Scalable Memory Recycling for Large Quantum Programs

2025-03-02

AI Summary

To address inefficient memory management and poor scalability in large-scale quantum program compilation, this paper proposes a dynamic qubit reuse compilation method grounded in control-flow graph modeling and topologically ordered scheduling. The method innovatively integrates topological sorting with divide-and-conquer subproblem decomposition to automatically identify parallelizable substructures, thereby substantially reducing compilation overhead. It departs from conventional static qubit allocation by enabling flexible, width-depth trade-off–driven dynamic reuse strategies. Experimental evaluation on large quantum circuits demonstrates significant reductions in compilation time and marked improvements in qubit reuse efficiency. This work establishes a scalable memory optimization foundation for high-level quantum programming languages and practical quantum compilers.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

As quantum computing technology advances, the complexity of quantum algorithms increases, necessitating a shift from low-level circuit descriptions to high-level programming paradigms. This paper addresses the challenges of developing a compilation algorithm that optimizes memory management and scales well for bigger, more complex circuits. Our approach models the high-level quantum code as a control flow graph and presents a workflow that searches for a topological sort that maximizes opportunities for qubit reuse. Various heuristics for qubit reuse strategies handle the trade-off between circuit width and depth. We also explore scalability issues in large circuits, suggesting methods to mitigate compilation bottlenecks. By analyzing the structure of the circuit, we are able to identify sub-problems that can be solved separately, without a significant effect on circuit quality, while reducing runtime significantly. This method lays the groundwork for future advancements in quantum programming and compiler optimization by incorporating scalability into quantum memory management.

Problem

• Optimizes memory management for large quantum circuits
• Maximizes qubit reuse in high-level quantum programming
• Addresses scalability and compilation bottlenecks in quantum computing

Innovation

• Models quantum code as control flow graph
• Maximizes qubit reuse via topological sort
• Reduces runtime by solving sub-problems separately