VQE on photonic quantum devices
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Updated
Aug 20, 2026 - Python
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.
Master’s thesis research framework for noise-robust hybrid quantum–classical neural networks (HQNNs), evaluating reliability, architecture design, and deployment strategies on NISQ hardware.
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.
Variational Quantum Algorithms
Quantum Computing | Optimisation: Exploring hardware-aware variational quantum algorithms for optimisation and machine learning on near-term quantum devices.
Collections of quantum methods: variational algorithms and measurement optimizations
Reproducible VQE research workflow for molecular simulation with FCI/CASCI baselines, Fourier landscape analysis, and ML-based configuration recommendation.
Quantum Assisted Simulator (QAS) for studying many-body localization in disordered Ising chains using Qiskit and QuTiP
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