Quantum-enhanced cluster detection in physical images
arXiv:2208.05522 · doi:10.1103/PhysRevApplied.19.054031
Abstract
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 have to first 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 if 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.
arXiv comment14 pages, 9 figures. Similar to published version. Supplemental material available at https://github.com/softquanta/clustering
References in corpus (13)
- Quantum Machine Learning
- Advances in Quantum Metrology
- Gaussian Quantum Information
- Atomic-scale imaging of a 27-nuclear-spin cluster using a single-spin quantum sensor
- Real applications of quantum imaging
- Quantum Reading of a Classical Digital Memory
- Phase Imaging with Computational Specificity (PICS) for measuring dry mass changes in sub-cellular compartments
- Generalization in Quantum Machine Learning: a Quantum Information Perspective
- Unsupervised classification of quantum data
- Quantum-enhanced barcode decoding and pattern recognition
- Ultimate Limits of Thermal Pattern Recognition
- Idler-free channel position finding
- Analytical bounds for non-asymptotic asymmetric state discrimination