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Hybrid Classical-Quantum Solutions to Accelerate the Adoption of Quantum Computing
Advait Deshpande, Luciano Baresi, Arosha K. Bandara
TL;DR
Near-term quantum computing has limitations for practical use, and hybrid software needs examination to understand how classical and quantum computing can work together. This paper examines that software stack and identifies challenges in verification, interoperability, workflow tooling, scalability, and quantum programming.
Problem
Near-term quantum computing offers insufficient performance gains for industrial or business-critical applications, motivating examination of how hybrid software can work in practice.
Source: -QUANTUM COMPUTING/SYSTEMS · p. 7, §4 · p. 14Method
The paper examines the software stack of hybrid classical–quantum computing to understand how these solutions may work in practice.
Source: -QUANTUM COMPUTING/SYSTEMS · p. 7Results
The paper identifies hybrid-development challenges in verification, cloud interoperability, workflow tools, service scalability, and learning quantum programming.
Source: §6 · p. 18Takeaways & Limitations
Hybrid software development faces practical challenges in verifying outputs, connecting cloud platforms, managing workflows, scaling services, and programming quantum logic.
Source: §6 · p. 18Near-term quantum computing offers insufficient performance gains for industrial or business-critical applications.
Source: §4 · p. 14
Abstract
from arXiv · showhide
Despite the promise and potential of quantum computing, most of the current day developments of quantum computing have several limitations for relevant practical use. As a result, hybrid classical-quantum computing has emerged as a viable solution. This paper discusses the software aspects of hybrid classical-quantum computing solutions to understand and examine how hybrid computing solutions can be expected to work in practice. This paper examines existing software solutions (cloud applications, middleware frameworks, and API gateways) that can enable classical and quantum computing to work together. For practitioners the paper provides a curated list of algorithms, full stack libraries, simulators, and cloud service providers that are likely to be central to the development of any hybrid computing solutions. We conclude by identifying an agenda for future research on classical-quantum hybrid software engineering, and some near-term priorities for development of functional, robust hybrid software systems. The paper argues that due to the deep dependency of quantum execution on classical pre- and post-processing, hybrid computing may represent a near-permanent outcome of classical-quantum computing rather than a transitional workaround.
1 INTRODUCTION
Hybrid computing combines specialized quantum execution with classical processing in response to current quantum hardware limitations. The paper treats hybrid software engineering as central and argues that hybrid systems will remain a structural feature of classical–quantum computing for the foreseeable future.
1 INTRODUCTION
- Quantum speedup depends on matching algorithms to problem structure, so hybrid software infrastructure is likely to comprise problem-specific workflows rather than one unified framework.The paper contrasts QAOA for combinatorial optimisation [15], VQE for simulation, and Shor’s algorithm for integer factorisation.Source: §1 · p. 2
- Current qubits are error-prone, unstable, and costly to operate; the paper reports physical-qubit error rates of 10^-3 and a target of 10^-15 before matching modern classical computers.These limitations motivate hybrid quantum-classical computing as a viable solution [30].Source: §1 · p. 4
- Hybrid systems assign quantum computers a small specialized workflow component while classical computers handle the larger workflow, control operations, and support imperative constructs through feedback and mid-circuit measurements.Classical computing is expected to remain central in the near term [23] [30].Source: §1 · pp. 4–5
- Cloud Quantum-as-a-Service platforms are expected to enable practical hybrid solutions, but their scalability, modularity, maintainability, and reusability remain unclear.Examples include Azure Quantum, Braket, and IBM Quantum.Source: §1 · p. 5
- The paper centers hybrid software engineering, maps existing solutions to limitations in a gap analysis, and argues hybrid computing will remain structural rather than transitional.It calls for research investment in software engineering foundations alongside hardware capability to support reliable deployment at scale.Source: §1 · pp. 5–6
2 HYBRID CLASSICAL
The section distinguishes vertical hybrid systems, where classical and quantum computing meet at a circuit-execution boundary, from horizontal workflows that interleave them. It argues that classical dependencies make hybrid software important now and likely to remain so.
-QUANTUM COMPUTING/SYSTEMS
- Horizontal hybrid computing, unlike vertical integration, requires low-latency feedback and co-scheduling, making it the more demanding software-stack challenge.Vertical cloud workflows submit compiled circuits to a QPU and return results for classical interpretation, with limited orchestration; horizontal VQA workflows repeatedly update circuit parameters from measurements [30].Source: -QUANTUM COMPUTING/SYSTEMS · p. 6, Figure 1 · p. 7, Figure 2 · p. 7
- VQAs use classical computation to train parameterised circuits, while hybrid algorithms also include classical pre- or post-processing, as in Shor’s [36] and Simon’s [37] algorithms.VQE is presented as the first VQA and an example of hybrid computing in a noisy environment; QAOA [15] exemplifies iterative quantum-classical computation until convergence.Source: -QUANTUM COMPUTING/SYSTEMS · pp. 6–8
- Hybrid algorithms may remain structurally advantageous beyond the NISQ era because classical systems retain essential data-handling, access, and translation roles [7].Even fault-tolerant hardware would not eliminate classical data input/output, shared cloud access, or translation of real-world data into quantum-compatible representations; classical and quantum resources can instead be allocated to their strengths [7].Source: -QUANTUM COMPUTING/SYSTEMS · pp. 8–9
- The curated resource list rates PennyLane production-ready for ML/VQA workflows and describes managed hybrid execution through Amazon Braket Hybrid Jobs.The list covers algorithms, full-stack libraries, simulators, and cloud providers, selected partly for practitioner documentation, funding prospects, workflow integration, and hybrid suitability.Source: -QUANTUM COMPUTING/SYSTEMS · pp. 9–10, Table 2 · p. 9
- The performance gains of available commercial quantum solutions over scaled-up classical hardware remain unclear.The section notes that most commercial offerings are cloud-based and that perceived quantum advantages in optimisation and simulation may encourage hybrid adoption.Source: -QUANTUM COMPUTING/SYSTEMS · p. 12
3 HYBRID COMPUTING SOLUTIONS IN PRACTICE
Although current quantum systems are hybrid in practice, concrete demonstrations of cooperative classical–quantum applications remain limited. Proposals span cloud orchestration, workflow and serverless services, middleware, and API gateways.
3 HYBRID COMPUTING SOLUTIONS IN PRACTICE
- Concrete applications that proficiently integrate classical and quantum computing remain limited, and existing examples address few problems and small systems.Many anticipated quantum software engineering features are not yet needed in practice.Source: §3 · pp. 12–13
- TOSCA4QC, extending the TOSCA cloud-application standard, supports separate classical and quantum stacks, illustrated by a toy web app calling a Qiskit implementation of Shor’s algorithm.The proposal remains high-level.Source: §3 · p. 12
- Quokka provides workflow services for variational quantum algorithm tasks, an initial step toward composing classical and quantum components.Source: §3 · p. 12
- QFAAS combines quantum SDKs, simulators, and cloud providers in a serverless platform, but mainly wraps quantum API calls rather than coordinating classical and quantum functions.Its two examples are Shor’s algorithm and random number generation.Source: §3 · pp. 12–13
- A middleware framework built on Ray.io targets heterogeneous classical–quantum computation, but its case studies concern circuit knitting and quantum machine learning.Source: §3 · p. 13
- The Quantum API Gateway lets users call classical and quantum services similarly, but its evaluation measures gateway delay when calling a quantum component, not heterogeneous integration.The prototype could use services from Amazon Braket.Source: §3 · p. 13
4 CURRENT LIMITATIONS
Current quantum hardware is too error-prone and offers insufficient performance gains for mission-critical applications, while software and cloud-service limitations impede reliable, portable classical–quantum workflows. Hybrid computing addresses only part of these challenges; consistent and replicable execution remains unresolved.
Current Limitations
- Near-term quantum computers offer insufficient gains for industrial or mission-critical applications, and decoherence and accumulating errors can make outputs impractical even for niche uses [10].The paper says everyday universal use is unlikely without significant hardware and software breakthroughs.Source: §4 · p. 14
- Quantum algorithms demand unfamiliar computational approaches, while developers need new languages, compilers, optimization tools, and reliable data transfer between classical and quantum stacks.Existing domain-specific languages partly address algorithm complexity, but developers must retrain; libraries and standardization efforts remain partial.Source: §4 · pp. 14–15
- Quantum-as-a-service outputs vary with processor and qubit topology, while hardware coupling and absent intermediate verification hinder consistent, replicable execution [35] [24].Variational algorithms may limit some noise, but controllable workflows and the accuracy of instruction execution remain unproven [29] [24].Source: §4 · pp. 14, 16
- Lack of interoperability between quantum cloud platforms creates significant technical overhead for performance verification of software, hardware benchmarking, and validating hardware behavior using numerical simulation [24].Source: §4 · p. 14
- Remote-call overhead and queuing latency constrain QaaS scalability, while developers face effort-intensive service-specific coding and limited visibility into information flows and qubit errors [24].Cloud QaaS latency is architectural; educational and tooling resources for classical developers remain nascent.Source: §4 · p. 16
- Hybrid resources lack direct integration with classical software, and C/C++ or Python developers often rely on separate stages or manual interventions instead of end-to-end workflows [25] [24].Middleware and the Quantum API Gateway begin to address integration but lack production-grade solutions; QFAAS wraps API calls without genuine co-execution.Source: §4 · pp. 16–17
5 FUTURE DIRECTIONS AND DEVELOPMENT AGENDA
Until quantum platforms match classical platforms in maturity and scale, hybrid systems offer a potential long-term bridge; practical use requires advances in design, documentation, and resource management, with AI-assisted development also expected to contribute.
Methodologies
- Hybrid software needs high-level design methodologies, standard patterns, domain-specific modelling languages, and architectures suited to different coupling and processing scenarios [27].Unlike classical software engineering, quantum software engineering lacks decades of production experience from which to distil established patterns, so methodology must develop alongside engineering practice.Source: §5 · p. 17
Tools and processes
- Hybrid development needs shared standards and practices to address fragmented, largely non-transferable SDKs, testing frameworks, simulators, and workflows.Priorities include standard development phases, hybrid-specific requirements engineering, testing and validation frameworks, and documentation and maintenance strategies; the field’s maturity challenge is also discussed in the Talavera Manifesto.Source: §5 · pp. 17–18
Resource management
- Hybrid systems need resource management that treats quantum coherence time and gate fidelity as scheduling constraints alongside classical resource metrics [34].Needed capabilities include dynamic quantum-classical allocation, workload performance prediction, energy optimization, and quality-of-service guarantees; coherence deadlines, hardware performance degradation, and QPU access costs complicate conventional approaches.Source: §5 · p. 18
AI-assisted software development
- LLMs could lower expertise barriers and assist quantum software development, but their effectiveness for hybrid computing remains unproven [27].Potential applications include circuit design, QUBO formulation, and translating classical algorithms into quantum-compatible code; early circuit-generation work [21] is nascent, while hardware scarcity and simulator-dependent development stacks limit evaluation.Source: §5 · p. 18
6 CONCLUSIONS
Hybrid classical–quantum software remains nascent and fragmented, with practical use constrained by unresolved engineering challenges. The paper calls for sustained investment in software foundations and near-term development priorities to make hybrid systems functional and robust.
6 CONCLUSIONS
- Hybrid software development faces missing intermediate verification, poor cloud-platform interoperability, unproven workflow tools, limited quantum-as-a-service scalability, and steep quantum-language learning curves.Source: §6 · p. 18
- Near-term priorities are high-level design methods, standardized development practices, and resource management for performance prediction, energy optimization, and quality-of-service guarantees.Although paradigms such as TOSCA4QC, Quokka, and QFAAS may simplify workflow development, few applications demonstrably and consistently exploit hybrid systems, and most limitations lack production-grade solutions.Source: §6 · p. 19
- Quantum software is in a formative period resembling earlier computing transitions, when fragmentation and manual workarounds gave way to shared standards and software engineering foundations.The paper argues that reliable, scalable adoption requires sustained investment in languages, abstractions, tools, standards, and practitioner education, not hardware advances alone.Source: §6 · p. 19
- Investment in quantum software engineering, interoperability, testing, and practitioner education is as strategically important as hardware investment and may be more tractable near term.Progress on software foundations requires sustained collaborative engineering, not breakthroughs in physics.Source: §6 · p. 19