VQE on photonic quantum devices
- Updated
- Python
#
variational-algorithms
Here are 10 public repositories matching this topic...
Quantum kernel estimation with backend-matched IBM noise modeling, plus reproducible “Wigner’s friend” branch-transfer coherence-witness experiments executed on superconducting quantum hardware.
machine-learning qml svm quantum-computing classification superconducting-qubits quantum-machine-learning qiskit ibm-quantum-computer feature-maps quantum-technology ibm-quantum quantum-ml noise-modeling variational-algorithms quantum-kernels superconducting-computing ibm-quantum-hardware eagle-processor kernel-alignment
- Updated
- Python
Surrogate benchmark for QAS. Includes code, datasets, and tools for fast evaluation and integration into custom QAS pipelines.
benchmarking quantum-computing quantum-machine-learning quantum-circuits surrogate-models quantum-benchmarks quantum-architecture-search parametrized-quantum-circuits variational-algorithms
- Updated
- Jupyter Notebook
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.
- Updated
- Jupyter Notebook
Variational Quantum Algorithms
- Updated
- Jupyter Notebook
Master’s thesis research framework for noise-robust hybrid quantum–classical neural networks (HQNNs), evaluating reliability, architecture design, and deployment strategies on NISQ hardware.
research quantum-computing quantum-machine-learning masters-thesis qiskit cirq nisq hybrid-quantum-classical-algorithms quantum-neural-networks hybrid-models pennylane research-framework hybrid-ai hqnn variational-algorithms noise-robust-learning
- Updated
- Dockerfile
Quantum Assisted Simulator (QAS) for studying many-body localization in disordered Ising chains using Qiskit and QuTiP
- Updated
- Python
Collections of quantum methods: variational algorithms and measurement optimizations
- Updated
- Jupyter Notebook
Quantum Computing | Optimisation: Exploring hardware-aware variational quantum algorithms for optimisation and machine learning on near-term quantum devices.
benchmarking quantum-computing quantum-chemistry qiskit nisq vqe hardware-aware variational-algorithms noise-aware learned-optimizers
- Updated
- Python
This project implements a quantum optimization solution for the classic Max-Cut problem, using Variational Quantum Imaginary Time Evolution (varQITE) on a 4-node graph.
- Updated
- Jupyter Notebook
Improve this page
Add a description, image, and links to the variational-algorithms topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the variational-algorithms topic, visit your repo's landing page and select "manage topics."