Research projects and notes at the intersection of quantum computing, tensor networks, machine learning, and simulation.
The current site uses compact research summaries. Future updates can add MDX notes, experiment logs, derivations, and reproducible notebooks.
Exploring compact tensor-network representations for supervised learning and interpretable model structure.
Research question
Can tensor network architectures provide useful inductive bias for small-scale image classification while remaining interpretable and computationally controlled?
Methods
Status: Active research notes
Notes and experiments around quantum feature maps, state preparation, and QML model behavior.
Research question
How do state preparation choices influence quantum machine learning experiments, and where do simple baselines reveal the real difficulty?
Methods
Status: Experiment planning and note consolidation
Research notes on imaginary-time evolution, simulation workflows, and numerical behavior.
Research question
How can double-bracket and QITE-style methods be organized into clear, testable simulation workflows?
Methods
Status: Notes in progress