Variational Quantum Circuits for Machine Learning. An Application for the Detection of Weak Signals

Featured Application Quantum classifier to detect weak signals. Quantum computing is a new paradigm for a multitude of computing applications. This study presents the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. One of the main applications for quantum computing is the development of new algorithms for machine learning. In this study, an implementation of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problems. This circuit is specially designed for the noisy intermediate-scale quantum (NISQ) computers that are currently available. As an experiment, the circuit is tested on a real quantum computer based on superconducting qubits for an application to detect weak signals of the future. Weak signals are indicators of incipient changes that will have a future impact. Even for experts, the detection of these events is complicated since it is too early to predict this impact. The data obtained with the experiment shows promising results but also confirms that ongoing technological development is still required to take full advantage of quantum computing.

Automated summary

This study presented technologies for physical implementation of qubits and quantum gates, focusing on the application of a quantum circuit based on support vector machines for classification problems. The experimental results showed promising outcomes, indicating the potential of quantum computing in detecting weak signals. However, further technological development is still required to fully leverage quantum computing capabilities.