Scalable manufacturing processes for quantum computing

Amin Hashemian

Hiep. L. Thi

Thorsten Last, Massimo Mongillo, Tsvetan Ivanov +6 more

Computational ecosystems in which classical supercomputers and general-purpose quantum computers provide a steady increase in value-creating computation capabilities have shown immense progress in recent years. Superconducting qubit technology, in particular, has emerged as a leading candidate for realizing a scalable quantum computing platform ready for paving the way to commercial quantum advantage. However, current academic approaches in fabrication and testing of quantum devices are not scalable and have already started to limit the rapid development of the field. Novel solutions are required to tackle the combined challenge of increasing the qubit count on a quantum processor and the need to further reduce the qubit’s error rates. This, in turn, will lead to a renewed acceleration in qubit manufacturing, test and diagnostics. Here we present aspects of how to move superconducting qubit manufacturing and testing from small-scale laboratory to large-scale fabrication facility environments. To enable this transfer, two key ingredients are demonstrated: (i) A foundry-compatible fabrication process of superconducting qubits that can benefit from the advanced process control in industry-scale CMOS fabrication facilities, and (ii) an acceleration of testing and cryogenic measurement throughput by using a milli-Kelvin cryo-CMOS signal multiplexer operating in near proximity to quantum devices and integrated qubit diagnostic and benchmarking tools with end-to-end data analytics. Although some of these elements have been explored independently, co-development is crucial to enable an efficient scalable development cycle for quantum computing technology. A full development cycle consisting of scalable manufacturing, testing, and benchmarking will enable the large-scale fabrication and control of quantum computing devices and thus pave the way to commercial quantum advantage.

Moritz Willmann, Marcel Albus, Jan Schnabel +1 more

The even distribution and optimization of tasks across resources and workstations is a critical process in manufacturing aimed at maximizing efficiency, productivity, and profitability, known as Robotic Assembly Line Balancing (RALB). With the increasing complexity of manufacturing required by mass customization, traditional computational approaches struggle to solve RALB problems efficiently. To address these scalability challenges, we investigate applying quantum computing, particularly quantum annealing, to the real-world based problem. We transform the integer programming formulation into a quadratic unconstrained binary optimization problem, which is then solved using a hybrid quantum-classical algorithm on the D-Wave Advantage 4.1 quantum computer. In a case study, the quantum solution is compared to an exact solution, demonstrating the potential for quantum computing to enhance manufacturing productivity and reduce costs. Nevertheless, limitations of quantum annealing, including hardware constraints and problem-specific challenges, suggest that continued advancements in quantum technology will be necessary to improve its applicability to RALB manufacturing optimization.

Ganiyat Abiodun Salawu, Bright Glen

Quantum computing is increasingly seen as a disruptive technology capable of expanding the computational limits of advanced manufacturing systems within the emerging Industry 5.0 framework. By utilizing quantum mechanical principles such as superposition, entanglement, and quantum parallelism, quantum computation enables new approaches to solving complex optimization, simulation, and data-intensive problems that are challenging or impractical for classical computers. This paper offers a comprehensive and critical review of the potential impacts of quantum computing on advanced manufacturing, focusing on intelligent production planning, supply chain optimization, materials discovery, predictive maintenance, and human–machine collaboration, key aspects of Industry 5.0. The originality of this review lies in its integrated analysis of quantum computing alongside artificial intelligence, digital twins, and cyber–physical systems, highlighting how these technologies, when combined, improve decision-making speed, process efficiency, and sustainability. Despite these opportunities, the integration of quantum computing into Industry 5.0 systems faces critical challenges, including hardware limitations, algorithm scalability, data security concerns, workforce readiness, and the complexity of integrating quantum solutions with existing industrial infrastructures. The role of hybrid quantum-classical architectures is examined as a feasible and transitional approach for near-term manufacturing applications. By critically assessing both technological strengths and practical constraints, this review positions quantum computing as a promising enabler of resilient, human-centered, and sustainable manufacturing ecosystems. The insights aim to assist researchers, industry players, and policymakers in strategically managing the integration of quantum technologies as manufacturing systems advance toward Industry 5.0.

Prabhat Mishra