Orchestrating the Quantum-Classical Divide

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Author: Denis Avetisyan

A new framework efficiently manages workloads between high-performance computers and quantum processors by treating fragmented quantum circuits as independent tasks.

A runtime-oriented execution model decouples circuit cutting from orchestration through fragment descriptors, facilitating hardware-aware and policy-driven scheduling across diverse high-performance computing and quantum computing backends-an approach detailed by three key contributions to the system.
A runtime-oriented execution model decouples circuit cutting from orchestration through fragment descriptors, facilitating hardware-aware and policy-driven scheduling across diverse high-performance computing and quantum computing backends-an approach detailed by three key contributions to the system.

DQR decouples quantum circuit cutting from HPC orchestration, enabling efficient hybrid execution and improved resource management for fault-tolerant quantum-classical computations.

Existing frameworks tightly couple quantum circuit decomposition with high-performance computing (HPC) orchestration, hindering the application of mature resource management policies to near-term quantum workloads. This paper introduces ‘Wave-Based Dispatch for Circuit Cutting in Hybrid HPC–Quantum Systems’, presenting DQR, a runtime framework that decouples circuit cutting from execution, treating fragments as independent, schedulable units. DQR achieves pipeline concurrency through a wave-based coordinator and demonstrates transparent failover recovery-rerouting tasks between on-premises and cloud-based quantum processing units (QPUs)-without pipeline restarts. Will this approach unlock scalable and robust hybrid quantum-classical computation within existing HPC infrastructures and accelerate the adoption of heterogeneous quantum backends?

Navigating the Quantum Bottleneck: Constraints and Opportunities

The allure of quantum computation lies in its potential for exponential speedups over classical algorithms, promising breakthroughs in fields like materials science and drug discovery. However, realizing this potential is currently constrained by the realities of existing quantum hardware, often referred to as ‘Noisy Intermediate-Scale Quantum’ (NISQ) devices. These systems are characterized by a limited number of qubits – the quantum equivalent of bits – and, crucially, by their susceptibility to decoherence. Decoherence refers to the loss of quantum information due to interactions with the environment, effectively introducing errors into calculations. While increasing qubit counts is a primary focus, maintaining qubit coherence for sufficient durations to perform complex operations remains a formidable challenge, hindering the development and practical application of quantum algorithms. This combination of limited scale and inherent noise creates a significant bottleneck, demanding innovative error mitigation strategies and novel hardware architectures to unlock the full power of quantum computing.

The challenge of verifying and refining quantum algorithms is fundamentally constrained by the limitations of classical computation. As quantum systems grow – even to sizes considered ‘moderate’ with only a few dozen qubits – the computational resources required to accurately simulate their behavior on conventional computers increase exponentially. This means that validating new quantum algorithms, or even understanding the intricacies of existing ones, rapidly becomes practically impossible. Researchers are therefore faced with a significant bottleneck; without the ability to comprehensively test algorithms on classical hardware, progress in quantum algorithm development is severely hampered, and the potential for discovering and implementing truly transformative quantum solutions is diminished. This limitation drives the need for alternative validation strategies, such as utilizing smaller, manageable quantum systems or developing novel error mitigation techniques.

Recognizing the immediate constraints of current quantum hardware, researchers are increasingly focused on hybrid algorithms that strategically partition computational tasks. These approaches acknowledge that classical computers remain superior for certain operations – such as data pre- and post-processing, and control flow – while quantum processors excel at specific subroutines like solving linear equations or simulating quantum dynamics. By carefully distributing the workload, these hybrid methods aim to circumvent the limitations of small qubit counts and short coherence times, enabling practical applications even with near-term quantum devices. This synergistic combination allows for the validation of quantum algorithms using classical resources and opens pathways to tackle complex problems that are intractable for either system alone, representing a crucial step towards fault-tolerant, scalable quantum computation.

Circuit cutting strategies, utilizing either wire severing (<span class="katex-eq" data-katex-display="false">8k8^{k}</span> subcircuits) or gate decomposition (<span class="katex-eq" data-katex-display="false">6k6^{k}</span> variants) into parallelizable subcircuits, enable efficient quantum circuit execution via tensor reconstruction.
Circuit cutting strategies, utilizing either wire severing (8k8^{k} subcircuits) or gate decomposition (6k6^{k} variants) into parallelizable subcircuits, enable efficient quantum circuit execution via tensor reconstruction.

Deconstructing Complexity: A Parallelization Strategy

Circuit cutting is a decomposition technique addressing the scalability limitations of quantum computation by dividing a large, complex quantum circuit into smaller, independent fragments. This fragmentation allows for the parallel execution of these sub-circuits, potentially reducing overall computation time and resource requirements. The process involves identifying sections of the circuit that can be computed separately without violating data dependencies, effectively transforming a single, monolithic circuit into a collection of smaller tasks. This approach is particularly beneficial for circuits exceeding the qubit count or connectivity limitations of available quantum processing units (QPUs).

Parallel execution of circuit fragments, enabled by circuit cutting, directly addresses resource utilization inefficiencies inherent in sequential quantum computation. Decomposing a large circuit allows multiple, independent fragments to be processed concurrently, increasing throughput and reducing total execution time. This is particularly impactful given the limited availability and high cost of quantum processing units (QPUs); parallelization minimizes the duration for which QPU resources are required. Furthermore, the approach facilitates the effective use of high-performance computing (HPC) resources for tasks such as fragment compilation, optimization, and classical data processing associated with quantum measurement results, thereby distributing the computational load and maximizing overall system efficiency.

Effective circuit cutting relies on a detailed analysis of dependencies between circuit fragments and the associated communication costs. The ‘FragmentDescriptor’ is a data structure that encapsulates this information, specifying which fragments must complete before others can begin, and quantifying the classical data transfer required between fragments during execution. Minimizing communication overhead-specifically, the volume of data moved between fragments and the latency of those transfers-is crucial for realizing performance gains from parallel execution. A well-defined ‘FragmentDescriptor’ enables efficient scheduling of fragments across available ‘HPCResources’ and ‘QPUResources’, preventing bottlenecks and maximizing overall circuit execution speed.

Circuit cutting, while leveraging quantum processing units (QPUs) for quantum computations, is fundamentally a hybrid approach requiring substantial classical high-performance computing (HPC) resources. The decomposition of large circuits into fragments, the scheduling of those fragments for parallel execution, and the collation of results all necessitate classical processing. Specifically, the ‘FragmentDescriptor’ – which details dependencies and communication needs – is managed and interpreted using classical HPCResources. Data transfer between the QPU and classical memory, as well as classical post-processing of quantum measurement outcomes, further relies on robust HPC infrastructure. Therefore, efficient circuit cutting is not solely dependent on QPU availability but critically relies on the co-availability and performance of both quantum and classical computing resources.

The DQR framework utilizes a three-layer architecture-circuit cutting, runtime orchestration, and execution-to efficiently distribute and manage quantum and classical computations across diverse hardware resources via GPFS and MPI.
The DQR framework utilizes a three-layer architecture-circuit cutting, runtime orchestration, and execution-to efficiently distribute and manage quantum and classical computations across diverse hardware resources via GPFS and MPI.

Orchestrating Hybrid Execution: The DynamicQueueRouter

The DynamicQueueRouter establishes a runtime environment that separates the process of dividing a quantum circuit into executable fragments from the overall management of high-performance computing (HPC) resources. This decoupling allows for independent optimization of circuit partitioning and task scheduling, improving system flexibility and scalability. By abstracting circuit cutting logic from the HPC orchestration layer, the framework enables dynamic adaptation to varying cluster conditions and workload demands, facilitating efficient execution of hybrid quantum-classical algorithms across heterogeneous computing platforms. This separation is key to enabling efficient distribution of quantum workloads and maximizing resource utilization.

WaveBasedDispatch is a scheduling and execution methodology integral to the DynamicQueueRouter. It operates by dividing quantum circuits into executable fragments and dispatching them for heterogeneous processing. Efficiency is achieved through non-blocking polling, where the system continuously checks for completed fragments without halting execution, and pipeline concurrency, enabling multiple fragments to be processed simultaneously across available resources. This approach minimizes idle time and maximizes throughput by overlapping communication and computation, facilitating a more responsive and scalable execution environment for hybrid quantum-classical workloads.

The DynamicQueueRouter integrates with established high-performance computing (HPC) infrastructure via Slurm, a widely used workload manager, to dynamically allocate computational resources for executing quantum circuit fragments. This integration enables the system to request and utilize available CPU cores as needed, facilitating scalable execution of hybrid quantum-classical workflows. Furthermore, the system leverages Qdislib, a library designed for scalable quantum circuit distribution and execution, to manage the dispatch and processing of these fragments across the allocated resources. Qdislib’s capabilities are crucial for handling the parallel execution of numerous circuit segments and efficiently coordinating the overall computation.

Performance evaluations of the DynamicQueueRouter demonstrate a 1.11x speedup over a standard, monolithic CPU baseline when executing hybrid quantum-classical workflows. This improvement validates the framework’s capacity for scalable heterogeneous scheduling of quantum circuit fragments across diverse computational resources. The observed speedup is a direct result of the system’s ability to efficiently distribute and concurrently execute quantum computations, leveraging both quantum processing units (QPUs) and classical high-performance computing (HPC) infrastructure. These results confirm that decoupling circuit cutting from HPC orchestration enables optimized resource utilization and reduced overall execution time for complex algorithms.

DQR policies A-D demonstrate significant makespan reductions on the 32-qubit HEA circuit, with the hatched portion representing the DQR-optimized time and consistently outperforming the <span class="katex-eq" data-katex-display="false">56.2</span> second CPU baseline.
DQR policies A-D demonstrate significant makespan reductions on the 32-qubit HEA circuit, with the hatched portion representing the DQR-optimized time and consistently outperforming the 56.2 second CPU baseline.

Expanding the Frontiers of Scalability with Hybrid Algorithms

The current generation of quantum computers, known as Noisy Intermediate-Scale Quantum (NISQ) devices, face inherent limitations in both the number of qubits and the depth of circuits they can reliably execute. To fully leverage the potential of these systems, researchers are increasingly focused on Hybrid Quantum-Classical Algorithms. These algorithms strategically partition computational tasks, assigning portions best suited for quantum processing – such as exploiting superposition and entanglement – while delegating the remaining calculations to classical computers. This division allows problems exceeding the capacity of either system alone to be tackled, effectively circumventing qubit constraints and depth limitations. By intelligently combining the strengths of both quantum and classical computation, hybrid approaches unlock access to more complex simulations and optimization problems, paving the way for practical quantum advantage even with near-term hardware.

The framework significantly advances the utility of established quantum computational techniques, notably Variational Quantum Algorithms and Trotterization. These methods, while powerful in theory, often face limitations on Near-Intermediate Scale Quantum (NISQ) devices due to qubit constraints and circuit depth. This new approach extends their capabilities by strategically partitioning complex quantum circuits into smaller, manageable fragments. By optimizing the execution of these fragments and minimizing the communication overhead between them, the framework allows for the simulation of larger and more intricate quantum systems than previously possible. This extension not only increases the scale of solvable problems but also improves the overall efficiency and accuracy of these core quantum algorithms, paving the way for practical applications in fields like materials science and drug discovery.

The pursuit of enhanced efficiency in quantum computations increasingly relies on synergistic combinations of algorithmic techniques. Specifically, methods like ‘CircuitKnitting’ and ‘TensorNetworks’ are being integrated with ‘circuit cutting’ to optimize quantum circuit execution. Circuit cutting strategically divides larger circuits into smaller, manageable fragments, while TensorNetworks provide a compact representation of quantum states, reducing computational demands. CircuitKnitting then intelligently reconnects these fragments, minimizing the introduction of extraneous quantum gates and preserving computational fidelity. This combined approach not only addresses the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices-which struggle with long and complex circuits-but also demonstrates a measurable reduction in overhead, as evidenced by recent systems achieving a coordination overhead of just 5% of the total DQR-time with a 9.1-second reduction in coordination time when utilizing 2,592 fragments across 193 MPI ranks.

The system’s ability to effectively distribute computational workload across multiple processing units is demonstrated through a substantial benchmark involving 2,592 fragments and utilizing 193 MPI ranks. This distributed approach yields a remarkably low coordination overhead, measured at only 5% of the total Dynamic Quantum Recirculation (DQR)-time. Crucially, this represents a significant optimization, achieving a 9.1-second reduction in coordination overhead compared to less efficient methods. This improvement highlights the scalability of the hybrid algorithm framework, enabling complex quantum computations to be executed with minimized communication delays and maximized resource utilization – a critical factor for tackling increasingly challenging problems on near-term quantum hardware.

The total run time of <span class="katex-eq" data-katex-display="false">L=2</span> seconds is decomposed into 76.7 seconds (40%) for the THPC computation running in parallel with 130 QC fragments, 104.6 seconds (55%) for the QC computation, and 9.1 seconds (5%) for MPI coordination and tensor reconstruction.
The total run time of L=2 seconds is decomposed into 76.7 seconds (40%) for the THPC computation running in parallel with 130 QC fragments, 104.6 seconds (55%) for the QC computation, and 9.1 seconds (5%) for MPI coordination and tensor reconstruction.

The pursuit of efficient hybrid quantum-classical computation, as demonstrated by DQR, necessitates a re-evaluation of traditional resource management. DQR’s wave-based dispatch, treating circuit fragments as independent units, echoes a fundamental principle of robust system design: isolating components to manage complexity. As Grace Hopper observed, “It’s easier to ask forgiveness than it is to get permission.” This sentiment aligns with DQR’s approach; rather than rigidly pre-planning every interaction between quantum and classical resources, the framework embraces a more adaptable, runtime-driven methodology, forgiving potential scheduling inefficiencies in favor of overall system responsiveness and fault tolerance. The elegance of this lies in its simplicity – a core tenet of sustainable, scalable design.

Beyond the Cut: Charting a Course Forward

The decoupling of quantum circuit fragmentation from high-performance computing orchestration, as demonstrated by DQR, represents a necessary step – not an arrival. The current approach treats cut fragments as independent schedulable units, akin to adding a new lane to a highway without revisiting the foundational road design. While this eases congestion, it does not address the underlying systemic inefficiencies. Future work must consider the interdependence of these fragments, exploring strategies that optimize resource allocation not at the level of individual cuts, but at the level of the entire quantum-classical workflow.

A critical limitation remains the assumption of homogeneity within cut fragments. Real-world quantum devices are rarely uniform; variations in qubit coherence and gate fidelity introduce subtle but significant performance bottlenecks. The framework should evolve to accommodate these heterogeneities, perhaps through adaptive scheduling algorithms that prioritize fragments based on device characteristics. The challenge is not merely to dispatch work, but to dispatch it intelligently.

Ultimately, the true test of this architecture lies in its capacity for fault tolerance. Cutting circuits, by its nature, introduces points of failure. A robust system must not only identify these failures but also seamlessly re-integrate fragments, rebuilding the computational path on the fly. The infrastructure should evolve without rebuilding the entire block; it demands a level of dynamic reconfiguration that remains largely unexplored.

Original article: https://arxiv.org/pdf/2604.15279.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-04-17 06:48