Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a priority based access protocol, they are unable to fully support reliable, efficient, and scalable execution of larger-scale applications. To overcome this limitation, we propose a comprehensive solution for efficient allocation of quantum programs to appropriate quantum devices, considering all the relevant cost metrics into account including, fidelity, execution time and communication overhead. We also formulate use-cases for distributed quantum workflow and propose modified graph based algorithms to solve for allocation of such use-cases, assuming a hybrid classical-quantum network. Since hardware advancements in large standalone devices is an ongoing process, it is critical to investigate such distributed workflows to maximize the best utilization of current NISQ devices. Our empirical study shows that the proposed techniques perform better than state-of-the-art methods for almost all evaluation parameters, with average improvements of approximately $5\%$ in execution time, $30\%$ in communication overhead, $40\%$ in wait time and $2\%$ in fidelity, providing better solutions to efficient allocation strategies.
Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a priority based access protocol, they are unable to fully support reliable, efficient, and scalable execution of larger-scale applications. To overcome this limitation, we propose a comprehensive solution for efficient allocation of quantum programs to appropriate quantum devices, considering all the relevant cost metrics into account including, fidelity, execution time and communication overhead. We also formulate use-cases for distributed quantum workflow and propose modified graph based algorithms to solve for allocation of such use-cases, assuming a hybrid classical-quantum network. Since hardware advancements in large standalone devices is an ongoing process, it is critical to investigate such distributed workflows to maximize the best utilization of current NISQ devices. Our empirical study shows that the proposed techniques perform better than state-of-the-art methods for almost all evaluation parameters, with average improvements of approximately 5% in execution time, 30% in communication overhead, 40% in wait time and 2% in fidelity, providing better solutions to efficient allocation strategies.
Quantum computing networks enable scalable collaboration and secure information exchange among multiple classical and quantum computing nodes while executing large-scale generative AI computation tasks and advanced quantum algorithms. Quantum computing networks overcome limitations such as the number of qubits and coherence time of entangled pairs and offer advantages for generative AI infrastructure, including enhanced noise reduction through distributed processing and improved scalability by connecting multiple quantum devices. However, efficient resource allocation in quantum computing networks is a critical challenge due to factors including qubit variability and network complexity. In this article, we propose an intelligent resource allocation framework for quantum computing networks to improve network scalability with minimized resource costs. To achieve scalability in quantum computing networks, we formulate the resource allocation problem as stochastic programming, accounting for the uncertain fidelities of qubits and entangled pairs. Furthermore, we introduce state-of-the-art reinforcement learning (RL) algorithms, from generative learning to quantum machine learning for optimal quantum resource allocation to resolve the proposed stochastic resource allocation problem efficiently. Finally, we optimize the resource allocation in heterogeneous quantum computing networks supporting quantum generative learning applications and propose a multi-agent RL-based algorithm to learn the optimal resource allocation policies without prior knowledge.
Context: Quantum computing is rapidly evolving, offering new opportunities for solving problems in optimization, cryptography, and simulation. However, the limited availability of quantum resources makes efficient utilization of quantum hardware a current challenge. Today’s paradigms often lead to under-utilization of qubits, increased costs, and execution delays, especially in the NISQ era. Objective: This work aims to improve the utilization of quantum hardware by introducing an execution model that integrates multiprogramming at circuit level with quantum shot-wise distribution in a single policy-driven pipeline. Methods: An architecture has been implemented that combines circuit scheduling and shot distribution techniques to aggregate multiple circuits and distribute their shots across heterogeneous QPUs. The approach was empirically validated on actual IBM Quantum devices using a diverse set of reference circuits. Results: The proposal achieved a reduction in cost of 95% and a reduction in tasks 92%. Moreover, the fidelity analysis of the results showed an increase in noise, with an average increase of approximately 20% using different statistical distances. Conclusions: This research provides a usable and extensible solution to increase the efficiency, cost effectiveness, and resilience of quantum workload execution in heterogeneous and dynamic cloud environments. These results obtained suggest that users should weigh the implications of fidelity versus cost (and time) savings based on the application requirements and their goals.
Quantum computers represent a radical technological advancement in the way information is processed by using the principles of quantum mechanics to solve very complex problems that exceed the capabilities of classical systems. However, in the current NISQ era (Noisy Intermediate-Scale Quantum devices), the available hardware presents several limitations, such as a limited number of qubits, high error rates, and reduced coherence times. Efficient management of quantum resources, both physical (qubits, error rates, connectivity) and logical (quantum gates, algorithms, error correction), becomes particularly relevant in the design and deployment of quantum algorithms. In this work, we analyze the role of resources in the various uses of NISQ devices today, identifying their relevance and implications for software engineering focused on the use of quantum computers. We propose a vision for runtime-aware quantum software development, identifying key challenges to its realization, such as limited introspection capabilities and temporal constraints in current platforms. As a proof of concept, we introduce Qonscious, a prototype framework that enables conditional execution of quantum programs based on dynamic resource evaluation. With this contribution, we aim to strengthen the field of Quantum Resource Estimation (QRE) and move towards the development of scalable, reliable, and resource-aware quantum software.
This paper presents a novel algorithm for resource allocation within quantum networks leveraging topological structures. The core claim is to design an efficient resource allocation strategy that maximizes quantum computation effectiveness and resource utilization. The proposed mechanism dynamically allocates quantum computational resources based on the network's topology, optimizing the computational workflow. This addresses a critical challenge in quantum computing – efficient resource management – by introducing a new approach. The algorithm's effectiveness is demonstrated through theoretical analysis and a focus on key performance indicators such as qubit fidelity, computational latency, and overall resource throughput. The algorithm aims to improve the practicality and scalability of quantum computation within distributed quantum networks. The primary goal is to mitigate the limitations of traditional resource allocation methods, which often struggle to account for the complex and dynamic nature of quantum network topologies. This work contributes to the advancement of quantum networking and its potential for realizing robust and scalable quantum computation.
Quantum Computing is advancing from isolated prototypes toward heterogeneous, cloud-accessed quantum processors that are noisy, capacity-limited, and operationally diverse. In this landscape, the prevalent device-centric execution model (i.e., compiling a circuit for a single backend and running all shots monolithically) constrains scale, reliability, and cost-effectiveness. This thesis argues for and instantiates a system-centric paradigm of distributed execution, which treats execution as an engineered process that orchestrates spatial and temporal distribution across multiple devices and time scales, guided by explicit Quality-of-Service (QoS) objectives. Spatial distribution aggregates multiple, heterogeneous Quantum Processing Units and supporting classical services into a shared resource pool, placing circuits, subcircuits, and workloads where their algorithmic needs best align with hardware characteristics and availability. Temporal distribution adapts execution over time, modulating sampling depth, pacing, and scheduling in response to drift, queue dynamics, and evidence of statistical convergence. Together, these dimensions are governed by policy-driven orchestrations that reconciles fidelity, latency, and cost, and that elevates user intent above device idiosyncrasies. This thesis delivers five main contributions: (1) a shot-wise execution methodology that distributes measurement shots across heterogeneous QPUs under policy control, reducing output variance and matching or surpassing single-device averages; (2) Quantum Broker, a QoS-aware orchestration framework with the Quantum QoS Specification Language (QQSpec) that consistently finds feasible, high-quality allocations and outperforms single-backend and uniform-shot baselines under competing fidelity/latency/cost goals; (3) Cut&Shoot, an integrated circuit-cutting + shot-wise pipeline that executes large circuits across multiple devices with significantly lower error than monolithic, cutting-only, or shot-wise-only baselines, at negligible time overhead relative to standard cutting; (4) a hybrid orchestration architecture, QCRAFT Scheduler–Shotwise, which combines high-level circuit scheduling with shot-wise distribution to achieve up to 95% cost reduction and 92% fewer job submissions while improving robustness to hardware variability; and (5) IncrementalExecution, an adaptive, evidence-seeking shot manager that approximates optimal allocations with minimal overhead and high fidelity across unseen circuits and backends. Evaluations on simulated and real cloud backends demonstrate practicality across heterogeneous providers. Situated within Quantum Software Engineering, this thesis promotes abstractions and runtime mechanisms that make distribution a first-class program property rather than a backend option. The resulting perspective embraces hardware diversity as an optimization opportunity, improves portability across providers, and lays a path from today’s Noisy Intermediate-Scale Quantum (NISQ) platforms to future networked and fault-tolerant regimes. The overarching vision is that, by engineering execution as a principled, evidence-seeking process, heterogeneous quantum resources become the substrate for scalable, reliable, and efficient Quantum Computing.
Quantum computing promises computational capabilities beyond those of classical systems by exploiting superposition, entanglement, and quantum interference. However, the scalability of current noisy intermediate-scale quantum (NISQ) devices is limited by physical and engineering constraints, restricting the number of available qubits. Distributed quantum computing (DQC) mitigates this limitation by interconnecting multiple quantum processing units (QPUs) via quantum networks capable of distributing high-fidelity entanglement. Realizing such networks requires overcoming probabilistic entanglement generation, finite memory coherence, photon loss, and architectural constraints. This dissertation conducts an in-depth and comprehensive study of resource allocation in quantum networks, aiming to support high-throughput and high-fidelity entanglement distribution over lossy infrastructures. Our work spans the quantum networking stack, from physical-layer entanglement distribution protocols to network planning, quantum storage architectures, and distributed quantum computing. We begin by introducing two asynchronous entanglement distribution protocols-Sequential and Parallel-designed for early-stage quantum networks without global synchronization. Unlike many prior models, our protocols account for classical communication delays and quantum memory decoherence, reflecting practical constraints. Through simulations on real-world topologies such as SURFnet, we show that simple mechanisms such as cutoff-based memory management and sequential asynchronous entanglement distribution protocols can deliver competitive performance while significantly simplifying coordination, narrowing the gap between theoretical proposals and deployable protocols. Next, we formulate quantum network planning as a utility maximization problem that jointly considers entanglement distribution rate and fidelity, treating repeater and memory placement as optimization variables. We propose scalable heuristics and mixed-integer formulations, evaluating them under realistic conditions such as finite coherence time of quantum memories, limited number of memories per repeaters and end nodes, and real network topologies such as ESNet. Our findings offer practical design guidance and highlight how optimal placements depend on underlying physical constraints. To support dynamic workloads and respond to traffic bursts, we propose the Quantum Storage Network (QSN) architecture, where storage nodes buffer EPR pairs in advance of demand as an overlay network. This temporal decoupling improves responsiveness and throughput. Simulations across multiple topologies show that QSNs reduce entanglement distribution service latency by up to 40% and provide improved robustness compared to non-overlay designs, underscoring the value of storage-aware planning in future quantum networks. Finally, we focus on DQC, where large-scale quantum circuits are executed across networked QPUs. We propose a window-based circuit partitioning algorithm that minimizes nonlocal gate execution while balancing load across QPUs. Building on this partitioning, we design an adaptive, dependency-aware entanglement generation scheduler that opportunistically initiates remote entanglement as soon as dependent gates are ready, while accounting for realistic constraints such as coherence limits, probabilistic link generation, and Bell-state measurement (BSM) contention. Applied to benchmark circuits such as QAOA and QFT, this joint approach reduces execution delay by up to 35%. We further perform a two-qubit gate fidelity-sensitivity analysis under to show when the distributed quantum computing can be beneficial. Results show that the number of QPUs significantly impacts sensitivity to non-local gate fidelity: with fewer QPUs, distributed execution can outperform monolithic execution under looser fidelity constraints, whereas with more QPUs, achieving the same advantage requires substantially higher non-local gate fidelities relative to local gate fidelities. These findings identify the parameter regimes in which distributed quantum computing is beneficial, mapping break-even regions (as a function of number of QPUs) and quantifying how much cross-QPU (non-local) gate fidelity must improve—or, equivalently, how much per-QPU local gate fidelity must improve under modularization—for distributed execution to outperform a monolithic baseline. Together, these contributions form a cohesive framework for resource allocation in quantum networks in order to build scalable, delay-tolerant quantum networks and distributed computing systems. By grounding architectural design in realistic physical assumptions, this work advances the state of the art from theoretical modeling to practical, deployable quantum infrastructure.