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Journal of Applied Physics2025Superconducting micro-resonators have application in sensors and quantum computing. Measurement of the resonator internal loss in the single-photon regime is a common tool to study the origins of dissipation, noise, and decoherence in quantum circuits, as well as characterization of materials used for quantum devices. However, such measurements are challenging and time-consuming with large uncertainties
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QIP 20252025A central challenge in quantum simulation is to prepare low-energy states of strongly interacting many-body systems. In this work, we study the problem of preparing a quantum state that optimizes a random all-to-all, sparse or dense, spin or fermionic k-local Hamiltonian. We prove that a simplified quantum Gibbs sampling algorithm achieves a Ω(1 k)-fraction approximation of the optimum, giving an exponential
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MICRO 20242024In this paper, we introduce Surf-Deformer, a code deformation framework that seamlessly integrates adaptive defect mitigation functionality into the current surface code workflow. It crafts several basic deformation instructions based on fundamental gauge transformations, which can be combined to explore a larger design space than previous methods. This enables more optimized deformation processes tailored
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TQC 20242024Given a linear system of equations Ax = b, quantum linear system solvers (QLSSs) approximately prepare a quantum state |x⟩ for which the amplitudes are proportional to the solution vector x. Asymptotically optimal QLSSs have query complexity O(κ log(1/ε)), where κ is the condition number of A, and ε is the approximation error. However, runtime guarantees for existing optimal and near-optimal QLSSs do not
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Language Resources and Evaluation2024In Artificial Intelligence research, perspectivism is an approach to machine learning that aims at leveraging data annotated by different individuals in order to model varied perspectives that influence their opinions and world view. We present the first survey of datasets and methods relevant to perspectivism in Natural Language Processing (NLP). We review datasets in which individual annotator labels
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