Quantum Platform Managers

• Quantum Platform Managers are middleware systems that unify quantum and classical resources to manage hybrid HPC workloads.
• Pilot-Quantum implements multi-level scheduling and plugin-based resource allocation, ensuring efficient orchestration of tasks across CPUs, GPUs, and QPUs.
• They enable advanced hybrid workflows such as variational quantum algorithms and circuit cutting, achieving significant performance speedups in practical applications.

A quantum platform manager in the context of modern quantum-HPC integration is a middleware or system abstraction that unifies provisioning, scheduling, and execution of hybrid quantum-classical workloads. Its primary function is to bridge user-facing programming models and heterogeneous infrastructure (classical CPUs/GPUs, quantum processing units—QPUs), enabling dynamic, efficient orchestration of quantum and classical computational resources under a single control plane. Pilot-Quantum is a representative implementation of this class of systems, bringing the well-established Pilot Abstraction from HPC into the quantum-classical domain, and focusing on scalable, application-level management for resource-rich, distributed, and hybrid workflows (Mantha et al., 2024).

1. Core Architecture and Components

Pilot-Quantum’s architecture is grounded in a multi-level control paradigm:

• Pilot-Manager: Centralized controller for the lifecycle and resource allocation of “pilots” (placeholder jobs with resource reservations). It handles user submissions of pilot descriptions (resources, walltimes), interacts with local schedulers (e.g., Slurm) or cloud APIs via specialized resource plugins, maintains a global queue of pending tasks, and is responsible for application-level scheduling of tasks to available pilots.
• Pilot-Agent: Daemon running within the resource allocation of a pilot, reporting its current resource inventory (cores, GPUs, QPUs), retrieving tasks from the Pilot-Manager, and executing them. It either launches tasks directly through system APIs (srun, CUDA), or delegates to an execution engine plugin (e.g., Dask for data-parallel patterns).
• Resource Plugins: System-specific adapters translating pilot descriptions to job submissions appropriate for the underlying scheduler or cloud environment.
• Execution Engine Plugins: Interface layers allowing native integration with Dask (for streaming/data-parallel), Ray (mixed CPU/GPU workloads), or native MPI/CUDA for tightly-coupled compute, abstracting over the heterogeneity of operational requirements.
• Task Manager: Accepts and tracks “task descriptions,” encapsulating per-task resource vectors and executables, and enforces placement and scheduling policies at the application level.

This layered, plugin-based architecture allows Pilot-Quantum to sit between batch schedulers and high-level workflow/programming frameworks, transparently orchestrating the sharing of classical and quantum resources.

2. Pilot Abstraction in Hybrid Quantum-Classical Scheduling

The core theoretical model builds upon the established Pilot Abstraction, formalizing a decoupling between system-level (L1) and application-level (L3) scheduling:

• Let $R = C \cup Q$ denote the union of classical and quantum resources.
• $P = \{p_1, \ldots, p_m\}$ is the set of active pilots, with each $p_i$ reserving a subset $R(p_i) \subseteq R$.
• $T = \{t_1, \ldots, t_n\}$ is the set of independent tasks, each with a resource vector $r_j = (c_j, g_j, q_j)$ specifying required cores, GPUs, and QPUs.
• Pilot capacities $\text{cap}(p_i)$ and load $\text{load}(p_i)$ are managed by the system.

The Pilot-Manager implements a greedy assignment policy: $$A(t_j) = \arg\min_{p_i} \left\{ \frac{\text{load}(p_i)}{\text{cap}(p_i)} \ \middle| \ r_j \subseteq R(p_i) \right\}$$ This approach flexibly matches incoming quantum or classical tasks to pilots with minimally loaded but compatible resources, thereby enabling adaptive utilization as pilots appear/expire and workloads change dynamically.

3. Unified Resource and API Surface

Pilot-Quantum exposes an API for unified management and transparent integration across resource modalities and frameworks:

• API: submit_pilot(pilot_description) and submit_task(task_description), where pilots and tasks declare explicit classical (cores, GPUs) and quantum (QPU backends, priority) requirements.
• Framework Integration: The Pilot-Agent uses Qiskit Providers or PennyLane plugins to submit quantum jobs (to hardware or cloud simulators), cuQuantum or Qiskit Aer for GPU or CPU simulations, and supports native numerical libraries (NumPy, PyTorch, JAX) for classical components.
• Abstraction: All computational jobs, regardless of their backend, are scheduled as “tasks,” with the Pilot-Agent ensuring that proper hardware-software bindings are respected. This abstraction unifies the submission and management of heterogeneous workloads and supports dynamic environment changes.

4. Hybrid Workflows: VQAs and Circuit Cutting

Pilot-Quantum directly enables hybrid workflow motifs critical in quantum-classical computing:

• Variational Quantum Algorithms (VQAs): Each iteration produces a sequence of tasks:
  • Quantum evaluation (expectation value measurement on QPU or simulator).
  • Classical gradient computation and optimizer steps.
  • Parameter updates.
  • Quantum tasks are scheduled to pilots with QPU (or GPU simulator) access; classical tasks are pipelined onto CPU/GPU pilots. The system exploits pipeline parallelism to overlap computation and latency.
• Circuit Cutting: Large circuits exceeding QPU capacity are partitioned into subcircuits using (e.g.) Qiskit’s circuit-cutting tools. For $k$ cuts, $S$ subexperiments are generated. Pilot-Quantum schedules all $S$ quantum sub-tasks in parallel, leveraging available QPU/simulator resources, and subsequently schedules an aggregation task for post-processing, maximizing parallelism and resource utilization.

5. Performance Benchmarks and Quantitative Results

Pilot-Quantum’s capability is substantiated by application-level benchmarks across several paradigms:

Application	Workload/Setup	Runtime/Speedup
Circuit Execution	1024 random circuits,	IonQ sim: 175–4260 s (2–28q); 87× speedup
	2–28 qubits, Ray engine	Qiskit Aer (GPU) vs IonQ sim
Distributed State Vector	31–37 qubits, adjoint gr.	678–2987 s (31–37q, 256 A100 GPUs, PennyLane)
Circuit Cutting	2 cuts/4 cuts, 34q SU2	Single-node: 58–304 s; 2.1–3.5× speedup; 5.5× w/ 16 nodes
Quantum ML	CIFAR-10 comp/class, 13q	32 nodes: 15× speedup in compression/classif.

These results demonstrate substantial speedups (3.5×–15×) versus sequential or less resource-aware baselines, with scaling limited primarily by memory footprint and task dependency structure.

6. Comparison with Other Middleware

Pilot-Quantum is contrasted with alternative quantum-HPC middleware:

System	Key Features
Qiskit Serverless	Managed cloud, Qiskit task decorator, Ray-based, no explicit resource allocation, no HPC support
Covalent	DAG abstraction (“electrons”), Dask-based, requires manual cluster setup, limited QPU prioritization
CUDA-Q	LLVM-based kernel, compiles to CPU/GPU/QPU, lacks dynamic runtime/resource scheduling
Pilot-Quantum	Multi-level scheduling, framework-agnostic, supports dynamic resizing, tightly- and loosely-coupled workflows, centralized per-task resource specification

Pilot-Quantum thus uniquely combines multi-level application-aware scheduling, resource-agnostic orchestration, and per-task granularity, positioning it as a leading open-source candidate for quantum platform management (Mantha et al., 2024).

7. Limitations and Open Challenges

Current limitations and areas for future extension include:

• Dependency Management: Lacks built-in DAG or conditional task support; dependencies must be managed externally.
• Native Quantum Tasks: Current model relies on classical drivers (Qiskit/PennyLane) for QPU access; direct submission of OpenQASM/QIR as first-class resource types remains a future goal.
• Scheduling Heuristics: Presently implements simple load-balancing; advanced policies (cost, latency, predictive) and explicit quantum-aware queueing are open fields.
• Integration with Workflow Engines: Tight coupling with DAG-based engines (e.g., Airflow, Nextflow), and predictive resource allocation models (“Q-Dreamer”) are planned.
• QPU Co-scheduling: Further development of prioritization and arbitration when multiple pilots compete for limited quantum hardware.

These limitations highlight the evolving nature of quantum platform management as system complexity and workload diversity increase.

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Overall, Pilot-Quantum and comparable quantum platform managers establish a general, extensible software substrate for orchestrating hybrid quantum-classical computation in HPC and distributed environments, enabling reproducible, high-performance workflows across physical and simulated quantum resources (Mantha et al., 2024).