Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements
We present Lifting, a QSD framework that addresses both challenges through repetition encoding, neural decoding, and in situ calibration.
ProofPaper ↗
Key points
- Quantum computing promises new capabilities beyond classical computation.
- Mid-circuit measurements (MCMs) enable error correction and outcome-dependent control, but their reliability is limited by noise on superconducting hardware.
- These operations depend on accurate quantum state discrimination (QSD), which assigns measured signals to state labels.
- Encoding supplies joint in-phase and quadrature (I-Q) readout signals from the target and auxiliary qubits, while contextual calibration trains the decoder for the conditions encountered during execution.
Sources (1)
- [1]Lifting: Quantum State Discrimination Framework for Mid-Circuit MeasurementsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 02:43 PM
We present Lifting, a QSD framework that addresses both challenges through repetition encoding, neural decoding, and in situ calibration.
Quantum computing promises new capabilities beyond classical computation.
Extractive summary: sentences quoted from the sources.
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