Quantum circuit learning

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

[1]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.

[2]  M. Schuld,et al.  Prediction by linear regression on a quantum computer , 2016, 1601.07823.

[3]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[4]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[5]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[6]  Moritz August,et al.  Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory , 2016, ArXiv.

[7]  Peter W. Shor,et al.  Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 1995, SIAM Rev..

[8]  J. Gambetta,et al.  Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets , 2017, Nature.

[9]  Matthias Troyer,et al.  Solving the quantum many-body problem with artificial neural networks , 2016, Science.

[10]  Masoud Mohseni,et al.  Quantum support vector machine for big feature and big data classification , 2013, Physical review letters.

[11]  Juan Carrasquilla,et al.  Machine learning quantum phases of matter beyond the fermion sign problem , 2016, Scientific Reports.

[12]  W. Marsden.  I and J , 2012 .

[13]  K. Müller,et al.  Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.

[14]  Matthias Rupp,et al.  Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. , 2015, Journal of chemical theory and computation.

[15]  Alán Aspuru-Guzik,et al.  A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.

[16]  Robert Raussendorf.  Quantum cellular automaton for universal quantum computation , 2005 .

[17]  Seth Lloyd,et al.  Quantum algorithm for data fitting. , 2012, Physical review letters.

[18]  A. Harrow,et al.  Quantum algorithm for linear systems of equations. , 2008, Physical review letters.

[19]  Keisuke Fujii,et al.  Harnessing disordered quantum dynamics for machine learning , 2016, 1602.08159.

[20]  Jun Li,et al.  Hybrid Quantum-Classical Approach to Quantum Optimal Control. , 2016, Physical review letters.

[21]  Franco Nori,et al.  QuTiP 2: A Python framework for the dynamics of open quantum systems , 2012, Comput. Phys. Commun..

[22]  Pawel Wocjan,et al.  Ergodic Quantum Computing , 2005, Quantum Inf. Process..