Quantum Resource Management Interface (QRMI)

• Quantum Resource Management Interface (QRMI) is a middleware abstraction that unifies quantum and classical computing resources through a standardized, vendor-neutral API.
• It employs multi-layer scheduling and task decomposition to efficiently orchestrate hybrid workflows across QPUs, CPUs, and GPUs.
• QRMI integrates observability and telemetry tools, enhancing real-time monitoring, resource optimization, and reproducibility in complex computing environments.

A Quantum Resource Management Interface (QRMI) provides a unified abstraction and operational control layer for managing quantum computing resources—physical and logical—within classical and hybrid high-performance computing environments. QRMI architectures enable seamless orchestration, scheduling, and monitoring of quantum resources (quantum processing units, or QPUs) alongside classical resources (CPUs, GPUs), facilitating efficient execution of hybrid workflows, maximizing usability and portability, and improving observability and system administration.

1. Architectural Foundations and Abstractions

QRMI architectures are designed as abstraction layers—often implemented as middleware libraries or runtime systems—that decouple application logic and workflow management from hardware-specific details. The primary role of the QRMI is to expose a standardized and vendor-neutral API for the acquisition, scheduling, execution, monitoring, and release of quantum resources, treating QPUs as first-class scheduling entities on par with classical resources (Sitdikov et al., 11 Jun 2025, Wennersteen et al., 24 Sep 2025).

Layered approaches are prominent. For instance, in Pilot-Quantum, the system is structured into four conceptual layers: the Resource Layer (L1) abstracts heterogeneous infrastructures including CPUs, GPUs, and QPUs; the Task Layer (L2) manages operational execution; the Workload Layer (L3) orchestrates sets of interdependent tasks using various parallelism strategies; and the Workflow Layer (L4) encapsulates high-level application logic and dependencies (Mantha et al., 24 Dec 2024).

A representative schematic for resource management is: $$\text{Pilot‑Manager} \rightarrow \begin{bmatrix} \text{Pilot}_1 \ \text{Pilot}_2 \ \vdots \ \text{Pilot}_n \end{bmatrix} \rightarrow \text{Task/Workload Execution}$$

Here, the Pilot‑Manager centrally governs resource allocation by dynamically deploying pilots (placeholder jobs) on classical and quantum resources, abstracting over direct resource acquisition and scheduling.

2. Resource Management Principles and API Design

QRMI encapsulates several critical principles:

• Resource Uniformity and API Standardization: QRMI provides a consistent method for expressing resource requirements, querying resource availability, and managing job life cycles regardless of vendor or hardware type. In practice, APIs typically include methods such as is_accessible, acquire, release, task_start, and task_result, providing compositionality across resource types (Sitdikov et al., 11 Jun 2025).
• Vendor-Neutral Integration: QRMI abstracts backend specifics—quantum hardware can be local or cloud-based, with access mediated through provider APIs (e.g., Qiskit, Pulser, PennyLane), Slurm Generic RESource (GRES) objects, or direct device APIs (Wennersteen et al., 24 Sep 2025).
• Resource Scheduling Coordination: QRMI operates either as a plug-in or a dedicated interface to the host resource manager (e.g., Slurm), enabling quantum jobs to be scheduled, queued, and executed under the same system as classical jobs without user-side distinction. This enables portable, reproducible workflow execution across environments (Sitdikov et al., 11 Jun 2025, Wennersteen et al., 24 Sep 2025).

Operationally, a job submission follows a standardized pattern: $$\text{Acquire Resource} \rightarrow \text{Execute Quantum Task} \rightarrow \text{Release Resource}$$

This ensures atomicity and prevents resource contention.

3. Multi-Layered Scheduling, Workflows, and Hybrid Execution

Advanced QRMI implementations employ a multi-layered scheduling stack:

• Two-layer Scheduling: Beyond the principal HPC scheduler (e.g., Slurm), a QRMI-enabled middleware daemon may introduce a second scheduling layer. This supports advanced functionality such as multi-user session management, fine-grained priority queues (preempting low-priority jobs), and pattern-aware scheduling to optimize QPU utilization by classifying jobs, e.g., as “qc-balanced” for quantum-classical balance (Wennersteen et al., 24 Sep 2025).
• Task Decomposition and Workload Orchestration: QRMI manages the decomposition of complex hybrid or quantum algorithms into smaller, parallelizable tasks. These may include independent circuit evaluations, distributed circuit “cutting” (partitioning into subcircuits), variational algorithm subroutines, or distributed statevector simulations (Mantha et al., 24 Dec 2024). The middleware can assign these subtasks across available QPUs, CPUs, and GPUs, leveraging suitable parallelism.
• Multi-SDK Interoperability: QRMI shields users and applications from the heterogeneity of programming SDKs (Qiskit, Pulser, PennyLane), exposing a uniform programming and execution interface even when multiple frameworks are used within the same workflow (Wennersteen et al., 24 Sep 2025).

4. Observability, Monitoring, and Telemetry

A foundational aspect of QRMI is the promotion of robust monitoring and administrative observability:

• Telemetry Integration: QRMI-based environments collect device-level and job-level metrics (e.g., qubit coherence times, calibration drift, per-job fidelity) and expose this data via standard observability stacks—Prometheus (for real-time scraping), InfluxDB (for time-series storage), and Grafana (for dashboard visualization) (Wennersteen et al., 24 Sep 2025).
• Administrative Access and Maintenance: The monitoring pipeline supports tracking QPU health, alerting on performance degradation, assisting in maintenance scheduling, and providing metadata useful for debugging and workflow optimization.
• Job Metadata Exposure: By capturing and transmitting per-job and per-qubit telemetry, end users can analyze run reliability, infer calibration boundary effects, and retroactively optimize workload scheduling.

Sample observability pipeline: $$\text{Telemetry Data} \longrightarrow \text{InfluxDB} \longrightarrow \begin{array}{c} \text{Prometheus} \ \downarrow \ \text{Grafana Dashboard} \end{array}$$

5. Portability, Interoperability, and System Usability

QRMI delivers substantial improvements in system portability and cross-ecosystem compatibility:

• Seamless Workflow Portability: Applications leveraging QRMI can be developed on local simulators, validated, and subsequently deployed on real QPUs by modifying a runtime resource parameter (e.g., --qpu=<resource>), ensuring high cross-environment reproducibility and easing the transition from development to production (Wennersteen et al., 24 Sep 2025).
• SDK and Backend Selection Abstraction: By decoupling backend and SDK specifics, QRMI enables users to target physical or simulated quantum resources without modifying workflow logic or code base, facilitating experimentation and resource benchmarking.

6. Benefits and Limitations

The QRMI paradigm offers several system-level benefits:

• Unified Scheduling: Eliminates the need for users and administrators to juggle separate scheduling systems for classical and quantum workloads.
• Optimized Resource Utilization: The middleware can reduce QPU idle time and ensure optimal allocation, especially vital for expensive and frequently oversubscribed quantum hardware (Mantha et al., 24 Dec 2024, Wennersteen et al., 24 Sep 2025).
• Reproducibility and Modularity: Programs and workflows can be reliably reproduced across heterogeneous environments; system upgrades or hardware changes can be absorbed by the abstraction without affecting the user interface (Sitdikov et al., 11 Jun 2025).

However, certain limitations and ongoing challenges are highlighted:

• Platform Heterogeneity: Coordination of access, scheduling, and telemetry collection across diverse infrastructure (cloud-based QPUs, local QPUs, various SDKs) remains a complex systems challenge (Mantha et al., 24 Dec 2024).
• Workflow Complexity: Managing application-level dependencies (e.g., Directed Acyclic Graph scheduling for task dependencies), predictive resource allocation, and multi-level scheduling requires further development and the integration of advanced orchestration algorithms.
• Security and Access Control: Proper access control for multi-user environments must be tightly integrated into the QRMI abstraction, particularly as hybrid environments scale up.

7. Representative Use Cases

QRMI frameworks have been demonstrated in various hybrid quantum–classical settings, including:

Use Case	Description	System Features
Quantum circuit cutting	Partitioning and parallel execution of large circuits into multiple subcircuits	Task scheduling, parallel subtask exec.
Distributed statevector simulation	Simulation of high-qubit states across many GPUs/CPUs	Workload decomposition, resource scaling
Quantum machine learning	Hybrid pipelines with classical pre/post-processing and quantum encoding/training	Multi-resource orchestration, modularity
Multi-SDK environment	Single QPU access via Qiskit, Pulser, or PennyLane	SDK abstraction layer, interop.

Empirical evaluations demonstrate substantial throughput and scaling improvements, often quantifying speedup ratios and demonstrating that modular resource allocation aligns well with both quantum and classical workload partitioning (Mantha et al., 24 Dec 2024, Wennersteen et al., 24 Sep 2025).

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In sum, the Quantum Resource Management Interface establishes a foundational architectural layer for unifying, abstracting, and orchestrating quantum resources within classical or hybrid computing environments. QRMI enables system-level advancements in resource utilization, workflow portability, observability, and multi-SDK interoperability, catalyzing the development of reliable and scalable quantum-classical applications in state-of-the-art research and production contexts.