variational-algorithms

VQE on photonic quantum devices

Quantum kernel estimation with backend-matched IBM noise modeling, plus reproducible “Wigner’s friend” branch-transfer coherence-witness experiments executed on superconducting quantum hardware.

Surrogate benchmark for QAS. Includes code, datasets, and tools for fast evaluation and integration into custom QAS pipelines.

In this folder, I upload my learning about quantum computing applied to machine learning. Each project contains both the mathematical breakdown and the code. The order reflects how I developed them, allowing you to see my learning process and corrections.

Applied Variational Quantum Eigensolver (VQE) implementations and syndrome decoding pipelines for quantum error correction research

Master’s thesis research framework for noise-robust hybrid quantum–classical neural networks (HQNNs), evaluating reliability, architecture design, and deployment strategies on NISQ hardware.

Quantum Computing | Optimisation: Exploring hardware-aware variational quantum algorithms for optimisation and machine learning on near-term quantum devices.

Variational Quantum Algorithms

Quantum Assisted Simulator (QAS) for studying many-body localization in disordered Ising chains using Qiskit and QuTiP

Collections of quantum methods: variational algorithms and measurement optimizations

Notebooks to run on real IBM devices

Reproducible reference implementations of landmark quantum computing papers, each validated against an independent classical oracle.

Reproducible VQE research workflow for molecular simulation with FCI/CASCI baselines, Fourier landscape analysis, and ML-based configuration recommendation.