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Enhancing quantum noise characterization via extra energy levels

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arxiv 2506.09131 v2 pith:TGS5CN6Z submitted 2025-06-10 quant-ph

Enhancing quantum noise characterization via extra energy levels

classification quant-ph
keywords noisecharacterizationquantumenergylevelsspamambiguitycomputing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Noise is a major challenge for building practical quantum computing systems. Precise characterization of quantum noise is crucial for developing effective error mitigation and correction schemes. However, state preparation and measurement (SPAM) errors on many current platforms can introduce large ambiguity into conventional noise characterization methods. In this work, we propose a scheme for enhancing quantum noise characterization using additional energy levels. We first develop a comprehensive theory on the identifiability of n-qudit SPAM noise given high-quality single-qudit control, showing the existence of gauge freedoms which can be completely described using subsystem depolarizing maps. We then show how to use these extra energy levels to reduce the gauge ambiguity in characterizing both SPAM and gate noise in the qubit subspace. We experimentally implement these ideas on a superconducting quantum computing device and demonstrate a qutrit-enabled enhancement in noise characterization precision.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Symmetries of Pauli Noise from Lindbladian Dynamics

    quant-ph 2026-07 unverdicted novelty 7.0

    Lindbladian perturbation theory reveals approximate symmetries on Pauli fidelities for Clifford gates, with only restricted off-diagonal dissipative errors breaking them at first order, enabling gauge fixing for SPAM ...

  2. Learning Mid-circuit Measurement Backaction from Three Repeated Measurements

    quant-ph 2026-05 unverdicted novelty 7.0

    Protocol learns single-qubit Z-twirled MCM instrument parameters from three repeated measurements on mixed input, yielding ~100x better Pauli-observable prediction than confusion-matrix models on IBM processors.