Blog
Quantum Machine Learning: New Paths with Stochastic Neural Networks
How neural networks can be expressed as quantum gate circuits — using stochastic neurons instead of deterministic activation functions. Work from within the QUICS consortium, including an interactive demo in which a trained QNN plays Flappy Bird.
Neural Networks vs. Quantum Neural Networks: What Actually Changes?
A practical comparison of classical neural networks and PQC-based quantum neural networks, including data encoding, measurement, and hybrid training.
From Classical to Quantum Machine Learning
A concise introduction to classical machine learning, quantum machine learning, and the main ways quantum circuits can be integrated into learning workflows.
Quantum magic: What QC can and cannot do
Entanglement alone isn't enough for a quantum advantage. This post explains the essential resource known as 'magic', why T-gates make all the difference, and where the limits of the technology lie.
Vehicle Routing Problem with Quantum Annealing
Quantum annealing helps solve complex logistics problems like the Vehicle Routing Problem. We split the problem into a clustering and routing phase, encode it as QUBO, and solve it with a D-Wave simulator.
Gate-based Quantum Computing vs Quantum Annealing
Gate-based quantum computing and quantum annealing are two approaches to implementing quantum computing. The first is universal, the second perfect for optimisation.
QC for Material Science
OpenFermion makes it easy to simulate complex quantum systems such as the Bose-Hubbard model — bridging the gap between algorithm developers and experimental physicists.