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Reproduction of the results obtained in the work of Mitarai et al. (https://doi.org/10.1103/physreva.98.032309) where they propose and apply a framework called Quantum Circuit Learning to three different optimization problems

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Quantum Circuit Learning

Reproduction of the results obtained in the work of Mitarai et al. [1]. Implemented following QuLacs tutorial in ref. [2].

Contents

  • qcl_tutorial: Implementation of the tutorial in ref. [2] to fit a sinusoidal function.
  • qcl_classification: Classification of data in two classes using qcl, reproducing results shown in fig. 4 of ref. [1]
  • qcl_dynamics: Simulation of the dynamics of a system of 5 spins described by a transverse Ising Hamiltonian using a 6 depth 6 qubit quantum circuit. Similar to the results obtained in fig. 5 of ref [1].

References

[1] - Mitarai K, Makoto Negoro, Masanobu Kitagawa, and Kiyotaka Fujii. 2018. ‘Quantum Circuit Learning’, Physical Review, 98.3 (American Physical Society) https://doi.org/10.1103/physreva.98.032309

[2] - Qulacs Authors. 2018. ‘Quantum Circuit Learning - Qulacs Documentation’, Docs.qulacs.org https://docs.qulacs.org/en/latest/apply/5.2_qcl.html

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Reproduction of the results obtained in the work of Mitarai et al. (https://doi.org/10.1103/physreva.98.032309) where they propose and apply a framework called Quantum Circuit Learning to three different optimization problems

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