Proc. Int. Workshop on Quantum Computation and Learning, May 27-29, 2000, Sundbyholm

Richard Bonner, Rūsiņš Freivalds

Simon Perdrix

We improve the upper bound on the minimal resources required for measurement-only quantum computation (M A Nielsen 2003 Phys. Rev. A 308 96–100; D W Leung 2004 Int. J. Quantum Inform. 2 33; S Perdrix 2005 Int. J. Quantum Inform. 3 219–23). Minimizing the resources required for this model is a key issue for experimental realization of a quantum computer based on projective measurements. This new upper bound also allows one to reply in the negative to the open question presented by Perdrix (2004 Proc. Quantum Communication Measurement and Computing ) about the existence of a trade-off between observable and ancillary qubits in measurement-only QC.

Wei Zhang, Tianming Liu, Yingfeng Wang +4 more

Quantum Machine Learning (QML) is an emerging interdisciplinary field at the intersection of quantum computing and machine learning, delivering new era for advancing learning beyond the limitations of conventional computational systems. By leveraging quantum computing, QML introduces fundamentally different representational spaces that may support novel learning dynamics, expressive model architectures, and potential algorithmic advantages. This workshop explores how quantum-based approaches can be meaningfully integrated into modern machine learning and computer graphics pipelines. Rather than positioning quantum computing as a distant theoretical concept, the workshop frames it as an emerging computational substrate with practical relevance for hybrid architectures, quantum-enhanced models, and future learning paradigms. Meanwhile, the workshop will also examine near-term quantum hardware, deployable system design, and rigorous evaluation methods for distinguishing genuine quantum advantage from strong classical baselines. By bringing together researchers from quantum computing, machine learning, and computer graphics, this workshop aims to bridge theory and practice, identify near-term application opportunities, and foster interdisciplinary discussion on the future of scalable and practical QML.

James Taylor Lewis Grimmelmann

Richard Bonner, Rūsiņš Freivalds

Quantum Computation and Learning : Third International Workshop, QCL 2002Riga, Latvia, Revised Proceedings