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

TL;DR

This paper tackles the challenge of making quantum software development accessible to classical programmers by introducing C2|Q>, a hardware-agnostic encoder–deployment–decoder framework that translates classical problem specifications into quantum workloads. The approach uses a modular pipeline: an encoder parses inputs into Quantum-Compatible Formats (QCFs) and selects algorithms; a deployment module transposes circuits to hardware, estimates fidelity/time/cost, and recommends devices; and a decoder maps quantum outputs back to classical solutions. Across three experiments, the framework achieves high encoder accuracy ($F_1=0.982$) and end-to-end completion rates of $93.8\%$ (Python) and $100\%$ (JSON), while delivering approximately a $40\times$ reduction in handwritten code compared with manual Qiskit implementations. The results testify to the framework’s generalisability across problem types and devices, its potential to reduce development effort, and its open-source availability, offering a practical path toward cross-platform quantum software engineering on current NISQ hardware.

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

Figures (8)

• Figure 1: A quantum circuit that prepares a Bell state.
• Figure 2: C2$\ket{\rm{Q}}$: pipeline with encoder–deployment–decoder architecture.
• Figure 3: Architecture of Parser.
• Figure 4: Topology of the Helmi quantum computer represented by the coupling map coupling_map = [(0, 3), (1, 3), (2, 3), (3, 4)].
• Figure 5: Comparison of supported devices under QAOA workloads as a function of problem size in qubits: (a) projected error rates, (b) execution time, and (c) execution cost. Together, these metrics demonstrate the trade-offs that guide hardware recommendation.