Quantum Machine learning in NISQ era

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In this repo presented several algorithms for quantum computers that have a hope for achieving advantage over classical models at least in some tasks in Noisy Intermediate Scale Quantum (NISQ) era or in nearest future.

1. QML frameworks
   Firstly tools for running quantum algorithms by simulation or by real quantum computers. Frameworks' abilities and limitations are presented.
2. Continuous Variable Quantum Computing
   Foundations of photonic approach to quantum computing with infinite dimensional qumodes instead of 2-dim qubits.
3. Generative models
   • Quantum Recurrent Unit on Gaussian platform
     Continuous variable based algorithm used for text translation.
   • Quantum GAN-s
     Algorithms with widely believed exponential advantage over classical counterparts.
4. Category theory and ZX-calculus
   Mathematically justified diagrammatic language for writing quantum circuits
5. Quantum Graph classification models
   Exponential advantage due to parallel processing of subgraphs. And natural quantum graph embedding based on number of perfect matchings in subgraphs.

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