Quantum-Enhanced Cluster Detection in Physical Images
Identifying clusters in data is an important task in many fields. In this paper, we consider situations in which data live in a physical world, so we first have to collect the images using sensors before clustering them. Using sensors enhanced by quantum entanglement, we can image surfaces more accurately than using purely classical strategies. However, it is not immediately obvious whether the advantage we gain is robust enough to survive data-processing steps such as clustering. It has previously been found that using quantum-enhanced sensors for imaging and pattern recognition can give an advantage for supervised learning tasks, and here we demonstrate that this advantage also holds for an unsupervised learning task, namely clustering.
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP); European Union (EU); USDOE
- Grant/Contract Number:
- AC02-07CH11359; 862644
- OSTI ID:
- 1973034
- Alternate ID(s):
- OSTI ID: 1906060
- Report Number(s):
- FERMILAB-PUB-22-869-SQMS-V; arXiv:2208.05522; oai:inspirehep.net:2135007; TRN: US2311646
- Journal Information:
- Physical Review Applied, Vol. 19, Issue 5; ISSN 2331-7019
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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