Towards Quantum Software for Quantum Simulation

2025-11-17

AI Summary

Current quantum simulation is hindered by the lack of a general-purpose software infrastructure—particularly in model specification, Hamiltonian construction, and hardware-aware mapping—limiting cross-platform deployment and scalability. To address this, we propose a unified framework based on modular Model-Driven Engineering (MDE), the first to integrate physical model definition, automated Hamiltonian generation, and hardware-adaptive mapping at a single abstraction level. The framework supports both digital and analog simulation modalities, enables automatic code generation, performance evaluation, and component reuse. We validate it on a representative high-energy physics use case, demonstrating an end-to-end, cross-platform quantum simulation workflow prototype. Our framework provides a scalable, reusable architectural blueprint for the quantum simulation software stack, significantly improving development efficiency and system interoperability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Quantum simulation is a leading candidate for demonstrating practical quantum advantage over classical computation, as it is believed to provide exponentially more compute power than any classical system. It offers new means of studying the behaviour of complex physical systems, for which conventionally software-intensive simulation codes based on numerical high-performance computing are used. Instead, quantum simulations map properties and characteristics of subject systems, for instance chemical molecules, onto quantum devices that then mimic the system under study. Currently, the use of these techniques is largely limited to fundamental science, as the overall approach remains tailored for specific problems: We lack infrastructure and modelling abstractions that are provided by the software engineering community for other computational domains. In this paper, we identify critical gaps in the quantum simulation software stack-particularly the absence of general-purpose frameworks for model specification, Hamiltonian construction, and hardware-aware mappings. We advocate for a modular model-driven engineering (MDE) approach that supports different types of quantum simulation (digital and analogue), and facilitates automation, performance evaluation, and reusability. Through an example from high-energy physics, we outline a vision for a quantum simulation framework capable of supporting scalable, cross-platform simulation workflows.

Problem

• Lack general-purpose frameworks for quantum simulation models
• Absence of software infrastructure for Hamiltonian construction
• Need hardware-aware mappings for scalable quantum simulation

Innovation

• Modular model-driven engineering for quantum simulation
• General-purpose frameworks for Hamiltonian construction
• Hardware-aware mappings supporting digital and analogue simulations