This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).

End-to-end agentic quantum-ML pipeline: PennyLane GPU quantum-kernel anomaly detection + LangGraph multi-agent system + locally-hosted Qwen3-14B, benchmarked across network-intrusion, LHC physics, and fraud.

Registered Software. Official code of the published article "Automatic design of quantum feature maps". This quantum machine learning technique allows to auto-generate quantum-inspired classifiers by using multiobjetive genetic algorithms for tabular data.

Implementations of quantum circuits for Quantum Neural Networks with Qutrits

A light, transparent, and modular inference & quantization engine for studying LLMs.

Benchmarking quanvolutional neural networks with QCML and Pennylane.

Trainable, scalable quantum embedding kernels with variational parameters

Quantum Robustness Score (QRS): a normalised metric for evaluating noise resilience in Quantum Kernel SVMs on NISQ hardware. Tested across 5 datasets, 3 noise channels, and ~2,000 simulation runs.

Quantum kernel SVM for rapid Ebola strain triage (Bundibugyo · Zaire · Sudan · Non-HF). ZZFeatureMap encoding of 20 clinical features. Motivated by the 2026 DRC/Uganda Bundibugyo PHEIC. PennyLane · scikit-learn · imbalanced learning.

Quantum Kernel Machine Learning for Drug Design A rigorous, end-to-end Qiskit implementation of quantum kernel SVMs for predicting blood-brain barrier permeability (BBBP) — a core ADMET property in CNS drug discovery — with three controlled experiments that actually test whether the quantum part is doing anything useful.

Modular Python framework for quantum machine learning using PennyLane, including variational classifiers, quantum kernels, and reproducible workflows for hybrid quantum–classical experiments.

Quantum machine learning for transient stability assessment of power systems.

Assignment as part of M.Sc. Quantum Science & Technology @ Trinity College Dublin.

Quantum-informed IBD modeling using ESM protein embeddings and Qiskit QSVC/quantum kernels; CLI for ingest → train → report.

This quantum machine learning technique allows to auto-generate quantum-inspired classifiers by using multiobjetive genetic algorithms for grayscale images, optimizing both quantum circuits and dimensionality reduction method.