RiverONE: Generating Knowledge-Intensive VLM by Simulated Quantum Machines
This work demonstrates that simulated quantum computation can be used as a construction-stage mechanism to create compact, knowledge-intensive VLMs for specialized scientific domains, though the application is narrow.
RiverONE is a lightweight vision-language model (1.9B parameters) for quantum calibration plot understanding, built using simulated quantum computation during training. It achieves at least 95% of the performance of a much larger model (NVIDIA Ising Calibration 1) while using less than 10% of its parameter count.
Quantum computing provides a powerful paradigm for representing and transforming high-dimensional information through superposition, entanglement, and measurement-induced nonlinear features. While current quantum hardware is not yet practical for direct large-scale vision-language model (VLM) inference, simulated quantum computation can be used during model construction to generate structured parameters for compact classical AI systems. We build RiverONE, a lightweight vision-language model for quantum calibration plot understanding, using simulated quantum computation. It employs a specialized visual encoder and an InternVL-based language backbone. To compensate for compression-induced information loss, we introduce quantum-generated parameters, which are materialized as classical tensors after training. This allows RiverONE to run entirely on classical GPUs at inference time, with no quantum hardware or runtime quantum simulation. With approximately 1.9 billion parameters, RiverONE achieves at least 95\% of the performance of NVIDIA Ising Calibration 1 on quantum calibration plot understanding tasks while using less than 10\% of its parameter count. These results suggest that simulated quantum computation can serve as a practical construction-stage mechanism for building lightweight, knowledge-intensive scientific VLMs. Our code is available at https://github.com/THeWakeSystems/RiverOne.