Low-overhead tailoring and learning of noise in graph states

Abstract:Graph and hypergraph states are important resource states for realizing universal quantum computation and diverse non-local physical phenomena. However, noise learning in such states is challenging due to their large entanglement and magic. This work establishes a low Clifford hierarchy circuit-based scheme for tailoring and learning noise in (hyper)graph states generated by multiple controlled-Z gates. The key is to convert the noisy input state into diagonal form and derive the convolution equation for the diagonal noise rates, which has a lower resource overhead. Such a diagonal form can be used in real quantum simulation by using the same conversion technique on the graph state input. We demonstrate that a single-depth Bell measurement is sufficient in our scheme for arbitrary graph states, while noise tailoring can be done via Pauli operations. After that, we suggest the Walsh-Hadamard transform and some approximation method for decoding the convolution equation to estimate the tailored noise. We prove that constant sampling and polynomial time complexities are sufficient to guarantee bounded noise estimation error with respect to the $l_2$-norm, which usually requires exponential complexity with existing noise estimation methods. The polynomial complexities are maintained even for the more stringent $l_1$ approximation under the sparse noise assumption. Conclusively, we propose novel methods for characterizing the noise properties of an entangled state at a lower cost than that of state generation. Moreover, compared with state verification methods, we can attain richer information on noise rates and enable noise mitigation, thereby providing guarantees for the preparation of graph states.

arXiv:2503.12870 [quant-ph]

(or arXiv:2503.12870v3 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2503.12870 Focus to learn more arXiv-issued DOI via DataCite

Phys. Rev. A 114, 042428 (2026)

https://doi.org/10.1103/rr8y-yz5m Focus to learn more DOI(s) linking to related resources

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