Quantum Science and Technologies

QUANTUM INFORMATION AND MACHINE LEARNING:

COMMON APPROACHES AND TOOLS

[Project No. 20TTATQTa003 funded by SCS of Armenia]

Two broad fields of knowledge have recently experienced explosive growth: Quantum Information and Machine Learning. The former relies on the fundamental principles of quantum physics and it aims to uncover the nature of information and pave the way for quantum technologies: quantum computations, simulations, communications and metrology. The latter combines Bayesian and frequentist statistics with artificial intelligence to analyze large data and make better decisions in many areas of human activities, ranging from computer vision and self-driving vehicles, to speech and text recognition and interactive human-machine interfaces. Remarkably, both fields are based on probabilistic theories, and draw their advantages from efficient implementation of computation and/or inference tasks. However, their interrelations remain largely unexplored, and in this project we take several steps in the direction of uncovering them. Thus, one of the main tasks in artificial intelligence is the search in a large database, or optimization of a multi-parameter function. The Grover algorithm is a hallmark of quantum computation that quadratically speeds up the search in an unstructured database. We suggest that this search can proceed even faster, if the physical model of the database is subject to an external open-loop control. The motivation for studying this problem is two-fold. First, in a hybrid system, the quantum search can be embedded into a classical machine learning to greatly improve its speed. Second, quantum algorithms rely on entanglement and superposition and uncovering their classical counterparts can lead to new tools in classical data processing. Quantum computations suffer from decoherence and noise affecting the qubits and quantum gates. We shall thus analyze various methods for noise mitigation, and optimize them via the classical method of extrapolation to zero noise limit. Another important example of problems relate to quasiprobability distributions that are frequently employed in quantum optics for representations of continuous quantum variables. They are more flexible than the usual probabilities and importing them to classical machine learning may greatly assist supervised algorithms. We also envisage physics-inspired methods for unsupervised learning.

In summary, the main purpose of this project is to study pertinent aspects of quantum information and computation and to adapt the thereby learned quantum methods and tools to basic machine learning tasks.


Reports

Report 2021

Report 2022

Report 2023

Outreach

Seminars

Group meetings: Every Tuesday 13:30-15:00 at AANL

Quantum Statistical Thermodynamics

Presentations of the project results

Project publications

  1. A. E. Allahverdyan, D. Petrosyan, Dissipative search of an unstructured database, arXiv:2106.02703 [quant-ph]
  2. A.E. Allahverdyan, K. V. Hovhannisyan, D. Petrosyan, Dynamical symmetrization of the state of identical particles, Proc. R. Soc. A 477, 20200911 (2021) [ arXiv:2011.08839 [quant-ph]]

Quantum technologies around the World

R. Thew, T. Jennewein, M. Sasaki, Focus on Quantum Science and Technology Initiatives Around the World, Quantum Sci. Technol. 5, 010201 (2019).

The European roadmap for quantum technologies

A. Acín et al., The quantum technologies roadmap: a European community view, New J. Phys. 20, 080201 (2018).

Quantum technologies in Armenia

To build competence in Quantum Science and Technology in Armenia, meaningful support at the National level is a necessity.