Q-BRIDGE, a graph-transformer denoiser conditioned on backend features, reconstructs ideal outcome distributions from noisy quantum executions and markedly improves oracle-based bug detection.
Quantum Software Engineering: Roadmap and Challenges Ahead
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abstract
As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective -key qualities of any industry-grade software-mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyse in depth the current state of quantum software engineering research. From this analysis, the key areas of quantum software engineering are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing Quantum Software Engineering.
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2026 1verdicts
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Backend-Aware Graph Learning for Denoising Outcome Distributions in Quantum Program Testing
Q-BRIDGE, a graph-transformer denoiser conditioned on backend features, reconstructs ideal outcome distributions from noisy quantum executions and markedly improves oracle-based bug detection.