Small-Scale Artificial Neural Networks for Statistical Modeling and Controling Quantum Systems

Borisenok S.

Kuantum Optiği ve Bilişimi Toplantısı KOBİT-6, Ankara, Türkiye, 3 - 04 Şubat 2022, ss.39

  • Yayın Türü: Bildiri / Özet Bildiri
  • Basıldığı Şehir: Ankara
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.39
  • Abdullah Gül Üniversitesi Adresli: Evet

Özet

Very recently a new statistical physics approach based on Artificial Neural Networks (ANNs) has been proposed by Wang, Jiang, and Zhou in 2020 for the particular case of a cyclic Ising system. A small-scale ANN is well-trained with the data collected from the „Ab Initio‟ experiment or numerical simulations for mimicking the microscopic statistical states of a quantum system. Then it can be spontaneously extrapolated to evaluate macroscopic states of the quantum system, its phase structures, and thermodynamic characteristics.

We study alternative small-scale ANN approaches (Masked Autoencoder for Distribution Estimation, Hodgkin-Huxley neural networks) to describe the statistical micro- and macro-states of different quantum systems (qubits, Ising spin systems, memristors), and their possible applications to control over the quantum system dynamics.

Additionally, we discuss the emulation of quantum algorithms with a small population of classical non-linear artificial neural elements (like Hodgkin-Huxley neurons) and their possible application to control other quantum and classical systems.

Keywords: Artificial Neural Networks, Quantum Dynamical Systems, Feedback Control