Pauli Noise Learning for Mid-Circuit Measurements
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913796682940416 |
|---|---|
| author | Hines, Jordan Proctor, Timothy |
| author_facet | Hines, Jordan Proctor, Timothy |
| contents | Current benchmarks for mid-circuit measurements (MCMs) are limited in scalability or the types of error they can quantify, necessitating new techniques for quantifying their performance. Here, we introduce a theory for learning Pauli noise in MCMs and use it to create MCM cycle benchmarking, a scalable method for benchmarking MCMs. MCM cycle benchmarking extracts detailed information about the rates of errors in randomly compiled layers of MCMs and Clifford gates, and we demonstrate how its results can be used to quantify correlated errors during MCMs on current quantum hardware. Our method can be integrated into existing Pauli noise learning techniques to scalably characterize and benchmark wide classes of circuits containing MCMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09299 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pauli Noise Learning for Mid-Circuit Measurements Hines, Jordan Proctor, Timothy Quantum Physics Current benchmarks for mid-circuit measurements (MCMs) are limited in scalability or the types of error they can quantify, necessitating new techniques for quantifying their performance. Here, we introduce a theory for learning Pauli noise in MCMs and use it to create MCM cycle benchmarking, a scalable method for benchmarking MCMs. MCM cycle benchmarking extracts detailed information about the rates of errors in randomly compiled layers of MCMs and Clifford gates, and we demonstrate how its results can be used to quantify correlated errors during MCMs on current quantum hardware. Our method can be integrated into existing Pauli noise learning techniques to scalably characterize and benchmark wide classes of circuits containing MCMs. |
| title | Pauli Noise Learning for Mid-Circuit Measurements |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2406.09299 |