Bridging the Quantum Divide: A Runtime for Hybrid Computation

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Author: Denis Avetisyan

Researchers have extended a versatile runtime system to seamlessly integrate classical and quantum processing, paving the way for more complex hybrid workflows.

The Q-IRIS software stack integrates quantum computation into existing workflows by leveraging both $Qiskit$ runtime with OpenQASM kernels and $QIR-EE$ driven $QIR$ kernels, enabling execution via simulators like $QSim$ or accelerators through $XACC$.
The Q-IRIS software stack integrates quantum computation into existing workflows by leveraging both $Qiskit$ runtime with OpenQASM kernels and $QIR-EE$ driven $QIR$ kernels, enabling execution via simulators like $QSim$ or accelerators through $XACC$.

Q-IRIS builds upon the IRIS task-based runtime, incorporating Quantum Intermediate Representation (QIR) to enable parallel and asynchronous quantum computation.

The increasing complexity of heterogeneous computing systems, now incorporating quantum accelerators, demands runtime environments capable of orchestrating both classical and quantum workloads. This paper introduces Q-IRIS-an evolution of the IRIS task-based runtime-designed to enable seamless integration of quantum computation via the Quantum Intermediate Representation (QIR) and XACC framework. Q-IRIS demonstrates asynchronous scheduling and execution of quantum tasks across diverse backends, including simulators, and showcases improved throughput through quantum circuit cutting-a technique for optimizing task granularity. As quantum hardware matures, how can hybrid runtime systems best address the challenges of coordinated scheduling and efficient classical-quantum interaction to unlock the full potential of heterogeneous computation?

Navigating the Limits of Classical Computation

The relentless pursuit of solutions to increasingly complex problems is bumping against the inherent limitations of classical computation. While Moore’s Law once reliably predicted exponential increases in processing power, its deceleration is becoming acutely felt in fields like materials science, drug discovery, and financial modeling. Simulating molecular interactions, optimizing logistical networks, or accurately forecasting market trends often requires computational resources that grow exponentially with problem size – quickly exceeding the capabilities of even the most powerful supercomputers. This escalating demand has spurred investigation into alternative paradigms, not to replace classical systems entirely, but to augment them with specialized architectures capable of tackling problems currently intractable. The inability of classical bits to efficiently represent and manipulate the complexities of certain systems necessitates exploring fundamentally different approaches to information processing, driving the search for novel computational methods and hybrid architectures.

While quantum computing promises exponential speedups for specific computational tasks – potentially revolutionizing fields like drug discovery and materials science – realizing this potential demands a pragmatic approach to implementation. Current quantum processors, known as quantum processing units (QPUs), are limited in qubit count and coherence, and operate at extremely low temperatures, making them unsuitable as direct replacements for conventional central processing units (CPUs) and graphics processing units (GPUs). Instead, a hybrid architecture is emerging, where quantum computers function as specialized co-processors, tackling specific subroutines within a larger computation handled by classical hardware. This integration necessitates careful partitioning of algorithms, efficient data transfer between QPUs and classical resources, and the development of software frameworks capable of managing this heterogeneous computing landscape. The challenge lies not only in building powerful quantum hardware, but in seamlessly weaving it into the existing computational infrastructure to unlock its transformative capabilities.

The pursuit of scalable computation is increasingly focused on heterogeneous systems that strategically integrate diverse processing units. Rather than relying solely on classical CPUs or attempting to build fully quantum computers, researchers are now combining the strengths of central processing units, graphics processing units, and quantum processing units. CPUs excel at control flow and general-purpose tasks, while GPUs provide massive parallelism ideal for data-intensive computations. Quantum processing units, though still in their early stages, offer the potential for exponential speedups in specific algorithms. This synergistic approach allows computations to be partitioned, assigning each task to the most suitable hardware component. For instance, a complex simulation might utilize a CPU for overall control, a GPU for large matrix operations, and a QPU for solving particularly challenging optimization problems. This integration isn’t merely about adding more hardware; it demands sophisticated runtime systems capable of intelligently scheduling tasks, managing data transfer between processors, and optimizing resource allocation to maximize performance and efficiency – ultimately paving the way for tackling problems currently intractable for even the most powerful supercomputers.

The effective utilization of heterogeneous quantum-classical systems hinges on sophisticated runtime environments capable of dynamically allocating and managing resources across diverse hardware accelerators. These intelligent systems move beyond static task assignment, instead employing techniques like automated code partitioning and scheduling to optimize performance. Such runtimes must account for the unique characteristics of each processing unit – the speed and coherence limitations of quantum processing units (QPUs), the parallel processing capabilities of GPUs, and the versatility of central processing units (CPUs). Furthermore, they require advanced compilation strategies that translate high-level algorithms into executable code tailored for this hybrid architecture, minimizing data transfer overhead and maximizing computational throughput. Ultimately, these runtime systems represent a crucial enabling technology, bridging the gap between the promise of quantum speedups and the practical realities of modern computation, and paving the way for scalable and efficient hybrid algorithms.

Q-IRIS: Orchestrating Quantum and Classical Collaboration

Q-IRIS functions as a unified runtime environment intended to facilitate the combined execution of quantum and classical algorithms. This integration is achieved by providing a single system that manages the orchestration of tasks across both computational paradigms, eliminating the need for separate, manually coordinated workflows. The design aims to abstract the complexities of heterogeneous hardware, allowing developers to express hybrid algorithms without being concerned with the underlying details of quantum or classical execution. By consolidating control and data flow, Q-IRIS intends to improve performance and simplify the development process for applications requiring both quantum and classical resources.

Q-IRIS builds upon the existing IRIS runtime system by integrating the QIR-EE execution engine, specifically designed for quantum program execution. IRIS provides the foundational infrastructure for task management, resource allocation, and inter-process communication, while QIR-EE handles the complexities of compiling and running quantum circuits. This extension allows Q-IRIS to seamlessly incorporate quantum computations into larger, classically-controlled workflows. QIR-EE interprets the Quantum Intermediate Representation (QIR) and translates it into instructions executable on supported quantum hardware or simulators, enabling a unified runtime environment for hybrid algorithms.

Effective communication between classical and quantum components is fundamental to Q-IRIS operation, as the system necessitates frequent data transfer and control signals for coordinated task execution. Specifically, classical processors within Q-IRIS manage overall program flow, decompose algorithms into classical and quantum subroutines, and handle pre- and post-processing of quantum computations. Quantum programs, executed by the QIR-EE, require input data from the classical side and return results that must be interpreted and utilized by classical algorithms. This interaction relies on well-defined interfaces and communication protocols to minimize latency and ensure data integrity, enabling the seamless integration of quantum acceleration into larger computational workflows. Data formats and transfer mechanisms are optimized for the specific hardware interfaces between the classical and quantum processing units.

Q-IRIS employs a scheduling strategy that dynamically allocates tasks to either classical or quantum hardware based on computational requirements and resource availability. This involves profiling tasks to determine their suitability for quantum acceleration and subsequently mapping them to the QIR-EE for execution on available quantum processing units (QPUs) or simulators. Classical tasks continue to be executed by the underlying IRIS runtime on CPUs or GPUs. Resource allocation is managed through a centralized scheduler that monitors the status of both classical and quantum resources, ensuring optimal utilization and minimizing communication overhead between heterogeneous platforms. The architecture supports prioritization of tasks and allows for the definition of data dependencies to maintain correct execution order across both computational environments.

A quantum device preparation (QDP) utilizing two quantum wire cuts successfully decomposes a four-qubit GHZ state into multiple quantum instances, each comprised of three two-qubit circuits, as detailed in Eq. 1 (Harada et al., 2024).
A quantum device preparation (QDP) utilizing two quantum wire cuts successfully decomposes a four-qubit GHZ state into multiple quantum instances, each comprised of three two-qubit circuits, as detailed in Eq. 1 (Harada et al., 2024).

Deconstructing Complexity with QIR and QDP

Quantum Intermediate Representation (QIR) functions as an intermediate language for quantum programs, decoupling the algorithm description from the underlying quantum hardware. This hardware-agnostic approach allows a single QIR program to be targeted for execution on diverse quantum processing units (QPUs) and simulators without requiring code modifications. QIR defines a standardized set of operations and data types, enabling optimization passes to be developed and applied independently of specific hardware constraints. The representation supports both high-level quantum constructs and low-level gate operations, facilitating a flexible compilation flow. By providing a common interface, QIR promotes code portability, reusability, and interoperability within the quantum software ecosystem.

The Quantum Intermediate Representation (QIR) leverages the Multi-Level Intermediate Representation (MLIR) compiler infrastructure to facilitate extensibility and optimization. MLIR provides a flexible framework for defining transformations and analyses on quantum programs represented in QIR. This allows developers to implement advanced compilation techniques, such as graph rewriting, operation fusion, and target-specific code generation, without modifying the core QIR definition. The use of MLIR’s dialect system enables the modular addition of optimization passes and the representation of hardware-specific instructions, ultimately improving the performance and efficiency of quantum programs across diverse computing platforms. Furthermore, MLIR’s infrastructure supports automated code generation and optimization, reducing the need for manual intervention and accelerating the development cycle.

Quasi-Probability Decomposition (QDP) is a circuit decomposition technique that represents a quantum circuit as a sum of tensor products of lower-dimensional circuits. This decomposition facilitates parallel execution by enabling independent computation of each tensor product component. The resulting smaller circuits require fewer qubits and gates, reducing resource requirements and increasing computational speed. QDP achieves this by expressing the density matrix of a quantum state as a mixture of product states, allowing operations to be broken down and distributed across multiple processing units. This is particularly beneficial for complex algorithms where the original circuit depth and qubit count would otherwise limit scalability and performance on available hardware.

Application of Quasi-Probability Decomposition (QDP) to algorithms such as GHZ State Preparation results in improved performance and scalability on heterogeneous computing architectures. The research detailed in the paper demonstrates this through a parallel execution of GHZ State Preparation utilizing 64 cores. This decomposition facilitates the partitioning of the quantum circuit into smaller, independently executable components, enabling concurrent processing and reduced overall execution time. The observed performance gains validate QDP as an effective optimization strategy for leveraging the capabilities of parallel computing systems in quantum program execution.

Experimental validation confirms successful GHZ state preparation using the QDP method, as demonstrated by the measured mean value of ⟨ZZZZ⟩ closely matching the theoretical expectation of 1, with standard deviation indicated by the shaded region.
Experimental validation confirms successful GHZ state preparation using the QDP method, as demonstrated by the measured mean value of ⟨ZZZZ⟩ closely matching the theoretical expectation of 1, with standard deviation indicated by the shaded region.

Towards Robust Quantum Applications: Reliability and Integration

Quantum computations are inherently susceptible to errors stemming from environmental noise and imperfections in quantum hardware. These errors, if left unaddressed, rapidly degrade the accuracy of results, hindering the potential of quantum algorithms. Consequently, error mitigation techniques have become indispensable tools in the field. These methods don’t eliminate errors entirely, but rather aim to extrapolate results that would be obtained in the absence of noise. Approaches range from clever pulse shaping and dynamical decoupling to post-processing techniques like zero-noise extrapolation and probabilistic error cancellation. By carefully characterizing and modeling the sources of error, researchers can apply these techniques to significantly reduce their impact, allowing for more reliable estimations of quantum observables and paving the way for practical applications of quantum computation, even on near-term, noisy intermediate-scale quantum (NISQ) devices.

Q-IRIS achieves enhanced versatility through its integration with XACC, a powerful framework designed to abstract the complexities of quantum hardware and simulation environments. This connection allows researchers and developers to deploy and test quantum algorithms across a diverse landscape of backends – ranging from readily available simulators to cutting-edge quantum processing units – without requiring substantial code modifications. By leveraging XACC’s unified interface, Q-IRIS effectively decouples the algorithm from the underlying physical implementation, fostering portability and accelerating the development cycle for quantum applications. This streamlined workflow enables broader access to quantum computing resources and facilitates rigorous testing and validation of algorithms before deployment on actual quantum hardware, ultimately improving the reliability and scalability of quantum solutions.

The architecture facilitates a streamlined workflow for quantum algorithm development by enabling deployment and testing across a variety of quantum computing environments. This is achieved through integration with XACC, a versatile framework that abstracts the complexities of interacting with diverse hardware and simulators. Researchers and developers can utilize a unified interface to execute the same algorithm on different platforms – from superconducting qubits to trapped ions and beyond – without significant code modifications. This portability is crucial for benchmarking algorithm performance, identifying hardware-specific optimizations, and ultimately, ensuring the robustness and scalability of quantum solutions. The ability to test on simulators also provides a cost-effective means of initial validation before leveraging potentially limited and expensive quantum hardware resources.

The convergence of Q-IRIS, the XACC framework, and robust error mitigation strategies is actively fostering the development of dependable quantum applications, with particular promise for fields like Variational Quantum Algorithms and Quantum Machine Learning. Recent demonstrations highlight this progress; specifically, researchers achieved an approximated accuracy of ≈ 1 in preparing a GHZ state, a critical benchmark for quantum computation, and this result closely aligns with established theoretical predictions. This high level of fidelity was obtained through 1024 computational shots per execution, illustrating the potential for scalable and reliable quantum processing when these technologies are combined. The ability to consistently generate accurate quantum states is a crucial step toward realizing the transformative capabilities of quantum computers across diverse scientific and industrial domains.

The development of Q-IRIS, as detailed in the study, inherently acknowledges the complexities of integrating disparate computational paradigms. This pursuit of hybrid classical-quantum workflows demands careful consideration of the underlying values embedded within the system’s architecture. As John Bell aptly stated, “No phenomenon is a phenomenon until it is an observed one.” This resonates with the need for rigorous validation and transparency in quantum computation-ensuring that the results are not merely mathematical possibilities, but demonstrable realities. Q-IRIS, by facilitating asynchronous quantum tasks, necessitates a framework where observation and validation are integral, preventing the automation of potentially untrustworthy computations. The system’s design must prioritize verification to maintain confidence in the hybrid workflows it enables.

What Lies Ahead?

The extension of task-based runtimes to encompass quantum computation, as demonstrated by Q-IRIS, is less a technological hurdle cleared and more an invitation to confront a deeper set of questions. The system facilitates hybrid computation, certainly, but the true challenge now resides in understanding what computations deserve that facilitation. Efficiency gains are readily quantifiable; ethical considerations, less so. The architecture reveals not only how to execute quantum algorithms, but also forces an accounting of the values encoded in their prioritization-a subtle, yet critical, distinction.

Future work will undoubtedly focus on optimizing the interplay between classical and quantum resources. However, the more pressing concern is the development of robust mechanisms for tracing the provenance of computational decisions. Automation, even in a hybrid context, does not absolve responsibility. The system’s capacity for asynchronous execution necessitates a corresponding capacity for auditing its outcomes, and ensuring alignment with intended – and justifiable – goals.

The next iteration of these systems must move beyond mere performance metrics. It should incorporate frameworks for evaluating the societal impact of these hybrid workflows. Technology is, after all, an extension of ethical choices, and every automation bears responsibility for its outcomes-a principle that must be as deeply embedded in the runtime as the quantum kernels themselves.

Original article: https://arxiv.org/pdf/2512.13931.pdf

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

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2025-12-17 08:56