Variational Quantum Anomaly Detection: Unsupervised mapping of phase diagrams on a physical quantum computer

Understand

One of the most promising applications of quantum computing is simulating quantum many-body systems.

• However, there is still a need for methods to efficiently investigate these systems in a native way, capturing their full complexity.
• Here, we propose variational quantum anomaly detection, an unsupervised quantum machine learning algorithm to analyze quantum data from quantum simulation.
• The algorithm is used to extract the phase diagram of a system with no prior physical knowledge and can be performed end-to-end on the same quantum device that the system is simulated on.