An independent research notebook
Quantum computing × machine learning
The intersection is
where it gets interesting.
Two fields. Two directions. A closer look at the methods, experiments and questions connecting them. In scope: only work where machine learning and quantum computing meet.
Choose a direction
Learning to understand
quantum systems.
Control, decoding and discovery. Follow the ways machine learning enters the quantum research workflow.
Explore AI for quantum ↗02 / QUANTUM FOR AIQuantum methods.
Learning problems.
Kernels, models and generative tasks. Read each proposed advantage in the context of the problem it addresses.
Explore quantum for AI ↗Across the interface
Follow the method.
Keep the conditions.
The interesting question is not simply whether something is faster. It is what was compared, under which assumptions, and at what cost.
The baseline for every note
A result needs
its context.
- The task
- What question does the work actually address?
- The comparison
- Which classical or quantum baseline is used?
- The resources
- What data, compute and measurements are assumed?
- The evidence
- Theory, simulation, experiment or reproduced run?
The first research notes are being prepared. I’m Gauthier Thiry, an independent writer exploring this intersection to help curious technical readers stay up to date.
About the writer