The current quantum landscape is characterised by a wide variety of quantum computers, each with unique capabilities and limitations. Despite its rapid advancement, this field is still in its early stages and lacks essential software engineering principles such as resilience and customisability by design. To provide developers with a quantum technology stack that embeds these principles by design, we propose leveraging the heterogeneity of existing quantum computers. Specifically, we introduce a novel distribution methodology, termed shot-wise, to distribute the quantum computation workload of a single circuit execution (i.e., its shots) across multiple quantum computers. This shot-wise methodology is highly customisable, allowing for the em-bedding of user-specific and scenario-specific requirements into the distribution process. Requirements can be expressed through constraints, such as the geographical location of the QPUs, and their architecture, or ensuring or avoiding specific combinations of QPUs and quantum providers. Additionally, requirements can be defined by one or more objective functions to be minimised or maximised, such as reducing waiting time, computation cost, or energy footprint. By also combining constraints and objective functions, our shot-wise methodology enhances flexibility and improves the overall resilience of quantum computations against potential quantum computer failures. Preliminary results indicate that this approach offers error rates comparable to, or lower than, those of single-device execution even in all the tested scenarios with different numbers of qubits, families of circuits and number of QPUs to distribute the shots.
The current quantum landscape is characterised by a wide variety of quantum computers, each with unique capabilities and limitations. Despite its rapid advancement, this field is still in its early stages and lacks essential software engineering principles such as resilience and customisability by design. To provide developers with a quantum technology stack that embeds these principles by design, we propose leveraging the heterogeneity of existing quantum computers. Specifically, we introduce a novel distribution methodology, termed shot-wise, to distribute the quantum computation workload of a single circuit execution (i.e., its shots) across multiple quantum computers. This shot-wise methodology is highly customisable, allowing for the em-bedding of user-specific and scenario-specific requirements into the distribution process. Requirements can be expressed through constraints, such as the geographical location of the QPUs, and their architecture, or ensuring or avoiding specific combinations of QPUs and quantum providers. Additionally, requirements can be defined by one or more objective functions to be minimised or maximised, such as reducing waiting time, computation cost, or energy footprint. By also combining constraints and objective functions, our shot-wise methodology enhances flexibility and improves the overall resilience of quantum computations against potential quantum computer failures. Preliminary results indicate that this approach offers error rates comparable to, or lower than, those of single-device execution even in all the tested scenarios with different numbers of qubits, families of circuits and number of QPUs to distribute the shots.
NISQ (Noisy Intermediate-Scale Quantum) era constraints, high sensitivity to noise and limited qubit count, impose significant barriers on the usability of QPUs (Quantum Process Units) capabilities. To overcome these challenges, researchers are exploring methods to maximize the utility of existing QPUs despite their limitations. Building upon the idea that the execution of a quantum circuit’s shots does not need to be treated as a singular monolithic unit, we propose a methodological framework, termed shot-wise, which enables the distribution of shots for a single circuit across multiple QPUs. Our framework features customizable policies to adapt to various scenarios. Additionally, it introduces a calibration method to pre-evaluate the accuracy and reliability of each QPU’s output before the actual distribution process and an incremental execution mechanism for dynamically managing the shot allocation and policy updates. Such an approach enables flexible and fine-grained management of the distribution process, taking into account various user-defined constraints and (contrasting) objectives. Demonstration results show that shot-wise distribution consistently and significantly improves the execution performance, with no significant drawbacks and additional qualitative advantages. Overall, the shot-wise methodology improves result stability and often outperforms single QPU runs, offering a robust and flexible approach to managing variability in quantum computing.
Currently, quantum researchers are facing the limitations of NISQ (Noisy Intermediate-Scale Quantum) computers, such as limited qubit counts and high sensitivity to external interference. In response to these challenges, two techniques have recently been proposed, among others, to enhance the execution of quantum circuits: circuit cutting and shot-wise distribution. Circuit cutting partitions large quantum circuits into smaller fragments that can be executed on today's NISQ devices. Shot-wise distribution leverages multiple heterogeneous quantum computers by distributing the shots of a single quantum circuit execution across various NISQ devices, improving resiliency and reducing overall execution time, in a flexible and customisable way. In this work, we investigate the synergic effect of a seamless composition of these two techniques into a unified pipeline of our own design, termed Cut&Shoot. This pipeline fragments a single quantum circuit into multiple independent sub-circuits, distributes the shots of each fragment across various quantum computers, and finally merges the results to reconstruct the output for the original circuit. Noisy simulation experiments seem to indicate that circuit cutting effectively reduces error rates, without the need to increase the number of shots performed. Shot-wise distribution, instead, while offering several qualitative advantages, does not significantly affect error rates, with negligible time overhead.
Quantum computing is progressing at a rapid pace, though still constrained by the limitations of Noisy Intermediate-Scale Quantum (NISQ) devices, such as restricted qubit counts and high susceptibility to noise. To address these constraints, researchers have begun adapting classical software engineering principles to the quantum realm, giving rise to the field of Quantum Software Engineering. Within this context, techniques like circuit cutting and shot- wise distribution have emerged as promising approaches to divide quantum circuits into smaller fragments and to improve the scalability and reliability of quantum computations. In this paper, we present our Cut&Shoot pipeline, which supports the cutting of quantum circuits and the flexible distribution of their shots across multiple quantum processing units (QPUs), in a unified execution strategy. We compare different policies to determine the number of shots for which each fragment should be executed on each QPU. Through 107,000+ experiments on simulated NISQ devices, we assess how circuit size, shot allocation, and QPU availability affect execution times and error rates. Our findings indicate that combining circuit cutting and shot- wise distribution provides a useful trade-off of execution times and error rates, while featuring high-qubit quantum computations as well as increased resilience and adaptiveness.
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.
Cloud-accessible quantum computing has made heterogeneous Quantum Processing Units (QPUs) available through provider platforms that differ in qubit technology, calibration state, queueing behavior, and pricing model. This heterogeneity makes execution management a quantum-engineering concern: a quantum circuit may be executable on several backends, but the resulting cost, latency, fidelity, and reproducibility depend on backend selection and on how measurement shots are allocated. This paper presents the Quantum Broker, an open-source and provider-agnostic framework for Quality-of-Service-aware quantum execution management. The framework operationalizes shot-wise distribution by exploiting the statistical independence of quantum measurement shots to dispatch a single circuit across multiple heterogeneous QPUs according to user-defined constraints and optimization objectives. It combines a modular execution architecture, interchangeable backend-profiling strategies, and the Quantum QoS Specification Language (QQSpec), a declarative language for expressing QoS requirements. Dispatch planning is supported by two solver backends: a mixed-integer linear-programming (MILP) solver, and a nonlinear dual-annealing solver. Experiments on benchmark quantum circuits and noisy simulator backends show that the Quantum Broker can produce feasible QoS-aware dispatch plans under heterogeneous backend conditions and can improve QoS satisfaction with respect to single-backend and naive multi-backend baselines. The results also expose a practical trade-off between planning time and dispatch quality, with the nonlinear solver producing higher-quality allocations at increased computational cost. Overall, the Quantum Broker provides an extensible execution-management layer for engineering portable, reproducible, and QoS-aware quantum software over fragmented quantum cloud infrastructures.
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.