Nondestructive classification of quantum states using an algorithmic quantum computer
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RESEARCH ARTICLE
Nondestructive classification of quantum states using an algorithmic quantum computer D. V. Babukhin1,2,3 · A. A. Zhukov1,4 · W. V. Pogosov1,5 Received: 20 February 2019 / Accepted: 4 October 2019 © Springer Nature Switzerland AG 2019
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 quantumclassical 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. Keywords Quantum computing · Quantum data processing · Postprocessing · Quantum error correction · Error mitigation
1 Introduction Machine learning is a computing paradigm, where recognition of patterns in available data plays a central role, but the computing system is not explicitly programmed; many examples indeed demonstrate success of this approach to real-world problems. Quantum machine learning is an emergent technology based on the assumption that quantum resources can be useful in the pattern analysis, see, e.g., Wiebe et al. (2012), Schuld et al. (2015a), Biamonte et al. (2017), Amin et al. (2018), Preskill (2018), and Adcock
W. V. Pogosov
[email protected] 1
Dukhov Research Institute of Automatics (VNIIA), Moscow, Russia, 127055
2
Russian Quantum Center (RQC), Moscow, Russia, 143026
3
Lomonosov Moscow State University (MSU), Moscow, Russia, 119991
4
National Research Nuclear University (MEPhI), Moscow, Russia, 115409
5
Institute for Theoretical and Applied Electrodynamics, Russian Academy of Sciences, Moscow, Russia, 125412
et al. (2015). Quantum algorithms within such applications can be used as a part of a larger computation scheme which also incorporates classical blocks. There are two major approaches for the construction of a quantum block in such schemes—it can be represented either by quantum annealer or by algorithmic quantum computer (Biamonte et al. 2017). Most of the proposals unfortunately are characterized by input/output bottlenecks occurring at stages of encoding classical data into quantum states and decoding them back (Aaronson 2015; Arun
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