Two matched-filter algorithms for neutral-atom qubit readout reduce measurement errors by 32-43% versus Gaussian thresholds and use 100x fewer parameters than CNNs while remaining scalable.
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4 Pith papers cite this work. Polarity classification is still indexing.
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quant-ph 4years
2025 4representative citing papers
A framework is presented for designing robust and precise effective Hamiltonians by identifying the minimal toggling-frame subspace and the complete set of achievable zeroth-order terms.
Reservoir computing using polynomial features from measurement signals achieves up to 50% error reduction on single-qubit and 11% on five-qubit datasets with 100x fewer multiplications than neural networks while reducing crosstalk.
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
citing papers explorer
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Efficient measurement of neutral-atom qubits with matched filters
Two matched-filter algorithms for neutral-atom qubit readout reduce measurement errors by 32-43% versus Gaussian thresholds and use 100x fewer parameters than CNNs while remaining scalable.
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Engineering Precise and Robust Effective Hamiltonians
A framework is presented for designing robust and precise effective Hamiltonians by identifying the minimal toggling-frame subspace and the complete set of achievable zeroth-order terms.
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Superconducting Qubit Readout Using Next-Generation Reservoir Computing
Reservoir computing using polynomial features from measurement signals achieves up to 50% error reduction on single-qubit and 11% on five-qubit datasets with 100x fewer multiplications than neural networks while reducing crosstalk.
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Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.