Quantization as an
Eigenvalue AI Problem
braketlab.io is a Neolab dedicated to decoding the intrinsic soul of matter. Through MoC-JEPA and Qiankun Net, we transform quantum many-body complexity into linear-time discovery.
02 — The Index Wall
Where complexity meets a linear horizon.
Classical algorithms collapse under exponential cost. MoC-JEPA holds the floor — achieving Schrödinger-level accuracy at a fraction of the cost as systems scale past 10⁴ atoms.
03 — Dual Engines
Two networks. One programmable universe of matter.
MoC-JEPA
The Equivariant World Model. Based on the Quantum Onion Model, MoC-JEPA (Moment-driven Charge Density Joint Embedding Predictive Architecture) projects massive charge-density data into atom-centered equivariant manifolds — letting physical laws emerge in latent space. The result: O(N) linear complexity for systems exceeding 10⁴ atoms.
Qiankun Net
The High-Fidelity Data Backbone. Integrating supercomputing-scale wavefunction data, Qiankun Net provides the high-precision ground truth required to anchor our foundation models with sub-chemical accuracy.