Quantum technology is accelerating out of the lab and into the real world, and a new article in Science argues that the field now stands at a turning point—one that is similar to the early computing age that preceded the rise of the transistor and modern computing.
The article, authored by scientists from University of Chicago, Stanford University, the Massachusetts Institute of Technology, the University of Innsbruck in Austria, and the Delft University of Technology in the Netherlands, offers an assessment of the rapidly advancing field of quantum information hardware, outlining the major challenges and opportunities shaping scalable quantum computers, networks, and sensors. The paper appears in the December 4, 2025, issue of Science.
“This transformative moment in quantum technology is reminiscent of the transistor’s earliest days,” said lead author David Awschalom, the Liew Family Professor of molecular engineering and physics at the University of Chicago, and director of the Chicago Quantum Exchange and the Chicago Quantum Institute. “The foundational physics concepts are established, functional systems exist, and now we must nurture the partnerships and coordinated efforts necessary to achieve the technology’s full, utility-scale potential. How will we meet the challenges of scaling and modular quantum architectures?”
Over the past decade, quantum technologies have transitioned from fundamental laboratory demonstrations to systems capable of enabling early real-world applications in areas such as communication, sensing, and computing. The authors note that this rapid maturation has been driven by the same tri-sector collaboration that fueled the rise of microelectronics: strong ties among academia, government, and industry.
Comparing platforms
The article surveys the current state of six leading quantum hardware platforms, including superconducting qubits, trapped ions, spin defects, semiconductor quantum dots, neutral atoms, and optical photonic qubits. To compare the progress between these platforms across the applications of computing, simulation, networking, and sensing, the authors used large language AI models such as ChatGPT and Gemini to assess the relative technology-readiness level (TRL) of each. TRLs evaluate the maturity of a technology on a scale of 1 (basic principles observed in a lab environment) to 9 (proven in an operational environment), though a higher TRL could still apply to an early-stage technology that has demonstrated a higher level of system sophistication.
The results offer a comparative snapshot of the field’s progress. Although advanced prototypes have demonstrated system operation and public cloud access, their raw performance remains early in development. For example, many meaningful applications, including large-scale quantum chemistry simulations, could require millions of physical qubits with error performance far beyond what is technologically viable today.
Context, therefore, is essential when evaluating technology readiness, said coauthor William D. Oliver, the Henry Ellis Warren (1894) Professor of electrical engineering and computer science, professor of physics, and director of the Center for Quantum Engineering at MIT.
“While semiconductor chips in the 1970s were TLR-9 for that time, they could do very little compared with today’s advanced integrated circuits,” he said. “Similarly, a high TRL for quantum technologies today does not indicate that the end goal has been achieved, nor does it indicate that the science is done and only engineering remains. Rather, it reflects a significant, yet relatively modest, system-level demonstration has been achieved—one that still must be substantially improved and scaled to realize the full promise.”
Assessing challenges by looking to history
The highest TRL scores went to superconducting qubits for quantum computing, neutral atoms for quantum simulation, photonic qubits for quantum networking, and spin defects for quantum sensing.
The authors identify several overarching challenges that must be addressed for quantum systems to scale effectively. Significant advancements in materials science and fabrication are required to enable consistent, high-quality, mass-producible devices that can be manufactured through reliable and cost-effective foundry processes. Wiring and signal delivery remain a central engineering bottleneck; most quantum platforms still require individual control channels for most qubits, and simply increasing the number of wires is not sustainable as these systems attempt to scale to the millions of qubits. (Similar problems were faced in the 1960s by computer engineers, known as the tyranny of numbers.) Power delivery, temperature management, automated calibration, and system control all pose related challenges, which will demand continuous advances as systems grow in complexity.
The article connects these engineering needs to lessons from the history of computing. Many of the most transformative developments in classical electronics—from the introduction of lithography to novel transistor materials—took years or decades to transition from laboratory research to industrial deployment. The authors argue that progress in quantum technologies will follow a similar arc. They emphasize the importance of system-level, top-down design strategies, a shared body of open scientific knowledge that avoids premature siloing, and…patience.
“Patience has been a key element in many landmark developments,” they write, “and points to the importance of tempering timeline expectations in quantum technologies.”