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No. 140 
No. 139 by Miroslav Dobsicek (RISE), Sebastian Raubitzek (SBA Research) and Filippo Vella (CNR-ICAR)
In recent years, major advances in quantum hardware, algorithms and software have created great opportunities for scientific discovery and industrial innovation. What can be observed is that quantum technology is entering a new phase where systems are becoming the central focus. Progress is no longer defined only by better qubits, individual algorithms or isolated demonstrations, but increasingly by whether quantum components can be integrated with classical computing, software, networks, sensing platforms and real applications. This transition, from quantum components to quantum systems, is changing both the research questions and the criteria by which progress should be judged.
State of the art
When ERCIM News last devoted a Special Theme to quantum computing, in January 2022, the field was still largely organised around individual building blocks: improving quantum hardware, developing algorithms and software stacks, identifying promising applications, and asking when quantum advantage might become possible [1]. These questions remain central, but four years later a broader transition is becoming visible.
Quantum technology is entering a systems phase. Progress is defined not only by better qubits, individual algorithms or isolated demonstrations, but increasingly by whether quantum components can be integrated with classical computing, software, networks, sensing platforms and real applications. This transition from quantum components to quantum systems is changing both the research questions and the criteria by which progress should be judged.
Quantum processors are increasingly considered as specialised accelerators connected to HPC and classical computing resources rather than as stand-alone machines. Quantum algorithms are embedded into classical optimisation, machine-learning, simulation and control workflows. Quantum communication is moving from individual links towards networks that must coexist with conventional telecommunications, key management and post-quantum cryptography. Quantum software research is expanding from circuit construction towards abstraction, interoperability, verification and composability. In sensing, longer-term visions increasingly combine sensing, communication and information processing within a single architecture.
This system perspective also changes how progress should be evaluated. Quantum algorithms need strong classical baselines, quantum processors must be assessed as parts of complete workflows, and quantum key distribution (QKD), quantum software and sensing technologies must function within operational infrastructures. European policy and infrastructure increasingly reflect this broader view. The Quantum Europe Strategy adopted in 2025 connects research and innovation with infrastructure, ecosystem development, skills and emerging applications [2], while EuroHPC is integrating several quantum computers with European supercomputing resources [3].
The contributions in this Special Theme reflect the same transition. They do not point towards a single route to quantum advantage or one dominant technology. Instead, they show how quantum effects, devices and algorithms may become components of larger computational, communication and sensing systems, and what new scientific and engineering problems arise when they do.
Main challenges
The familiar physical limitations of quantum technology remain: noise, restricted scale, limited circuit depth and the cost of fault tolerance constrain quantum computing, while communication and sensing face their own technological limitations. But system integration adds another layer of difficulty.
One central challenge is identifying where quantum resources genuinely add value. Computational hardness alone does not imply quantum advantage. Classical optimisation, simulation and machine learning benefit from mature algorithms, specialised hardware and decades of engineering. Credible quantum applications therefore require strong classical comparisons and a clear understanding of which part of a workflow might plausibly benefit from quantum processing.
A second challenge is integration. Most near- and medium-term applications are likely to be hybrid. Quantum processors must interact with CPUs, GPUs, HPC systems, classical optimisation loops, networks and domain-specific software. Performance therefore becomes an end-to-end system property: latency, communication, resource scheduling, data encoding and orchestration may matter as much as quantum execution itself.
A third challenge is software and infrastructure maturity. Quantum software remains strongly influenced by particular devices, providers and software development kits (SDKs). Quantum communication likewise needs more than secure physical-layer protocols: practical systems require interoperability, reliability, key management and integration with existing networks. Across the field, it is increasingly important to distinguish clearly between demonstrated capability, plausible research direction and longer-term vision.
Future directions
These challenges point towards a field increasingly defined by co-design. Hardware, algorithms, software and applications cannot be optimised independently. Quantum processors will increasingly be treated as specialised components within larger computing systems; quantum communication will combine quantum and classical security mechanisms; sensing architectures may integrate local information processing; and software abstractions must hide unnecessary hardware complexity without losing the information required for meaningful resource decisions.
Shared infrastructure will be particularly important because it allows these complete systems to be studied. HPC-integrated quantum computers, operational QKD testbeds, open software tools and network experimentation platforms provide environments in which assumptions about performance, scalability and interoperability can be tested.
Contributions in this issue
The contributions to this Special Theme span a wide range of topics, from foundational algorithms and simulation to quantum networks, communication and security, hybrid quantum-classical systems, quantum software engineering, sensing, and longer-term technology visions.
Several articles address the fundamental question of where quantum computation may provide useful capabilities. Halffmann argues that computational hardness alone is not enough to identify promising quantum-optimisation applications and instead emphasises problem structure and comparison with mature classical solvers. Tirado-Domínguez and colleagues introduce a constraint-aware scheduling framework that separates optimisation objectives from constraints within a hybrid QAOA formulation. Dahi and colleagues address multi-objective optimization, combining distributed execution, hardware-aware circuit design and classical assistance.
Quantum machine learning raises similar questions about representation and structure. Chakraborty focuses on the embedding of classical data into quantum states and discusses trainability, inductive bias and quantum variants of dropout as elements of a more resource-aware design methodology. Settino and colleagues explore quantum neuromorphic computing, where the dynamics, memory and dissipation of the physical quantum systems themselves become computational resources. Muñoz and Fuentes demonstrate the reverse flow of ideas: tensor-network methods originating in quantum physics are used on classical GPU hardware for exact counting of software configurations. These contributions illustrate how quantum-algorithm research is increasingly concerned with identifying useful structure rather than simply mapping hard problems onto quantum hardware.
The articles on quantum communication and security show particularly clearly the movement from components towards systems. Hübel, Kos and Ramacher report operational experience from Austria’s QCI-CAT infrastructure, where QKD has been combined with post-quantum cryptography, key-management systems and governmental and medical applications. Raubitzek and colleagues ask what scaling towards national QKD infrastructure would require in terms of nodes, fibre and equipment.
Cicconetti and Zavatta describe an Italian inter-regional quantum-network testbed initially based on commercial QKD but designed to evolve towards quantum repeaters and Quantum Internet experimentation. Kanitschar and Pacher present continuous-variable QKD and its potential compatibility with existing optical-telecommunication technology. Rios and Montenegro approach quantum-safe communication from the complementary perspective of post-quantum cryptography, showing through end-to-end benchmarking that deployment bottlenecks can arise as much from protocols, software and networks as from cryptographic primitives themselves. Together, these contributions move the discussion from individual security mechanisms towards complete quantum-safe communication infrastructures.
The system perspective is equally apparent in hybrid quantum-classical applications. Stamatiou and Magoutis embed quantum annealers within model-predictive control loops and show that surrounding communication and infrastructure can dominate the quantum-computation latency. Salatino and colleagues divide chaotic forecasting between a quantum reservoir providing nonlinear information processing and a classical recurrent component providing memory.
Barbierato and Mihalachi examine quantum generative models and quantum Monte Carlo methods in financial applications. Ciaramella and colleagues investigate quantum machine learning for malware analysis, including cases where classical baselines remain more accurate or dramatically faster – an important result in a field where realistic comparison is essential. Pilato and colleagues explore hybrid and quantum-inspired representations for reactive robotics. Collectively, these articles reinforce the view that useful near-term quantum technologies are more naturally understood as components inside classical workflows than as replacements for complete classical systems.
As these systems become more complex, quantum software engineering becomes increasingly important. Alvarado-Valiente and colleagues revisit the historical software crisis and argue that engineering practices should mature alongside quantum hardware rather than after it. Aparicio-Morales and colleagues explore service-oriented architectures for exposing, selecting and orchestrating heterogeneous quantum capabilities through higher-level interfaces.
Zayas-Gallardo and colleagues use grammar-based genetic programming to automate the synthesis and optimisation of quantum oracles. Källman examines composability from another direction: when the output of one quantum model becomes the input of another, preserving a single observable is insufficient; what matters is the reduced quantum state exposed at the component boundary. Pilato and colleagues investigate quantum logic programming in Prolog as a higher-level route from logical descriptions towards quantum operations. Larsen and colleagues transfer established formal-methods techniques, including model reduction, model checking and satisfiability solving, to quantum-circuit simulation and verification. Together, these contributions move quantum software beyond the programming of individual circuits towards components that can be abstracted, composed, optimised and verified.
The final contributions extend the systems perspective towards sensing and longer-term architectures. Wendin presents the Quantum Retina, a vision in which arrays of quantum sensors are progressively integrated with information processing and communication, ultimately treating sensing and computation as parts of one architecture. Hein and Shneider examine cryogenic photonic computing in space: primarily a classical photonic architecture in the nearer term, but also a possible infrastructure pathway towards future photonic quantum processing and networking.
Taken together, the contributions in this issue show a field becoming broader, more concrete and more engineering-driven. Progress in quantum technology still depends critically on better devices and deeper understanding of quantum phenomena, but useful quantum technology will not emerge from the quantum component alone. It will depend on how that component interacts with algorithms, classical computing, software, networks, sensors and applications.
The transition from quantum components to quantum systems may therefore be one of the defining challenges of the coming years. The articles collected here approach that transition from very different directions: improving algorithms and abstractions, integrating quantum technologies with existing infrastructure, identifying bottlenecks and limitations, and exploring architectures whose enabling components are only beginning to emerge. Together, they illustrate an important change in the field: the question is increasingly not simply what quantum technology can do, but how quantum technology can become a useful part of the systems we build.
References
[1] S. Ali and S. Selstø, “Quantum Computing – Introduction to the Special Theme,” ERCIM News, No. 128, January 2022.
[2] European Commission, Quantum Europe Strategy: Quantum Europe in a Changing World, COM(2025) 363, 2 July 2025.
[3] EuroHPC Joint Undertaking, “EuroHPC JU Opens Access to its Quantum Computers,” 25 June 2026. The access programme initially included superconducting, photonic and trapped-ion systems and forms part of EuroHPC’s explicit strategy of integrating quantum computers with European HPC infrastructure.
Please contact:
Miroslav Dobsicek, RISE, Sweden
Sebastian Raubitzek, SBA Research, Austria
Filippo Vella, CNR-ICAR, Italy