This Perspective analyses how historical narratives and structural separations of expertise slowed the formation of this coupling and outlines what it takes to build: explicit interfaces between technical teams and domain context and intermediate layers that translate quantum outputs into decision-relevant observables without suffocating foundational innovation.
Abstract
Quantum computing is a deep technology whose progress cannot be driven effectively from one direction alone. While the field has developed a growing catalogue of mathematically grounded algorithmic speedups, industrial impact will depend just as much on starting from real industrial decision contexts and working downward to what must be computed, validated and integrated. In this Perspective, I argue that sustained progress requires treating these two directions: bottom-up development from physics, hardware and algorithms, and top-down development from industrial needs and constraints. Equally primary and continuously coupled. This dual-viewpoint is not a matter of balance for its own sake. Quantum computers cannot solve arbitrary problems, so engagement with industry must remain anchored in algorithmic tractability. Yet tractable computations are rarely valuable unless they connect to decision points in established workflows such as candidate selection in drug discovery or the design of a new aircraft shape with improved aerodynamics. I analyse how historical narratives and structural separations of expertise slowed the formation of this coupling and outline what it takes to build it: explicit interfaces between technical teams and domain context and intermediate layers that translate quantum outputs into decision-relevant observables without suffocating foundational innovation. Framed this way, quantum computing's opportunity is clearest where deep physical modelling meets high-value decisions. Provided the field co-designs both sides from the outset.
It is argued that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods, and quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the full costs of state preparation, measurement, error handling, and coupling to classical simulation are included.
Bruno Camino, C. R. A. Catlow, J. Buckeridge et al.· 0 citations
Quantum computing has generated significant expectations within the scientific community. However, the development of noise-resilient quantum computers capable of addressing a broad class of practical problems is expected to require at least another decade. During this ongoing evolution, rigorous step-by-step tutorials are essential not only to clarify fundamental concepts but also to disseminate them beyond the core quantum computing community to the wider engineering audience interested in applications. In many engineering fields, the efficient solution of linear systems of equations is a central computational task. For this reason, although it remains an open question whether the Harrow–Hassidim–Lloyd (HHL) algorithm will ultimately play a decisive role in future fault-tolerant application-scale quantum (FASQ) computers, clear and rigorous tutorials on this paradigmatic algorithm remain of fundamental importance. While well-structured introductions to elementary implementations of HHL already exist, certain simplifying assumptions—appropriate for pedagogical clarity—may limit their extension to more general or application-oriented settings. In this work, the HHL algorithm is presented through a detailed analytical treatment, followed by a complete example including a generic module for eigenvalue inversion. A corresponding Jupyter Notebook implementation in Qiskit is provided to support practical understanding. After working through the material, readers are expected to gain a clearer appreciation of how quantum computing concepts operate in practice and why quantum approaches are of interest for combinatorial optimization and machine learning problems.
P. Asinari, Matteo Maria Piredda, Giulio Barletta et al.· IEEE Access· 0 citations
Quantum computing is now evolving in different directions, with quantum annealing and gate-model approaches tackling different types of problems. This paper looks at how the shift from D-Wave’s annealing systems to hybrid quantum-classical models changes how companies adopt these technologies. The main issue is the gap between what quantum hardware can do and what businesses need, especially for combinatorial optimization, machine learning, and cryptography.The focus includes D-Wave’s Pegasus topology processors, the 5,000-plus qubit Advantage system, and the new gate-model Gate Model 1 (Leap) integration layer. The goal is to gather real-world benchmarking data and design principles to find out which quantum method provides clear business benefits now and which challenges are still out of reach. The proposed framework connects problem structure, shown through Quadratic Unconstrained Binary Optimisation (QUBO) density, variable count, and constraint tightness, to processor suitability scores. This gives practical advice for deployment. This work is unique because it systematically compares the performance of annealing and gate models on typical industry cases. It includes methods for penalty-weight calibration, chain-break reduction through post-processing, and measuring hybrid solver delays. Simulation results show that annealing can provide time-to-solution improvements of up to 87% compared to classical branch-and-bound on dense graph problems with 512 logical variables. In contrast, gate-model circuits tackling the same problems face 3.4 times the delay due to current decoherence issues.
Akey Sungheetha, R. R.· 2026 International Conferenc...· 0 citations
Quantum computers are moving from research laboratories to industrial machines accessible via the cloud and integrated into high-performance computing facilities. However, translating theoretical quantum protocols into hardware experiments remains a major bottleneck, requiring expertise across protocol design, compilation, simulation, and cloud execution. Here, we introduce an agentic workflow that automates this pipeline on neutral-atom quantum processors (here two Pasqal QPUs available on the cloud) while keeping the researcher in the loop for critical validation. In three case studies from many-body physics and optimization, the agent went from published paper or patent to a QPU campaign run overnight. In particular, human intervention was crucial to ensure scientific validity: the agent selected an inadequate observable in one experiment and constructed a plausible but incorrect hardware diagnosis in another, with both failures detected only through domain-expert review. Finally, we use a second agent to classify a corpus of 633 Rydberg-array arXiv papers and show that nearly half are implementable on present-day QPUs while identifying specific hardware upgrades needed for the rest. Together, these results demonstrate that agentic workflows provide a practical bridge between theoretical ideas and physical hardware, opening quantum experimentation to a much broader scientific community.
Constantin Dalyac, A. Dauphin, L. Henriet et al.· 0 citations
Certain physics-based technologies (quantum computing, fusion energy, advanced materials, brain–computer interfaces) have remained “five years away” for decades. This paper argues that this perpetual horizon is not a forecasting failure but the temporal signature of Perpetual Five-Year Technologies (PFYTs): technologies of atoms, not bits, whose technical object and enabling ecosystem must co-develop. Drawing on philosophy of technology and sociology of scientific practice, and grounded in quantitative analysis of cross-platform performance data and high-impact research across hardware architectures, the paper shows that PFYT temporality is endogenous, produced by concretization dynamics and recursive constraint discovery rather than by market failures or insufficient funding. Quantum computing provides the paradigmatic case. Treating the quantum processor as a technical individual in active individuation explains why multiple computing paradigms persist and why each breakthrough resets rather than collapses the horizon. The convergence of machine learning and quantum hardware into pipelines for drug and materials discovery shows how Physical AI reshapes PFYT dynamics, compressing some cycles while introducing new forms of co-individuation between intelligence and matter. The framework generates diagnostics for physics-based frontiers where technical objects and milieus co-produce developmental time.
