Hamilton-Zero: A Neural Foundation Model for Solving Quantum Ground States
burny_tech · x · 2026-08-16
The paper introduces Hamilton-Zero, a neural network foundation model with 0.5B parameters. Pre-trained on vast Hamiltonian systems data (varying topology, size, interaction types), it aims to solve for ground states of arbitrary quadratic qubit Hamiltonians, a frequent task in computational quantum physics.
More from Research
- Study: Capabilities outside training scope hard to recover; K-5 model generalization limited — paulnovosad · 2026-08-16
- Exploring Visual Model Fingerprinting via Tensor Scanning — Glad_Contest_8014 · 2026-08-16
- Ofir Press: The industry is now driven by evals — OfirPress · 2026-08-16
- Study finds forced experience consolidation degrades LLM agent memory — _AndrewZhao · 2026-08-16
- Paper: Gender-Associated Linguistic Bias in LLMs — sbulaev · 2026-08-16
- CUHK's VideoCoCo: executable code as CoT lifts VBench-2.0 average by 25.7 points — 机器之心 · 2026-08-16