Quantum discriminator for binary classification
Abstract Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces—this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-dimensional spaces. The quantum discriminator is trained using a quantum-classical hybrid algorithm in $$$$\mathcal {O}(N\log N)$$$$ time, and inferencing is performed on a universal quantum computer in $$$$\mathcal {O}(N)$$$$ time. The quantum discriminator takes as input the binary features extracted from a given datum along with a prediction qubit, and outputs the predicted label. We analyze its performance on the Iris and Bars and Stripes data sets, and show that it can attain 99% accuracy in simulation.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 2281281
- Journal Information:
- Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 14; ISSN 2045-2322
- Publisher:
- Nature Publishing GroupCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
Similar Records
Comments on one-form global symmetries and their gauging in 3d and 4d
The diphoton $$q_T$$ spectrum at N$$^3$$LL$$^\prime $$ + NNLO