A. Y. Matsuura

Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor

  1. M. S. Moreira,
  2. G. G. Guerreschi,
  3. W. Vlothuizen,
  4. J. F. Marques,
  5. J. van Straten,
  6. S. P. Premaratne,
  7. X. Zou,
  8. H. Ali,
  9. N. Muthusubramanian,
  10. C. Zachariadis,
  11. J. van Someren,
  12. M. Beekman,
  13. N. Haider,
  14. A. Bruno,
  15. C. G. Almudever,
  16. A. Y. Matsuura,
  17. and L. DiCarlo
Artificial neural networks are becoming an integral part of digital solutions to complex problems. However, employing neural networks on quantum processors faces challenges related
to the implementation of non-linear functions using quantum circuits. In this paper, we use repeat-until-success circuits enabled by real-time control-flow feedback to realize quantum neurons with non-linear activation functions. These neurons constitute elementary building blocks that can be arranged in a variety of layouts to carry out deep learning tasks quantum coherently. As an example, we construct a minimal feedforward quantum neural network capable of learning all 2-to-1-bit Boolean functions by optimization of network activation parameters within the supervised-learning paradigm. This model is shown to perform non-linear classification and effectively learns from multiple copies of a single training state consisting of the maximal superposition of all inputs.
arxiv pdf

Variational preparation of finite-temperature states on a quantum computer

  1. R. Sagastizabal,
  2. S. P. Premaratne,
  3. B. A. Klaver,
  4. M. A. Rol,
  5. V. Negîrneac,
  6. M. Moreira,
  7. X. Zou,
  8. S. Johri,
  9. N. Muthusubramanian,
  10. M. Beekman,
  11. C. Zachariadis,
  12. V.P. Ostroukh,
  13. N. Haider,
  14. A. Bruno,
  15. A. Y. Matsuura,
  16. and L. DiCarlo
The preparation of thermal equilibrium states is important for the simulation of condensed-matter and cosmology systems using a quantum computer. We present a method to prepare such
mixed states with unitary operators, and demonstrate this technique experimentally using a gate-based quantum processor. Our method targets the generation of thermofield double states using a hybrid quantum-classical variational approach motivated by quantum-approximate optimization algorithms, without prior calculation of optimal variational parameters by numerical simulation. The fidelity of generated states to the thermal-equilibrium state smoothly varies from 99 to 75% between infinite and near-zero simulated temperature, in quantitative agreement with numerical simulations of the noisy quantum processor with error parameters drawn from experiment.
arxiv pdf