Journal
NPJ QUANTUM INFORMATION
Volume 4, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41534-018-0116-9
Keywords
-
Categories
Funding
- UK Engineering and Physical Sciences Research Council (EPSRC) [EP/P510270/1]
- Rahko Limited
- EPSRC [EP/L015242/1]
- Cambridge Quantum Computing Limited (CQCL)
- Royal Society
- US DOD [ARO-MURI W911NF-17-1-0304]
- UK MOD [ARO-MURI W911NF-17-1-0304]
- UK EPSRC under the Multidisciplinary University Research Initiative [ARO-MURI W911NF-17-1-0304]
- NVIDIA Corporation
- EPSRC [EP/R018693/1] Funding Source: UKRI
Ask authors/readers for more resources
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circuits in the same family achieve better accuracy and can be used to classify highly entangled quantum states, for which there is no known efficient classical method. We compare performance for several different parameterizations on two classical machine learning datasets, Iris and MNIST, and on a synthetic dataset of quantum states. Finally, we demonstrate that performance is robust to noise and deploy an Iris dataset classifier on the ibmqx4 quantum computer.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available