Quantum circuit learning | RDL Research Database

Abstract

We propose a classical-quantum hybrid algorithm for machine learning on\nnear-term quantum processors, which we call quantum circuit learning. A quantum\ncircuit driven by our framework learns a given task by tuning parameters\nimplemented on it. The iterative optimization of the parameters allows us to\ncircumvent the high-depth circuit. Theoretical investigation shows that a\nquantum circuit can approximate nonlinear functions, which is further confirmed\nby numerical simulations. Hybridizing a low-depth quantum circuit and a\nclassical computer for machine learning, the proposed framework paves the way\ntoward applications of near-term quantum devices for quantum machine learning.\n

Keywords

Computer scienceQuantumQuantum circuitQuantum machine learningQuantum computerQuantum algorithmElectronic engineeringQuantum networkPhysicsQuantum mechanicsEngineering

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Publication Info

Year: 2018
Type: article
Volume: 98
Issue: 3
Citations: 1398
Access: Closed

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APA Style

                            
                                
                                    Kosuke Mitarai, 
                                
                                    Makoto Negoro, 
                                
                                    Masahiro Kitagawa
                                
                                et al.
                            
                            (2018). 
                            Quantum circuit learning. 
                            Physical review. A/Physical review, A
                            , 98
                            (3)
                            .
                            https://doi.org/10.1103/physreva.98.032309
                        

Identifiers

DOI: 10.1103/physreva.98.032309