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Invited Talk 4: Quantum in ML and ML in Quantum
Ivan Oseledets

Fri Dec 11 08:05 AM -- 08:50 AM (PST) @
in First Workshop on Quantum Tensor Networks in Machine Learning »

In this talk, I will cover recent results in two areas: 1) Using quantum-inspired methods in machine learning, including using low-entanglement states (matrix product states/tensor train decompositions) for different regression and classification tasks. 2) Using machine learning methods for efficient classical simulation of quantum systems. I will cover our results on simulating quantum circuits on parallel computers using graph-based algorithms, and also efficient numerical methods for optimization using tensor-trains for the computational of large number (up to B=100) on GPUs. The code is a combination of classical linear algebra algorithms, Riemannian optimization methods and efficient software implementation in TensorFlow.

  1. Rakhuba, M., Novikov, A. and Oseledets, I., 2019. Low-rank Riemannian eigensolver for high-dimensional Hamiltonians. Journal of Computational Physics, 396, pp.718-737.
  2. Schutski, Roman, Danil Lykov, and Ivan Oseledets. Adaptive algorithm for quantum circuit simulation. Physical Review A 101, no. 4 (2020): 042335.
  3. Khakhulin, Taras, Roman Schutski, and Ivan Oseledets. Graph Convolutional Policy for Solving Tree Decomposition via Reinforcement Learning Heuristics. arXiv preprint arXiv:1910.08371 (2019).

Author Information

Ivan Oseledets (Skoltech)

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