Abstract Quantum computers are increasingly used not as standalone machines but as accelerators within hybrid workflows, in which a classical system prepares a circuit, submits it to a quantum processor, and interprets probabilistic results. This thesis treats the quantum–classical interface as a systems problem rather than a physics one. The orchestration, scheduling, and observability taken for granted in classical cloud-native operations are largely absent at the quantum execution boundary. From a layered analysis of the interface and a survey of existing orchestration and observability work, the thesis derives a set of requirements and answers them with a proof-of-concept, the Quantum Circuit Controller (QCC), a control plane for the quantum–classical interface. It brings cloud-native orchestration and vendor-neutral, standards-based observability to the quantum execution boundary, treating a quantum backend as a managed, observable resource rather than an opaque remote endpoint. Exercised end-to-end on a representative quantum workload across both simulated and real hardware, it satisfies these requirements and makes the computational cost and behaviour of the interface visible. The thesis proposes two standardisation pathways, a vendor-neutral execution interface and a set of quantum observability conventions. Throughout, the work brings the discipline of site reliability engineering to the quantum–classical space. Keywords quantum–classical interface hybrid quantum–classical workloads Kubernetes OpenTelemetry observability cloud-native orchestration Qiskit
Abstract Quantum computers are increasingly used not as standalone machines but as accelerators within hybrid workflows, in which a classical system prepares a circuit, submits it to a quantum processor, and interprets probabilistic results. This thesis treats the quantum–classical interface as a systems problem rather than a physics one. The orchestration, scheduling, and observability taken for granted in classical cloud-native operations are largely absent at the quantum execution boundary. From a layered analysis of the interface and a survey of existing orchestration and observability work, the thesis derives a set of requirements and answers them with a proof-of-concept, the Quantum Circuit Controller (QCC), a control plane for the quantum–classical interface. It brings cloud-native orchestration and vendor-neutral, standards-based observability to the quantum execution boundary, treating a quantum backend as a managed, observable resource rather than an opaque remote endpoint. Exercised end-to-end on a representative quantum workload across both simulated and real hardware, it satisfies these requirements and makes the computational cost and behaviour of the interface visible. The thesis proposes two standardisation pathways, a vendor-neutral execution interface and a set of quantum observability conventions. Throughout, the work brings the discipline of site reliability engineering to the quantum–classical space. Keywords quantum–classical interface hybrid quantum–classical workloads Kubernetes OpenTelemetry observability cloud-native orchestration Qiskit
Hybrid quantum-classical workflows combine quantum processing units (QPUs) with classical hardware to address computational tasks that are challenging or infeasible for conventional systems alone. Coordinating these heterogeneous resources at scale demands robust orchestration, reproducibility, and observability. Even in the presence of fault-tolerant quantum devices, quantum computing will continue to operate within a broader hybrid ecosystem, where classical infrastructure plays a central role in task scheduling, data movement, error mitigation, and large-scale workflow coordination.
In this work, we present a cloud-native framework for managing hybrid quantum-HPC pipelines using Kubernetes, Argo Workflows, and Kueue. Our system unifies CPUs, GPUs, and QPUs under a single orchestration layer, enabling multi-stage workflows with dynamic, resource-aware scheduling. We demonstrate the framework with a proof-of-concept implementation of distributed quantum circuit cutting, showcasing execution across heterogeneous nodes and integration of classical and quantum tasks. This approach highlights the potential for scalable, reproducible, and flexible hybrid quantum-classical computing in cloud-native environments.
The integration of quantum computing technologies with established classical infrastructure represents a transformative advancement in computational capabilities. This manuscript addresses architectural frameworks enabling seamless orchestration between quantum and classical environments through container-based methodologies. Kubernetes extensions provide standardized interfaces for managing hybrid workloads while ensuring reproducibility across experimental iterations. Custom resource definitions facilitate quantum job specifications with appropriate hardware requirements, while specialized operators handle complex scheduling demands inherent to quantum processing. Implementation architectures demonstrate enhanced resource utilization through dynamic allocation mechanisms that accommodate both paradigms simultaneously. Practical evaluations reveal substantial improvements in workflow automation when orchestration platforms coordinate quantum simulators alongside cloud-based quantum processing units. The extensibility of container-based solutions proves particularly valuable for enterprises navigating evolving quantum hardware landscapes. The technological foundation described establishes a robust framework for quantum-classical integration while addressing security and deployment considerations. Findings indicate strategic advantages in adopting containerized approaches for quantum workflow management, particularly regarding experimental reproducibility and resource optimization across heterogeneous computing environments.
Early quantum computers are being offered by Cloud providers, with the ability to run Hybrid Quantum-Classical (HQC) applications that span Quantum and Classical (binary) computing resources. These HQC algorithms help benchmark and validate the performance of such near-term quantum hardware. Equally important is the software platform that allows a remote user to program the quantum computer on the cloud. The recent Qiskit Runtime platform from IBM allows a seamless and prioritized access to quantum backends and co-located classical hardware within their hybrid cloud for optimized execution of HQC workloads. In contrast, IBM’s Circuit API offers finer application definition but the classical resources are not co-located but placed farther away. Here, we study and contrast the execution flows of a Variational Quantum Eigensolver (VQE) application defined using Qiskit Runtime, and an equivalent implementation using the Circuit API. We quantify the latencies of the various components in these execution. This offers insights on how to better orchestrate the execution of quantum applications across such hybrid resources.
Quantum programming is today largely discussed at the level of circuits and application programminginterfaces: a developer constructs a circuit, submits it to a simulator or a cloud-hosted device, and readsback measurement results. As quantum computing moves toward shared, heterogeneous, andincreasingly hybrid quantum-classical environments, a complementary systems-level question becomesimportant — how should the lifecycle of a quantum computation be managed once it leaves the hands ofthe programmer, particularly when many such computations compete for limited execution resources?This paper presents QuantumOS, a research-oriented software platform that treats a quantum circuit notmerely as an object to be executed, but as a managed workload: a quantum job that is created, queued,scheduled according to a selectable policy, assigned to a computational resource by a resource manager,optionally transformed by a circuit-processing stage, executed on a local Quantum Virtual Machine(QVM), and finally recorded and surfaced through a monitoring dashboard. QuantumOS is explicitly aresearch prototype and does not control physical quantum hardware; its current execution backend is aclassical, local circuit simulator. We describe the system's layered architecture, its job-lifecycle andscheduling abstractions, and its execution workflow, and we report an experimental evaluation of theQVM's performance and correctness characteristics. Measured results show that QVM execution timeremains low for small circuits and rises sharply beyond approximately eight to nine simulated qubits,consistent with the expected cost of classical state-vector simulation, and that execution time growsroughly in proportion to the number of measurement shots requested. A correctness evaluation of fourcanonical circuits — a single-qubit Hadamard gate, a Pauli-X gate, a two-qubit Bell state, and a three-qubitGHZ state, each sampled at 10,000 shots — shows observed outcome probabilities within approximatelyhalf a percentage point of their theoretical values, consistent with ordinary statistical sampling variationrather than simulation error. We discuss how the broader scheduling and resource-managementarchitecture supports the paper's central systems claim even where full quantitative benchmarking ofevery subsystem remains for future work, and we outline the educational value of a locally runnableplatform that makes both quantum-circuit behavior and workload-management concepts directlyobservable. We conclude by identifying the prototype's current limitations and the concrete next stepsrequired to mature QuantumOS into a more completely benchmarked research platform.
Quantum computing is increasingly accessed through cloud-based hybrid environments, but practical adoption depends on more than exposing remote quantum backends. It also requires managed abstractions for orchestrating classical and quantum stages, handling queues, binding backends, and integrating surrounding workflow logic. This paper presents a comprehensive survey of Quantum Serverless from that perspective. Our contribution is twofold: (1) we consolidate the key architectural components of Quantum Serverless into a unified reference architecture; and (2) we evaluate current industry and research offerings through a KPI-driven framework covering ecosystem coverage, service operability, queue handling, pricing flexibility, near-real-time readiness, and production maturity. Using this framework, we analyze 18 quantum cloud platforms and show that the ecosystem is growing but remains unevenly mature. Queue-based execution is already a baseline capability, whereas stronger serverless characteristics, such as Function-as-a-Service, Containers-as-a-Service, near-real-time responsiveness, and production readiness, remain limited. Overall, current Quantum Serverless is best understood as a queue-driven hybrid orchestration model rather than a fully mature cloud-native serverless layer. The survey identifies the main maturity gaps and outlines directions for future development.
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.