Курс по квантовому машинному обучению

Variational Quantum Circuits for Deep Reinforcement Learning since 2019. Xanadu Quantum Software Competition 1st Prize 2019.

Simulate and optimize quantum communication networks using quantum computers.

Qiskit implementation of classical shadow formalism with VQE for calculating ground state energies of molecules

Variational Quantum Algorithms for Unsupervised Image Segmentation

Fairness-aware, explainable AutoML for quantum + classical ML — 21 models, one Optuna search, SHAP & AI reports. uvx quoptuna

QuACS: Variational Quantum Algorithm for Coalition Structure Generation in Induced Subgraph Games

Comparative study: Quantum vs. classical models for Cart Pole. Examining entanglement layers and data re-uploading, highlighting quantum model superiority.

When performance survives noise, identifiability may not.

Verification harness for quantum ML. A reproducible lab for stress-testing quantum models where predictive accuracy, identifiability, curvature, and robustness under noise can diverge.

This repository contains the source code and results for the experiments presented in Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits.

An advanced exploration of Quantum Fourier Transform (QFT) using Quantum Machine Learning (QML). This project delves into the optimization of variational quantum circuits, leveraging machine learning techniques to evaluate and visualize the transformation capabilities of QFT in quantum computing.

The code for the article "Certified variational quantum algorithms for eigenstate preparation"

DDQCL implementation using Qiskit. Variational quantum circuit that maps a randomly generated set of four 4-qubit input states to four 4-qubit output states. Circuit parameters are refined over time to get the lowest cost parameter set.

Hybrid Quantum-Classical Brain Tumor Detection using ResNet50, VQC, Grad-CAM, and Integrated Gradients for Explainable AI in Medical Imaging.

Planting a clinical dependency graph into the entanglement topology of a shallow quantum circuit, to generate synthetic MIMIC-IV ICU data. A light-cone theorem forces the circuit to be shallow, and makes the graph's alignment measurable.

Companion notebook for A Technical Introduction to Quantum Neural Networks. Four small PennyLane experiments on encoding, depth and trainability, classical baselines, and finite-shot cost.

A demonstration of using variational quantum optimization (VQO) to find a quantum protocol that maximally violates the CHSH inequality.

Curated GitHub Pages site tracking Quantum ML, Quantum NLP, Quantum Vision, and Hybrid Quantum-Classical AI - papers, architectures, LaTeX equations, circuit diagrams, and hardware milestones (2009–2026).

Quantum circuit-based hyperparameter optimization for boosting algorithms (XGBoost, AdaBoost, GradientBoost, CatBoost, LightGBM) using PennyLane variational circuits — classical vs quantum comparison