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to algorithms, and cross-platform co-design across superconducting, neutral atom, and diamond-based systems, guided by quantitative resource estimates targeting DOE-priority scientific applications. Position
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platforms and models to improve upon existing error correcting codes, while leveraging platform-specific physics knowledge, be it in ultracold neutral atoms, trapped ions or superconducting qubits. This may
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in ultracold neutral atoms, trapped ions or superconducting qubits. This may also involve investigation of the decoherence processes and related question of persistent quantum entanglement. Essential
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team focusing on developing the next generation of machine learning driven approaches to analyzing scattering data on quantum materials. The BNL-lead part of the project will focus on new ways to extract