Noisy Probabilistic Error Cancellation and Generalized Physical Implementability
Tian-Ren Jin, Kai Xu, Yu-Ran Zhang, Heng Fan

TL;DR
This paper extends the framework of probabilistic error cancellation to account for experimental constraints, enabling more practical and robust quantum error mitigation techniques in noisy quantum processors.
Contribution
It generalizes physical implementability to include arbitrary experimental quantum operations, improving the practicality of error mitigation methods.
Findings
Demonstrates noiseless error cancellation with noisy Pauli operations.
Analyzes bias introduced by noisy cancellation.
Links generalized implementability to quantum information measures.
Abstract
Decoherence severely limits the performance of quantum processors, posing challenges to reliable quantum computation. Probabilistic error cancellation, a quantum error mitigation method, counteracts noise by quasiprobabilistically simulating (non-physical) inverse noise operations. However, existing formulations of physical implementability, quantifying the minimal cost of simulating non-physical operations using physical channels, do not fully account for the experimental constraints, since noise also affects the cancellation process and not all physical channels are experimentally accessible. Here, we generalize the physical implementability to encompass arbitrary convex sets of experimentally available quantum states and operations. Within this generalized framework, we demonstrate noiseless error cancellation with noisy Pauli operations and analyze the bias of noisy cancellation.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMulti-Criteria Decision Making · Bayesian Modeling and Causal Inference
