LIMINAL fits nested Lindblad models to tomographic data and uses likelihood-ratio tests to identify minimal dynamics for a five-qubit superconducting processor, supporting three-local Hamiltonian terms and two-local dissipation but not three-local dissipation.
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Soft decoding with analog measurement data raises repetition-code thresholds by 25% and reduces error rates up to 30x on superconducting qubits, with one byte per shot sufficient for near-optimal performance.
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Learning Lindblad Dynamics of a Superconducting Quantum Processor
LIMINAL fits nested Lindblad models to tomographic data and uses likelihood-ratio tests to identify minimal dynamics for a five-qubit superconducting processor, supporting three-local Hamiltonian terms and two-local dissipation but not three-local dissipation.
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Soft information decoding with superconducting qubits
Soft decoding with analog measurement data raises repetition-code thresholds by 25% and reduces error rates up to 30x on superconducting qubits, with one byte per shot sufficient for near-optimal performance.