aqora

QMCTorch: a PyTorch Implementation of Real-Space Quantum Monte Carlo Simulations of Molecular Systems

Nicolas Renaud

Renaud, N., (2023). QMCTorch: a PyTorch Implementation of Real-Space Quantum Monte Carlo Simulations of Molecular Systems. Journal of Open Source Software, 8(91), 5472, https://doi.org/10.21105/joss.05472

The Journal of Open Source SoftwareNov 21, 20230 citations

State-Averaged Orbital-Optimized VQE: A quantum algorithm for the democratic description of ground and excited electronic states

Martin Beseda, Silvie Illésová, Saad Yalouz +1 more

Beseda et al., (2024). State-Averaged Orbital-Optimized VQE: A quantum algorithm for the democratic description of ground and excited electronic states. Journal of Open Source Software, 9(101), 6036, https://doi.org/10.21105/joss.06036

arXiv:2401.11884The Journal of Open Source SoftwareSep 26, 20244 citations

The Walrus: a library for the calculation of hafnians, Hermite polynomials and Gaussian boson sampling

Brajesh Gupt, Josh Izaac, Nicolás Quesada

Gupt et al., (2019). The Walrus: a library for the calculation of hafnians, Hermite polynomials and Gaussian boson sampling. Journal of Open Source Software, 4(44), 1705, https://doi.org/10.21105/joss.01705

The Journal of Open Source SoftwareDec 19, 201974 citations

Proceedings of QCS 2022: Third International Workshop on Quantum Computing Software

IEEE/ACM International Workshop on Quantum Computing Software (QCS)Nov 1, 20220 citations

QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits

Zhenxiao Fu, Lei Jiang, Fan Chen

Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, with performance constrained by noise. Addressing this limitation requires hardware-facing capabilities beyond gate sequences: mid-circuit measurement and classical feedback for quantum error correction (QEC), precise timing for dynamical decoupling (DD), and pulse-level waveform access for calibration. OpenQASM 3 exposes these capabilities through a hardware-level programming interface. Despite rapid progress in large language models (LLMs) for code generation, datasets targeting these advanced features remain lacking. We introduce QASM-Eval, the first comprehensive dataset to train and evaluate LLMs on OpenQASM 3, targeting completion of hardware-facing constructs within a supplied program context. QASM-Eval comprises an expert-curated test set of 1,200 tasks and a training set of over 4,000 tasks, covering classical logic, timing scheduling, pulse control, and complex tasks inspired by quantum-control workflows. An extended verifier automatically checks syntax, quantum measurement distributions, and program timelines. Our evaluation identifies substantial headroom for OpenQASM 3 code generation and significant gains from targeted fine-tuning. Fine-tuned Llama-3-8B outperforms zero-shot GPT-5.6-Terra, while fine-tuned Llama-3-70B achieves 61.17% overall pass@1 and approaches few-shot-augmented GPT-5.6-Terra. QASM-Eval provides a benchmark and training resource for specification-guided code generation over the hardware-facing constructs of OpenQASM 3, supporting the development of LLM assistants for quantum programming. Data and code: https://github.com/fuzhenxiao/QASM-EvalQuantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, with performance constrained by noise. Addressing this limitation requires hardware-facing capabilities beyond gate sequences: mid-circuit measurement and classical feedback for quantum error correction (QEC), precise timing for dynamical decoupling (DD), and pulse-level waveform access for calibration. OpenQASM 3 exposes these capabilities through a hardware-level programming interface. Despite rapid progress in large language models (LLMs) for code generation, datasets targeting these advanced features remain lacking. We introduce QASM-Eval, the first comprehensive dataset to train and evaluate LLMs on OpenQASM 3, targeting completion of hardware-facing constructs within a supplied program context. QASM-Eval comprises an expert-curated test set of 1,200 tasks and a training set of over 4,000 tasks, covering classical logic, timing scheduling, pulse control, and complex tasks inspired by quantum-control workflows. An extended verifier automatically checks syntax, quantum measurement distributions, and program timelines. Our evaluation identifies substantial headroom for OpenQASM 3 code generation and significant gains from targeted fine-tuning. Fine-tuned Llama-3-8B outperforms zero-shot GPT-5.6-Terra, while fine-tuned Llama-3-70B achieves 61.17% overall pass@1 and approaches few-shot-augmented GPT-5.6-Terra. QASM-Eval provides a benchmark and training resource for specification-guided code generation over the hardware-facing constructs of OpenQASM 3, supporting the development of LLM assistants for quantum programming. Data and code: https://github.com/fuzhenxiao/QASM-Eval

cs.LGCross-listedarXiv:2605.30358Apr 30, 20260 citations

Compiling Quantum Algorithms for Architectures with Multi-qubit Gates (Open Access, Publisher's Version)

Esteban A. Martinez, Thomas Monz, Daniel Nigg +2 more

Jun 24, 20160 citations

Enabling the Verification and Formalization of Hybrid Quantum-Classical Computing with OpenQASM 3.0 compatible QASM-TS 2.0

Abstract

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Enabling the Verification and Formalization of Hybrid Quantum-Classical Computing with OpenQASM 3.0 compatible QASM-TS 2.0

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