GTN Co-Authored Paper Published in Nature Quantum Information.

A paper on Hierarchical Quantum Classifiers co-authored by Vid Stojevic, CTO of GTN, has been published in Nature Quantum Information.

Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. This paper demonstrate that more expressive circuits in the same family achieve progressively better accuracy. Furthermore, the approach is used to classify highly entangled quantum states, for which there is no known efficient classical method.

The work is tightly linked to GTN's efforts to scale ML models to capture quantum properties. Importantly, the work provides a strong theoretical support that tensorial methods, our core technology, are required for doing machine learning on quantum systems. GTN, being the world's leader in these technologies, will have capabilities to address currently intractable challenges of significance in drug discovery.

These are important technical results supporting GTN’s current quantum-inspired machine learning approaches, as well as our longer term plans to be fully ready for quantum computing chipsets when they become powerful enough to demonstrate quantum supremacy.

The paper was in collaboration with numerous academics including Andrew G. Green at UCL, one of GTN’s Advisors, and Prof. Simone Severini from UCL and Amazon.

The paper can be found here.