TY - JOUR
T1 - Hybrid Helmholtz machines
T2 - a gate-based quantum circuit implementation
AU - van Dam, Teresa J.
AU - Neumann, Niels M.P.
AU - Phillipson, Frank
AU - van den Berg, Hans
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Quantum machine learning has the potential to overcome problems that current classical machine learning algorithms face, such as large data requirements or long learning times. Sampling is one of the aspects of classical machine learning that might benefit from quantum machine learning, as quantum computers intrinsically excel at sampling. Current hybrid quantum-classical implementations provide ways to already use near-term quantum computers for practical applications. By expanding the horizon on hybrid quantum-classical approaches, this work proposes the first implementation of a gated quantum-classical hybrid Helmholtz machine, a gate-based quantum circuit approximation of a neural network for unsupervised tasks. Our approach focuses on parameterized shallow quantum circuits and effectively implements an approximate Bayesian network, overcoming the exponential complexity of exact networks. In addition, a new balanced cost function is introduced, preventing the need of millions of training samples. Using a bars and stripes data set, the model, implemented on the Quantum Inspire platform, is shown to outperform classical Helmholtz machines in terms of the Kullback–Leibler divergence.
AB - Quantum machine learning has the potential to overcome problems that current classical machine learning algorithms face, such as large data requirements or long learning times. Sampling is one of the aspects of classical machine learning that might benefit from quantum machine learning, as quantum computers intrinsically excel at sampling. Current hybrid quantum-classical implementations provide ways to already use near-term quantum computers for practical applications. By expanding the horizon on hybrid quantum-classical approaches, this work proposes the first implementation of a gated quantum-classical hybrid Helmholtz machine, a gate-based quantum circuit approximation of a neural network for unsupervised tasks. Our approach focuses on parameterized shallow quantum circuits and effectively implements an approximate Bayesian network, overcoming the exponential complexity of exact networks. In addition, a new balanced cost function is introduced, preventing the need of millions of training samples. Using a bars and stripes data set, the model, implemented on the Quantum Inspire platform, is shown to outperform classical Helmholtz machines in terms of the Kullback–Leibler divergence.
KW - Gate-based quantum computing
KW - Helmholtz machine
KW - Hybrid algorithms
KW - Machine learning
KW - Quantum computing
UR - http://www.scopus.com/inward/record.url?scp=85081693581&partnerID=8YFLogxK
U2 - 10.1007/s11128-020-02660-2
DO - 10.1007/s11128-020-02660-2
M3 - Article
AN - SCOPUS:85081693581
VL - 19
JO - Quantum Information Processing
JF - Quantum Information Processing
SN - 1570-0755
IS - 6
M1 - 174
ER -