Nondestructive classification of quantum states using an algorithmic quantum computer

Methods of processing quantum data become more important as quantum computing devices improve their quality towards fault tolerant universal quantum computers. These methods include discrimination and filtering of quantum states given as an input to the device that may find numerous applications in quantum information technologies.

In the present paper, researchers address a scheme of a classification of input states, which is nondestructive and deterministic for certain inputs, while probabilistic, in general case. This can be achieved by incorporating phase estimation algorithm into the hybrid quantum-classical computation scheme, where quantum block is trained classically. The team performs proof-of-principle implementation of this idea using superconducting quantum processor of IBM Quantum Experience.

Another aspect they are interested in is a mitigation of errors occurring due to the quantum device imperfections. They apply a series of heuristic tricks at the stage of classical post-processing in order to improve raw experimental data and to recognize patterns in them.

These ideas may find applications in other realization of hybrid quantum-classical computations with noisy quantum machines. (SciRate)

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