Quantum distance-based classifier with constant size memory, distributed knowledge and state recycling
This work addresses resource constraints in quantum machine learning for near-term hardware, but it is incremental as it builds on existing methods.
The paper tackles the problem of reducing quantum memory requirements for distance-based classification on near-term quantum devices, showing that only part of the information needs coherent evolution, enabling an algorithm with significantly reduced memory size, and improves accuracy by recycling post-measurement states.
In this work we examine recently proposed distance-based classification method designed for near-term quantum processing units with limited resources. We further study possibilities to reduce the quantum resources without any efficiency decrease. We show that only a part of the information undergoes coherent evolution and this fact allows us to introduce an algorithm with significantly reduced quantum memory size. Additionally, considering only partial information at a time, we propose a classification protocol with information distributed among a number of agents. Finally, we show that the information evolution during a measurement can lead to a better solution and that accuracy of the algorithm can be improved by harnessing the state after the final measurement.