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Scientists led by TRIUMF<\/a> and the Perimeter Institute for Theoretical Physics<\/a>, in collaboration with D-Wave Quantum Inc.<\/a>, have published research in npj Quantum Information<\/a>. This research combines quantum annealing technology with generative AI to address particle physics simulation bottlenecks for CERN\u2019s Large Hadron Collider (LHC)<\/a> upgrades.<\/p>\n The team developed a quantum-AI hybrid approach aimed at enhancing particle collision simulations in terms of speed, accuracy, and computational efficiency. This method utilizes D-Wave\u2019s annealing quantum computing technology to generate synthetic data for analyzing particle collisions. The approach involves manipulating qubits to condition the processor, which helps generate particle showers with specific desired properties. The research specifically addresses the computational intensity of simulating calorimeter data from LHC experiments, which is projected to create a bottleneck in future data analysis.<\/p>\n If scalable, this framework has implications beyond particle physics, including applications in synthetic data generation for sectors such as finance, healthcare, and manufacturing. The work highlights that quantum processors, such as those from D-Wave, maintain constant energy consumption regardless of workload size, a characteristic that differentiates them from classical GPUs which exhibit increased energy use with workload. This research demonstrates a method for addressing scientific computational bottlenecks by combining quantum and AI technologies. Contributions to this published research also came from the National Research Council of Canada (NRC)<\/a>, the University of British Columbia<\/a>, and the University of Virginia<\/a>.<\/p>\n Read the announcement from Newswise here<\/a>, the Perimeter Institute article here<\/a>, and the paper in npj Quantum Information here<\/a>.<\/p>\n July 11, 2025<\/p>\n
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