Variational-toolbox-based separability detection of multiqubit states

Parametrized quantum circuits (PQCs) are crucial in variational quantum algorithms. While it is commonly believed that the optimal PQC is solely used to reproduce the target state, we here reveal that the optimal PQC can also provide valuable insights into the state's properties. We propose variational toolboxes to identify the $k$-separability of pure states, with or without preparation noise, by checking the structure within the optimal PQCs. Additionally, we introduce adaptive optimization strategies to detect the $k$-separability of mixed states. Compared to fixed PQCs, our approach controls fewer parameters for low-rank states. Finally, we validate our methods through numerical demonstrations for various states.

Authors

Institutions

Publication Details

Journal
Physical review. A/Physical review, A
Published
2025-07-14
DOI
https://doi.org/10.1103/44wv-9qlf
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Variational-toolbox-based separability detection of multiqubit states

Qiongyi He, Shao-Ming Fei, Karl J. Friston
Physical review. A/Physical review, A
Fault Detection and Control Systems
article

Variational-toolbox-based separability detection of multiqubit states

Qiongyi He, Shao-Ming Fei, Karl J. Friston
article en

Abstract

Parametrized quantum circuits (PQCs) are crucial in variational quantum algorithms. While it is commonly believed that the optimal PQC is solely used to reproduce the target state, we here reveal that the optimal PQC can also provide valuable insights into the state's properties. We propose variational toolboxes to identify the $k$-separability of pure states, with or without preparation noise, by checking the structure within the optimal PQCs. Additionally, we introduce adaptive optimization strategies to detect the $k$-separability of mixed states. Compared to fixed PQCs, our approach controls fewer parameters for low-rank states. Finally, we validate our methods through numerical demonstrations for various states.

Physical review. A/Physical review, AVol. 112(2)
Peking University (CN), Hefei University (CN), Shanxi University of Traditional Chinese Medicine (CN), Capital Normal University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Reduced inequalities
Openalex Percentile: Top 80%
Fault Detection and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.