Taming the Quantum Ghost: Automated Flakiness Detection in Quantum Software

Author: Denis Avetisyan

As quantum software grows in complexity, researchers have developed an automated pipeline to identify and diagnose the root causes of unreliable, or ‘flaky’, tests.

An automated pipeline systematically identifies and diagnoses quantum errors, dissecting the origins of system instability to pinpoint the root causes of computational failure.

This work presents a novel system leveraging large language models to analyze code and issue reports, improving the accuracy of flaky test detection and root-cause analysis in quantum software engineering.

Despite the growing reliance on automated testing in quantum software development, the inherent probabilistic nature of quantum computations introduces a unique challenge: flaky tests that yield inconsistent results without code changes. This paper, ‘Automating Detection and Root-Cause Analysis of Flaky Tests in Quantum Software’, presents an automated pipeline leveraging Large Language Models (LLMs) to detect and diagnose these elusive errors by analyzing issue reports, pull requests, and code context. Our results demonstrate that LLMs, particularly Google Gemini, can achieve high accuracy-with F1-scores up to 0.9643 for root-cause identification-in triaging flaky reports and understanding their underlying causes. Will this automated approach pave the way for more robust and reliable quantum software engineering workflows?

Breaking the Context Barrier: The Limits of Scale

Large Language Models (LLMs) exhibit an impressive capacity for generating human-quality text and deciphering nuanced language patterns, yet this proficiency is notably limited by the constraints of their Context Window. This window defines the amount of text an LLM can consider at any given time when processing information or formulating a response. While LLMs excel at tasks requiring localized understanding, their performance diminishes when dealing with longer documents, intricate narratives, or problems demanding extensive background knowledge – effectively creating a bottleneck in their reasoning abilities. The finite size of this window forces the model to either truncate information, losing crucial context, or rely on simplified representations, hindering its capacity for comprehensive analysis and ultimately restricting the scope of problems it can effectively address. Consequently, despite ongoing advancements, the Context Window remains a fundamental challenge in realizing the full potential of these powerful models.

The practical application of Large Language Models faces a significant hurdle: a limited capacity for comprehensive information processing. This constraint isn’t merely about handling lengthy documents; it fundamentally impacts the ability to engage in complex thought. When presented with extended narratives or intricate problems, LLMs struggle to maintain coherence and relevance across the entire input, often losing track of crucial details introduced earlier. Similarly, effectively leveraging vast stores of background knowledge proves difficult, as the model cannot simultaneously consider both the query and a sufficiently broad contextual understanding. Consequently, performance degrades on tasks demanding sustained reasoning, nuanced comprehension, or the integration of disparate pieces of information – highlighting a critical limitation that hinders the realization of truly intelligent language processing.

Increasing the context window – the amount of text an LLM can consider at once – presents formidable scaling challenges that go beyond simply adding more computational power. Traditional transformer architectures, upon which most LLMs are built, exhibit quadratic growth in computational cost and memory usage as the context window expands; effectively, doubling the context length quadruples the resources required. This limitation drives research into innovative approaches like sparse attention mechanisms, which selectively focus on the most relevant parts of the input, and recurrent architectures designed to process information sequentially, reducing the need to store the entire context at once. Furthermore, techniques such as retrieval-augmented generation, where the LLM dynamically accesses external knowledge sources, offer a pathway to circumvent context length limitations by providing relevant information on demand, rather than embedding it all within the input. These architectural explorations are crucial for realizing the full potential of LLMs, enabling them to tackle increasingly complex tasks that demand a broader understanding of information.

Beyond the Static Core: Augmenting Intelligence with RAG

Retrieval-Augmented Generation (RAG) addresses limitations in Large Language Model (LLM) knowledge by incorporating external data sources. This is achieved by retrieving relevant information from sources like Knowledge Graphs, databases, or document collections prior to response generation. The retrieved content is then provided as context to the LLM, effectively supplementing its pre-trained parameters with current or specific information. This integration allows LLMs to access and utilize data beyond their initial training corpus, improving response accuracy, reducing reliance on potentially outdated or incomplete internal knowledge, and enabling applications requiring up-to-date or domain-specific information.

Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) performance by supplementing the LLM’s pre-trained parameters with information retrieved from external knowledge sources. This process circumvents the limitations of solely relying on the LLM’s static knowledge, enabling it to access and incorporate up-to-date or domain-specific data during response generation. By grounding responses in retrieved evidence, RAG minimizes factual inaccuracies and hallucinations, resulting in outputs that are both more informed and demonstrably attributable to verifiable sources. The retrieved context effectively functions as a dynamic extension of the LLM’s knowledge base, improving the relevance, accuracy, and overall quality of generated text.

Large Language Models (LLMs) are constrained by a finite context window – a maximum input size that limits the amount of information they can process at once. This restricts their ability to handle tasks requiring extensive background knowledge or lengthy documents. Retrieval-Augmented Generation (RAG) addresses this limitation by dynamically retrieving relevant information from external sources before generating a response. Instead of relying solely on the LLM’s pre-trained parameters, RAG supplements the input with retrieved data, effectively expanding the accessible knowledge base beyond the confines of the context window. This enables LLMs to perform more complex reasoning, answer questions requiring broad context, and process information exceeding their inherent input limitations, without requiring retraining of the model itself.

Deconstructing Thought: Mechanisms for Enhanced Reasoning

The attention mechanism is a core component of large language models (LLMs) that allows the model to weigh the importance of different tokens within the input sequence when generating output. This is achieved by calculating attention weights, which determine how much focus the model gives to each token. Critically, LLMs process input tokens in parallel, losing inherent sequential information; therefore, positional encoding is employed to inject information about the position of each token in the sequence. Positional encodings are added to the token embeddings, providing the model with a sense of order. Without attention, the model would treat all input tokens equally, and without positional encoding, it would be unable to distinguish between different orderings of the same tokens, significantly hindering its ability to understand and generate coherent text.

Chain-of-Thought (CoT) prompting is a technique that improves large language model (LLM) reasoning capabilities by explicitly requesting step-by-step explanations before providing a final answer. Instead of directly asking for a solution, prompts are engineered to elicit intermediate reasoning steps, effectively decomposing complex problems into smaller, more manageable sub-problems. This process not only increases the accuracy of responses, particularly in tasks requiring multi-hop reasoning or arithmetic, but also enhances the interpretability of the model’s decision-making process by revealing the logical sequence leading to the final output. Evaluations demonstrate CoT prompting consistently outperforms standard prompting methods on benchmarks such as arithmetic reasoning, commonsense reasoning, and symbolic manipulation.

Instruction tuning is a supervised fine-tuning process where a pre-trained Large Language Model (LLM) is further trained on a dataset of instruction-response pairs. This process optimizes the model’s ability to interpret and execute diverse instructions, moving beyond simple text completion. Datasets for instruction tuning are constructed by providing the model with a natural language instruction and the desired corresponding output; examples include question answering, summarization, and code generation. The resulting model demonstrates improved generalization capabilities, performing more reliably on unseen tasks and instructions compared to models trained solely on next-token prediction. Performance is evaluated using metrics like accuracy, ROUGE scores for text generation, and pass@k for code generation, quantifying the model’s ability to correctly follow instructions and produce desired outputs.

The Illusion of Knowledge: Towards Reliable and Efficient LLMs

Large language models, despite their impressive abilities, are prone to a significant issue known as hallucination – the generation of information that is factually incorrect or lacks coherent meaning. This isn’t simply a matter of occasional errors; hallucination represents a fundamental challenge to the reliability of these models, potentially leading to the dissemination of misinformation or flawed decision-making. The tendency to confidently present fabricated information stems from the models’ predictive nature; they are trained to generate text that sounds plausible, not necessarily text that is true. Addressing this requires sophisticated techniques to ground the models in verifiable knowledge and to calibrate their confidence levels, ensuring that outputs reflect the actual likelihood of their accuracy – a critical step towards trustworthy artificial intelligence.

A fundamental challenge with large language models lies in ensuring their stated confidence levels accurately reflect their actual correctness; this is the core of model calibration. Often, models express high probabilities for incorrect answers, misleading users into trusting flawed information. Effective calibration techniques adjust the model’s output probabilities to better align with observed accuracy, providing a more reliable measure of trustworthiness. This alignment is not merely about boosting numbers; it fundamentally improves the utility of LLMs in critical applications, such as medical diagnosis or financial forecasting, where miscalibration could have serious consequences. By refining how models express uncertainty, researchers aim to build systems that are not only powerful but also transparent and dependable, fostering greater user trust and enabling safer deployment in real-world scenarios.

Recent research has focused on improving the reliability of Large Language Models (LLMs) through rigorous testing and evaluation, particularly within the complex domain of quantum computing. A significant advancement involved the expansion of a quantum flaky test dataset by 54%, bringing the total to 71 distinct tests designed to expose unpredictable behavior. Utilizing this expanded dataset, LLMs demonstrated a strong capacity for classifying these flaky test reports, with the gemini-2.5-flash model achieving a peak F1-score of 0.9420. This performance indicates a promising ability to automatically diagnose and potentially mitigate issues in quantum systems. Further validation came with the gpt-4o model, which attained an accuracy of 0.8592 on related tasks even when provided with limited contextual information, highlighting the potential for efficient and insightful analysis with minimal input.

Parameter efficiency represents a critical advancement in large language model development, directly addressing the substantial computational demands that often hinder widespread deployment. Reducing the number of parameters within a model – without significantly sacrificing performance – translates to lower energy consumption, decreased memory requirements, and faster processing speeds. This optimization is particularly vital for applications targeting resource-constrained environments, such as mobile devices or edge computing platforms. Moreover, a focus on parameter efficiency often facilitates the effectiveness of few-shot and zero-shot learning capabilities, enabling models to generalize to new tasks with minimal or no task-specific training data, further enhancing their adaptability and utility across a broader range of applications.

The pursuit of reliable quantum software demands a constant challenging of assumptions. This work, automating the detection of flaky tests, embodies that spirit. It isn’t simply about identifying failures, but about understanding why those failures occur, even when the code appears logically sound. As Andrey Kolmogorov observed, “The errors are not in the details, they are in the concepts.” This rings true here – the pipeline doesn’t just flag inconsistent test results, it delves into the code and issue reports, leveraging Large Language Models to reverse-engineer the root cause. It’s a system designed to expose flaws in the foundational concepts guiding the software’s construction, turning potential bugs into signals of deeper, often subtle, problems.

Beyond the Surface: Where Does Quantum Flakiness Lead?

The automation of flaky test diagnosis, as demonstrated, isn’t merely about efficiency; it’s a formalized interrogation of the quantum software stack. This work doesn’t solve flakiness-that would be naive. Instead, it shifts the problem. By externalizing the pattern recognition to Large Language Models, the system exposes the inherent ambiguity within the code itself, demanding a closer look at what constitutes ‘correct’ behavior in the first place. The real challenge isn’t identifying the symptoms, but deconstructing the assumptions that allow them to manifest.

Future investigations should deliberately push the boundaries of this approach. Introducing adversarial examples-flaky tests specifically designed to evade detection-would reveal the limitations of the LLM’s reasoning. Furthermore, extending the analysis beyond individual test failures to encompass systemic patterns across an entire codebase could uncover architectural weaknesses contributing to instability. It’s a process of controlled demolition, understanding how things break to reveal their underlying structure.

Ultimately, the pursuit of robust quantum software isn’t about eliminating errors, but about creating systems that gracefully accommodate them. Flakiness, therefore, isn’t a bug to be fixed, but a signal-a persistent reminder that the quantum realm resists absolute definition. The next step isn’t better detection, but a fundamental rethinking of what constitutes ‘reliable’ computation in a probabilistic universe.

Original article: https://arxiv.org/pdf/2603.09029.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

Breaking the Context Barrier: The Limits of Scale

Beyond the Static Core: Augmenting Intelligence with RAG

Deconstructing Thought: Mechanisms for Enhanced Reasoning

The Illusion of Knowledge: Towards Reliable and Efficient LLMs

Beyond the Surface: Where Does Quantum Flakiness Lead?

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