Ecosystem-Agnostic Standardization of Quantum Runtime Architecture: Accelerating Utility in Quantum Computing

TL;DR

This paper proposes a standardized, flexible quantum runtime architecture to enhance quantum computing utility by enabling better integration, programmability, and scalability across diverse hardware and hybrid systems.

Contribution

It introduces an ecosystem-agnostic quantum runtime architecture that supports flexible programming models and distributed execution, addressing current limitations in quantum computing middleware.

Findings

Proposes a standardized quantum runtime architecture for better hardware integration.

Enables flexible programming and scheduling for hybrid quantum-classical systems.

Facilitates open-source community adoption and distributed deployment.

Abstract

Fault tolerance is a long-term objective driving many companies and research organizations to compete in making current, imperfect quantum computers useful - Quantum Utility (QU). It looks promising to achieve this by leveraging software optimization approaches primarily driven by AI techniques. This aggressive research covers all layers of Quantum Computing Optimization Middleware (QCOM) and requires execution on real quantum hardware (QH). Due to the nascent nature of the technology domain and the proprietary strategies of both large and small players, popular runtimes for executing quantum workloads lack flexibility in programming models, scheduling, and hardware access patterns, including queuing, which creates roadblocks for researchers and slows innovation. These problems are further exacerbated by emerging hybrid operating models that place Graphical Processing Unit (GPU) supercomputing and Quantum Intermediate Representation (QIR) at the heart of real-time computations across quantum and distributed resources. There is a need for a widely adopted runtime platform (RP) driven by the open-source community that can be easily deployed to work in a distributed manner between Quantum Processing Unit (QPU), GPU, control hardware, external compute resources and provide required flexibility in terms of programming & configuration models.