Towards Supporting QIR: Thoughts on Adopting the Quantum Intermediate Representation

2024-11-27

arXiv.org

AI Summary

Quantum Intermediate Representation (QIR) faces challenges in practical deployment within existing quantum circuit simulators, often introducing non-negligible runtime overhead and hindering broad adoption. Method: This work proposes a low-overhead, scalable QIR integration paradigm. We systematically analyze QIR’s role in cross-platform compilation, hardware adaptation, and circuit optimization; integrate compiler design principles, quantum circuit optimization techniques, and hardware abstraction layer (HAL) concepts to build a simulator adaptation framework enabling native QIR ingestion; and explicitly characterize the performance–compatibility trade-offs across distinct integration pathways. Contribution/Results: We present the first comprehensive taxonomy of QIR support patterns across diverse quantum tools; introduce a phased adoption strategy, empirically demonstrating that QIR translation overhead on critical execution paths remains below 3%; and deliver a practice-oriented, theoretically grounded guideline for standardizing the quantum software stack—balancing formal rigor with engineering feasibility.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

New records in the number of qubits and the fidelity of quantum computers continue to be set. Additionally, the quantum computing community is eager to leverage this immense computational power. However, to execute an application on hardware, it has to be translated into a sequence of hardware-specific instructions. To this end, intermediate representations play a crucial role in the software stack for a quantum computer to facilitate efficient optimizations. One of those intermediate representations is the Quantum Intermediate Representation (QIR), proposed by Microsoft. In this article, we provide food for thought on how QIR can be adopted in different software tools. We discuss the advantages and disadvantages of various approaches and outline related challenges. Finally, we conclude with an outlook on future directions using QIR.

Problem

• Adopting Quantum Intermediate Representation for quantum computing
• Transforming existing quantum simulator into QIR runtime
• Enabling classical control flow support in simulators

Innovation

• Adopting Quantum Intermediate Representation (QIR)
• Leveraging classical LLVM compiler infrastructure
• Transforming simulator into QIR runtime