A proposed machine-learning update to quantum readout error matrices claims 6.6-29.9% metric improvements in simulation, but its least-squares core is degenerate because each qubit's probability vector sums to one.
Readout sweet spots for spin qubits with strong spin-orbit interaction
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abstract
Qubit readout schemes often deviate from ideal projective measurements, introducing critical issues that limit quantum computing performance. In this work, we model charge-sensing-based readout for semiconductor spin qubits in double quantum dots, and identify key error mechanisms caused by the back-action of the charge sensor. We quantify how the charge noise of the sensor, residual tunneling, and $g$-tensor modulation degrade readout fidelity, induce a mixed post-measurement state, and cause leakage from the computational subspace. For state-of-the-art systems with strong spin-orbit interaction and electrically tunable $g$-tensors, we identify a readout sweet spot, that is, a special device configuration where readout is closest to projective. Our framework provides a foundation for developing effective readout error mitigation strategies, with broad applications for optimizing readout performance for a variety of charge-sensing techniques, advancing quantum protocols, and improving adaptive circuits for error correction.
fields
quant-ph 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
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Personalized Improvement of Standard Readout Error Mitigation using Low-Depth Circuits and Machine Learning
A proposed machine-learning update to quantum readout error matrices claims 6.6-29.9% metric improvements in simulation, but its least-squares core is degenerate because each qubit's probability vector sums to one.