hardware selection, and result interpretation - making them difficult for classical software engineers to use. To bridge this gap, we present C2|Q>: a hardware-agnostic quantum software development framework that translates classical specifications (code) into quantum-executable programs while preserving methodological rigor. The framework applies modular software engineering principles by classifying the workflow into three core modules: an encoder that classifies problems, produces Quantum-Compatible Formats (QCFs), and constructs quantum circuits, a deployment module that generates circuits and recommends hardware based on fidelity, runtime, and cost, and a decoder that interprets quantum outputs into classical solutions. In evaluation, the encoder module achieved a 93.8% completion rate, the hardware recommendation module consistently selected the appropriate quantum devices for workloads scaling up to 56 qubits, and the full C2|Q>: workflow successfully processed classical specifications (434 Python snippets and 100 JSON inputs) with completion rates of 93.8% and 100%, respectively. For case study problems executed on publicly available NISQ hardware, C2|Q>: reduced the required implementation effort by nearly 40X compared to manual implementations using low-level quantum software development kits (SDKs), with empirical runs limited to small- and medium-sized instances consistent with current NISQ capabilities. The open-source implementation of C2|Q>: is available at https://github.com/C2-Q/C2Q

Problem

• Bridging classical and quantum software development for broader accessibility
• Reducing low-level implementation complexity for classical software engineers
• Automating quantum workflow from specification to hardware execution

Innovation

• Framework translates classical code to quantum programs
• Modular workflow with encoder, deployment, and decoder modules
• Hardware-agnostic system recommends quantum devices automatically