Publications (6)
sQUlearn -- A Python Library for Quantum Machine Learning
David A. Kreplin, Moritz Willmann, Jan Schnabel +3
sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scik…
Quantum machine learning models for graphs
Frédéric Sauvage, Pranav Kalidindi, Frederic Rapp +1
Geometric Machine Learning (GML) successes have been achieved through the thorough study and design of new equivariant neural networks. In comparison, geometric quantum machine lea…
Automated near-term quantum algorithm discovery for molecular ground states
Fabian Finger, Frederic Rapp, Pranav Kalidindi +10
Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform…
Quantum Gaussian Process Regression for Bayesian Optimization
Frederic Rapp, Marco Roth
Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on paramete…
Efficiently Simulable Pauli Correlation Encoding
Daniele Lizzio Bosco, Gabriel Matos, Chen-Yu Liu +4
Pauli Correlation Encoding (PCE) is a heuristic framework for binary optimisation that encodes classical variables into many-body Pauli observables. While PCE requires fewer qubits…
Reinforcement learning-based architecture search for quantum machine learning
Frederic Rapp, David A. Kreplin, Marco F. Huber +1
Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influen…