Machine Learning for Reliable Quantum Computing
Full title: Machine Learning for Reliable Quantum Computing: An Algorithm–Hardware Co-Design Perspective
Summary: Reliable quantum computing requires co-design across the full stack, including algorithm structure, compilation, and execution, rather than isolated optimization of any single layer. This talk presents a unified framework in which these layers are jointly optimized under realistic hardware constraints. Structure-aware compilation exploits algorithmic interaction graphs to reduce circuit depth; joint optimization across mapping, term ordering, and algorithm parameters yields significantly improved success probability; and execution-layer analysis reveals that error mitigation effectiveness depends critically on circuit symmetry and native gate choice. While demonstrated on superconducting quantum processors, the underlying co-design principles are potentially transferable across architectures. Building on this foundation, the talk highlights machine learning as the key enabler for closing the feedback loop: data-efficient, parameterized noise models learned directly from workload execution data achieve substantially improved predictive fidelity over vendor models without the overhead of dedicated characterization. These insights situate the co-design perspective within the emerging framework of quantum deep learning. In this broader setting, the talk also presents an operational definition, a unifying taxonomy, and a benchmarking protocol grounded in explicit resource contracts.
Full title: Machine Learning for Reliable Quantum Computing: An Algorithm–Hardware Co-Design Perspective
Summary: Reliable quantum computing requires co-design across the full stack, including algorithm structure, compilation, and execution, rather than isolated optimization of any single layer. This talk presents a unified framework in which these layers are jointly optimized under realistic hardware constraints. Structure-aware compilation exploits algorithmic interaction graphs to reduce circuit depth; joint optimization across mapping, term ordering, and algorithm parameters yields significantly improved success probability; and execution-layer analysis reveals that error mitigation effectiveness depends critically on circuit symmetry and native gate choice. While demonstrated on superconducting quantum processors, the underlying co-design principles are potentially transferable across architectures. Building on this foundation, the talk highlights machine learning as the key enabler for closing the feedback loop: data-efficient, parameterized noise models learned directly from workload execution data achieve substantially improved predictive fidelity over vendor models without the overhead of dedicated characterization. These insights situate the co-design perspective within the emerging framework of quantum deep learning. In this broader setting, the talk also presents an operational definition, a unifying taxonomy, and a benchmarking protocol grounded in explicit resource contracts.
Speaker:Dr. Yanjun Ji has an interdisciplinary research background in Physics and Computer Science. In 2020, she completed my Master's degree in Theoretical Physics at Saarland University. In 2024, she earned her Ph.D. in Computer Science at the University of Stuttgart. She is currently a postdoctoral researcher at Forschungszentrum Jülich. She is committed to interdisciplinary research at the cutting-edge convergence of quantum computing and artificial intelligence. Her research encompasses both foundational theoretical investigations and the development of practical applications, aiming to serve society and benefit humanity.
Moderators:
Dr. Pawel Gora, CEO of Quantum AI Foundation Quantum AI Foundation
Dr. Sebastian Zajac, member of QPoland QPoland - QWorld
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Highlights
- 2 hours
- Online