REVIEW 2 cited by
Enhancing quantum noise characterization via extra energy levels
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Enhancing quantum noise characterization via extra energy levels
read the original abstract
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.
Forward citations
Cited by 2 Pith papers
-
Symmetries of Pauli Noise from Lindbladian Dynamics
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 ...
-
Learning Mid-circuit Measurement Backaction from Three Repeated Measurements
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.