Nondestructive classification of quantum states using an algorithmic quantum computer

Abstract

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, we 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. We perform proof-of-principle implementation of this idea using superconducting quantum processor of IBM Quantum Experience. Another aspect we are interested in is a mitigation of errors occurring due to the quantum device imperfections. We apply a series of heuristic tricks at the stage of classical postprocessing 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.

arXiv comment18 pages

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Cited by in corpus (4)

• Hybrid digital-analog simulation of many-body dynamics with superconducting qubits
• The effect of quantum noise on algorithmic perfect quantum state transfer on NISQ processors
• Quantum error reduction with deep neural network applied at the post-processing stage
• Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics