Insider Brief NVIDIA said Europe is expanding its quantum-GPU computing infrastructure as part of a broader rollout of 35 new artificial intelligence supercomputers across the continent, a move aimed at strengthening the region’s capabilities in quantum research, AI development and scientific computing. The announcements, made ahead of the ISC High Performance conference in Hamburg, highlight […]

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Europe Expands Quantum-GPU Computing as NVIDIA Announces 35 New AI Supercomputers

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A generalised two-qubit Hamiltonian-based projective quantum feature map efficiently encodes classical data using both local and pairwise qubit interactions, as presented by Rafael Simões do Carmo and colleagues at São Paulo State University and Hospital Israelita Albert Einstein. The approach increases information density within quantum circuits while accommodating current hardware limitations and has been implemented in the open-source Python library, pqfmlib. Benchmarking across four biomedical datasets, utilising both IBM quantum processors and statevector simulations, demonstrates that this generalised Hamiltonian family consistently outperforms classical baselines, suggesting a key pathway towards achieving practical quantum advantage in the near term. Enhanced quantum advantage via axis-resolved encoding in biomedical data analysis Statistical gains over classical baselines reached 156 qubits, with a consistent pattern observed across four biomedical datasets. Achieving statistical quantum advantage previously required fewer qubits and was limited to narrow applications, but this generalised Hamiltonian family expands the scope of potential utility. Efficiently encoding classical data, the generalised two-qubit Hamiltonian-based projected quantum feature map embeds variables along different axes within a single qubit, maximising information density. Implementation within the open-source library, pqfmlib, enables further research and development in this rapidly evolving field. More data can be processed in shallower circuits via axis-resolved encoding, reducing susceptibility to errors inherent in current quantum hardware. A demonstration revealed that the generalised two-qubit Hamiltonian family consistently outperformed reference projected quantum feature maps across the four biomedical datasets, achieving statistical gains when utilising up to 156 qubits on IBM quantum processors, alongside statevector simulations for comparison. Further investigation is ne

The Future of Quantum Computing

Europe Expands Quantum-GPU Computing as NVIDIA Announces 35 New AI Supercomputers

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Europe Expands Quantum-GPU Computing as NVIDIA Announces 35 New AI Supercomputers

Researchers Develop New Quantum Feature Map for Machine Learning

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