Language models for quantum simulation

Shuangxiang Zhou, Ronghang Chen, Zheng An +2 more

Ahmed S. Tolba, Magdi Z. Rashad, Mohamed A. El-dosuky

Quantum computing is a promising approach of computation that is based on equations from Quantum Mechanics. A simulator for quantum algorithms must be capable of performing heavy mathematical matrix transforms. The design of the simulator itself takes one of three forms: Quantum Turing Machine, Network Model or circuit model of connected gates or, Quantum Programming Language, yet, some simulators are hybrid. We studied previous simulators and then we adopt features from three simulators of different implementation languages, different paradigms, and for different platforms. They are Quantum Computing Language (QCL), QUASI, and Quantum Optics Toolbox for Matlab 5. Our simulator for quantum algorithms takes the form of a package or a programming library for Quantum computing, with a case study showing the ability of using it in the circuit model. The .NET is a promising platform for computing. VB.NET is an easy, high productive programming language with the full power and functionality provided by the .NET framework. It is highly readable, writeable, and flexible language, compared to another language such as C#.NET in many aspects. We adopted VB.NET although its shortage in built-in mathematical complex and matrix operations, compared to Matlab. For implementation, we first built a mathematical core of matrix operations. Then, we built a quantum core which contains: basic qubits and register operations, basic 1D, 2D, and 3D quantum gates, and multi-view visualization of the quantum state, then a window for demos to show you how to use and get the most of the package.

Jingfei Li, Peng Zhang, Dawei Song +1 more

Xindian Ma, Xinyu Long, Yefei Zhang +11 more

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.

Mohamed Eldosuky

Quantum computing is a promising approach of computation that is based on equations from Quantum Mechanics and nanotechnology. Understanding and simulating quantum operations on a classical computer has different approaches and is modeled in different ways. However, the most repute ways are Quantum Turing Machine, Network Model or circuit model of connected gates and Quantum Programming Language. Many simulators do exist that varies in many features such as implementation languages, target platforms, paradigms, visuality, and applied quantum algorithms. There was a developmental trade-off between the simplicity and the functionality of the simulator. The goals and research activities are outlined as follows: First, surveying the quantum computing world, its characteristics, issues, problems, and challenges. Second, surveying the existing quantum simulators, illustrating representative simulators of major approaches, their features, and functionalities. Third, introducing a framework to provide the power of quantum computing , while insisting the simplicity and high usability. Finally, providing a case study for a new quantum algorithm based on the introduced framework.

Joseph C. H. Chen