Support Vector Machines on Noisy Intermediate Scale Quantum Computers
arXiv:1909.11988
Abstract
Support vector machine algorithms are considered essential for the implementation of automation in a radio access network. Specifically, they are critical in the prediction of the quality of user experience for video streaming based on device and network-level metrics. Quantum SVM is the quantum analogue of the classical SVM algorithm, which utilizes the properties of quantum computers to speed up the algorithm exponentially. In this work, we derive an optimized preprocessing unit for a quantum SVM that allows classifying any two-dimensional datasets that are linearly separable. We further provide a result readout method of the kernel matrix generation circuit to avoid quantum tomography that, in turn, reduces the quantum circuit depth. We also derive a quantum SVM system based on an optimized HHL quantum circuit with reduced circuit depth.
References in corpus (8)
- Quantum Computing in the NISQ era and beyond
- Quantum algorithm for solving linear systems of equations
- Quantum support vector machine for big data classification
- Experimental Realization of Quantum Artificial Intelligence
- Concrete Categorical Model of a Quantum Circuit Description Language with Measurement
- Jensen-Shannon divergence as a measure of distinguishability between mixed quantum states
- Experimental Quantum Computing to Solve Systems of Linear Equations
- Optimization of Circuits for IBM's five-qubit Quantum Computers