Code assistants for quantum computing operate exclusively on text, disregarding visual representations (circuit diagrams, Bloch spheres, and histograms) that permeate the domain’s documentation. This work is presented as a resource-and-pilot study with three contributions: (1) the first public multimodal dataset for Qiskit-oriented quantum programming, containing 8,366 samples (45% with images) across function completion, code generation, and question answering tasks, released under Apache 2.0; (2) a reproducible synthetic generation pipeline that extracts content from public Qiskit documentation, transcribes images via a multimodal model, generates question-answer pairs, and validates code through automatically generated unit tests; and (3) a feasibility study of parameter-efficient VLM specialization, in which rsLoRA (rank 32, two epochs) on Qwen3-VL-8B-Instruct is retained as the best configuration under a controlled method-selection comparison of five LoRA variants. On the text-only external benchmarks Qiskit HumanEval and Qiskit HumanEval Hard, which measure quantum-code specialization rather than visual understanding, the fine-tuned model reaches 41.72% and 30.46% Pass@1 respectively, above the IBM-specialized Qwen2.5-Coder-14B-Qiskit (40.40% / 25.83%) and granite-3.3-8b-qiskit (17.88% / 9.93%), and + 17.22 / + 15.89 pp over the Qwen3-VL-8B-Instruct base. Visual capability is assessed on the synthetic multimodal test split, where multimodal samples reach 64.29% Pass@1 versus 46.33% on text-only samples ( + 17.96 pp); a matched training ablation that withholds the multimodal portion of the training set localizes + 6.25 pp of that gap to the multimodal training signal. The dataset, models, and code are available under the Apache 2.0 license.
Code assistants for quantum computing operate exclusively on text, disregarding visual representations (circuit diagrams, Bloch spheres, and histograms) that permeate the domain’s documentation. This work is presented as a resource-and-pilot study with three contributions: (1) the first public multimodal dataset for Qiskit-oriented quantum programming, containing 8,366 samples (45% with images) across function completion, code generation, and question answering tasks, released under Apache 2.0; (2) a reproducible synthetic generation pipeline that extracts content from public Qiskit documentation, transcribes images via a multimodal model, generates question-answer pairs, and validates code through automatically generated unit tests; and (3) a feasibility study of parameter-efficient VLM specialization, in which rsLoRA (rank 32, two epochs) on Qwen3-VL-8B-Instruct is retained as the best configuration under a controlled method-selection comparison of five LoRA variants. On the text-only external benchmarks Qiskit HumanEval and Qiskit HumanEval Hard, which measure quantum-code specialization rather than visual understanding, the fine-tuned model reaches 41.72% and 30.46% Pass@1 respectively, above the IBM-specialized Qwen2.5-Coder-14B-Qiskit (40.40% / 25.83%) and granite-3.3-8b-qiskit (17.88% / 9.93%), and + 17.22 / + 15.89 pp over the Qwen3-VL-8B-Instruct base. Visual capability is assessed on the synthetic multimodal test split, where multimodal samples reach 64.29% Pass@1 versus 46.33% on text-only samples ( + 17.96 pp); a matched training ablation that withholds the multimodal portion of the training set localizes + 6.25 pp of that gap to the multimodal training signal. The dataset, models, and code are available under the Apache 2.0 license.
Code Large Language Models (Code LLMs) have emerged as powerful tools, revolutionizing the software development landscape by automating the coding process and reducing time and effort required to build applications. This paper focuses on training Code LLMs to specialize in the field of quantum computing. We begin by discussing the unique needs of quantum computing programming, which differ significantly from classical programming approaches or languages. A Code LLM specializing in quantum computing requires a foundational understanding of quantum computing and quantum information theory. However, the scarcity of available quantum code examples and the rapidly evolving field, which necessitates continuous dataset updates, present significant challenges. Moreover, we discuss our work on training Code LLMs to produce high-quality quantum code using the Qiskit library. This work includes an examination of the various aspects of the LLMs used for training and the specific training conditions, as well as the results obtained with our current models. To evaluate our models, we have developed a custom benchmark, similar to HumanEval, which includes a set of tests specifically designed for the field of quantum computing programming using Qiskit. Our findings indicate that our model outperforms existing state-of-the-art models in quantum computing tasks. We also provide examples of code suggestions, comparing our model to other relevant code LLMs. Finally, we introduce a discussion on the potential benefits of Code LLMs for quantum computing computational scientists, researchers, and practitioners. We also explore various features and future work that could be relevant in this context.
Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.
Recent advances in large language models (LLMs) have enabled the automation of an increasing number of programming tasks, including code generation for scientific and engineering domains. In rapidly evolving software ecosystems such as quantum software development, where frameworks expose complex abstractions, a central question is how best to incorporate domain knowledge into LLM-based assistants while preserving maintainability as libraries evolve.
In this work, we study specialization strategies for Qiskit code generation using the Qiskit-HumanEval benchmark. We compare a parameter-specialized fine-tuned baseline introduced in prior work against a range of recent general-purpose LLMs enhanced with retrieval-augmented generation (RAG) and agent-based inference with execution feedback.
Our results show that modern general-purpose LLMs consistently outperform the parameter-specialized baseline. While the fine-tuned model achieves approximately 47% pass@1 on Qiskit-HumanEval, recent general-purpose models reach 60-65% under zero-shot and retrieval-augmented settings, and up to 85% for the strongest evaluated model when combined with iterative execution-feedback agents -representing an improvement of more than 20% over zero-shot general-purpose performance and more than 35% over the parameter-specialized baseline.
Agentic execution feedback yields the most consistent improvements, albeit at increased runtime cost, while RAG provides modest and model-dependent gains. These findings indicate that performance gains can be achieved without domain-specific fine-tuning, instead relying on inference-time augmentation, thereby enabling a more flexible and maintainable approach to LLM-assisted quantum software development.
Can AI agents visually comprehend quantum circuit diagrams and generate verified executable code--and at what cost? We present Quantum Circuit Vision, a cost-aware evaluation framework for multimodal AI agents on quantum circuit visual understanding. We construct a 132-circuit benchmark spanning 13 categories (1--10 qubits) with executable Amazon Braket code and unitary-fidelity verification. Evaluating three frontier Claude-family models at different capability-cost tiers with n=5 repeated trials, we find that the mid-tier model (Sonnet 4.6, 1.30× credits) offers the most favorable balance on the cost-accuracy frontier: 91% pass rate on the core subset at 18% of the per-call cost of the strongest model (Opus 4.6), whose accuracy advantage is not statistically significant (paired t: p=0.083). Logistic regression confirms that circuit depth--not qubit count--is the primary predictor of failure (p<0.001). Chain-of-thought prompting shows no statistically significant effect (all p>0.18, n=5), suggesting that visual pattern recognition outweighs explicit reasoning strategy for structurally coupled diagrams. We propose a cascade routing strategy (cheap → expensive models) that achieves 84% accuracy at 38% of single-model cost, demonstrating that model routing dominates prompt engineering as a cost lever. We release QCV-Dataset (132 circuits, 5 modalities, 1,931 files) on Hugging Face Hub as an open evaluation infrastructure with structured metadata for discoverability, interoperability, and responsible AI documentation, and all evaluation code, cost logs, and verification scripts on GitHub for full reproducibility.
Quantum programs are typically developed using quantum Software Development Kits (SDKs). The rapid advancement of quantum computing necessitates new tools to streamline this development process, and one such tool could be Generative Artificial intelligence (GenAI). In this study, we introduce and use the Qiskit HumanEval dataset, a hand-curated collection of tasks designed to benchmark the ability of Large Language Models (LLMs) to produce quantum code using Qiskit – a quantum SDK. This dataset consists of more than 100 quantum computing tasks, each accompanied by a prompt, a canonical solution, a comprehensive test case, and a difficulty scale to evaluate the correctness of the generated solutions. We systematically assess the performance of a set of LLMs against the Qiskit HumanEval dataset's tasks and focus on the models ability in producing executable quantum code. Our findings not only demonstrate the feasibility of using LLMs for generating quantum code but also establish a new benchmark for ongoing advancements in the field and encourage further exploration and development of GenAI-driven tools for quantum code generation.
As quantum computing moves from the noisy intermediate-scale quantum (NISQ) era toward early fault-tolerant quantum computing (EFTQC), quantum-enhanced AI is shifting from a conceptual idea to experimental research. Quantum components are expected to extend the capabilities of existing AI systems, yet it remains unclear how such components will help within large multimodal models. In this study, we try to answer that question for vision-language models (VLMs) using classically simulated quantum layers. With LLaVA-OneVision as the backbone, we evaluate 4- and 8-qubit basic-ring and strongly entangling circuits at four insertion and four replacement locations. Insertion adds quantum layers to the classical model, while replacement substitutes them for classical components. Using the same setup, we compare performance and computational cost between classical and quantum-classical hybrid models. On the Visual Spatial Reasoning dataset, inserting a quantum layer after the visual projector reaches 80.7% development accuracy, 2.28 percentage-points above classical full fine-tuning. Replacing the final decoder feed-forward sublayer retains 79.22% accuracy with 13.07 million fewer trainable parameters. Quantum insertion can thus improve accuracy, and selective replacement can reduce parameters. These benefits depend strongly on where the quantum layer is inserted or which classical component it replaces: earlier replacements lower accuracy, and fewer parameters do not reliably shorten training or reduce computation. To assess potential security benefits, we evaluated the robustness of quantum-classical hybrid models under six attack settings. Overall, our study maps where quantum layers help and hurt within VLMs, offering a starting point for hybrid VLM design; whether these gains generalize remains open. The code is available at https://github.com/TITANS-Lab-Artifacts/QuantumVLM.