Charlot Lab · Quantum information at the edge · tensor-network compression
A policy's weights, like an entangled quantum state, need not be stored in full if their structure is low. A tensor network (here the simplest one, a rank-r factorization) writes a big weight as small cores. This trains a little control policy live in your browser, then asks the question two ways. Compress-after (red): squeeze the finished dense policy to rank r. Train-compact (teal): train the policy in factored form from the start. Slide the rank and watch which one keeps the policy. No quantum computer, the classical mathematics of entanglement, run on your own device.
What you are seeing. The curves are measured live, not asserted. Compress-after stays low at every rank because a from-scratch dense weight sits near its high-rank random start, so truncating it throws the policy away. Train-compact jumps to full accuracy at a small rank, because the control function itself is low-complexity and the factored form has room to fit it. That is the whole deployable lesson: train the tensor-network form, don't squeeze a dense one (on a pretrained model, truncate then fine-tune). The same result at scale is what the tensorization literature reports. Offline in the lab's research record: a pure-Rust factored kernel that cuts the on-device multiply count by the same factor, and this exact bench in Python. More: Quantum information at the edge.