Princeton researchers have created a superconducting qubit that stays stable more than three times longer than previous designs, marking a major leap toward practical quantum computers. Credit: Shutterstock
Princeton engineers extended qubit lifetimes using a new tantalum-silicon design that sharply cuts energy loss. The improvement could enable large, stable quantum processors capable of real-world problem solving.
Princeton engineers have taken a significant step toward developing useful quantum computers by creating a superconducting qubit that remains stable for three times longer than the strongest versions available today.
“The real challenge, the thing that stops us from having useful quantum computers today, is that you build a qubit and the information just doesn’t last very long,” said Andrew Houck, leader of a federally funded national quantum research center, Princeton’s dean of engineering and co-principal investigator on the paper. “This is the next big jump forward.”
The breakthrough was described in the November 5 issue of Nature, where the team reported that their qubit stays coherent for more than 1 millisecond. This represents the longest lifetime ever demonstrated in laboratory tests and is nearly fifteen times better than what is typically used in commercial-scale processors. To confirm the improvement, the researchers built a working quantum chip using the new design, overcoming one of the main limitations that prevent reliable error correction and large-scale quantum systems.
The team explained that their qubit uses an architecture similar to the systems developed by Google and IBM, making it compatible with existing processor designs. Houck said that replacing parts of Google’s Willow processor with Princeton’s components could make it operate 1,000 times more effectively. He added that the advantages of the new approach grow even more quickly as more qubits are added, increasing the overall impact in larger systems.
A Princeton team has reported their new qubit lasts for over 1 millisecond, three times longer than the best ever reported in a lab setting, and nearly fifteen times longer than the industry standard for large-scale processors. Credit: Copyright Princeton University; Office of Communications; Matt Raspanti (2025)
Plug-and-Play Design Compatible With Google and IBM Systems
Quantum computers are expected to handle challenges that traditional computers cannot, but current devices are still far from reaching that goal. One of the biggest limitations comes from the qubit itself, which typically loses its information before meaningful calculations are completed.
Increasing coherence time, or how long a qubit can maintain its quantum state, is a critical requirement for progress. The improvement demonstrated by the Princeton qubit is the largest jump in coherence time achieved in more than ten years.
Why Qubit Coherence Is the Biggest Barrier to Quantum Computing
Many groups are exploring different approaches to qubit design, but Princeton’s device is based on a well-known option called the transmon qubit. These qubits, also used by Google and IBM, rely on superconducting circuits that operate at extremely cold temperatures. They offer solid protection from environmental noise and work well with today’s manufacturing processes.
However, it has been extremely difficult to increase the coherence time of transmon qubits. Google’s recent research concluded that progress in their most advanced processor is now limited mainly by the quality of the materials used to build the qubits.
Transmon Qubits: Strengths, Weaknesses, and Google’s Material Limits
The Princeton team took a two-pronged approach to redesigning the qubit. First, they used a metal called tantalum to help the fragile circuits preserve energy. Second, they replaced the traditional sapphire substrate with high-quality silicon, the standard material of the computing industry. To grow tantalum directly on silicon, the team had to overcome a number of technical challenges related to the materials’ intrinsic properties. But ultimately they prevailed, unlocking the deep potential of this combination.
Nathalie de Leon, the co-director of Princeton’s Quantum Initiative and co-principal investigator of the new qubit, said that not only does their tantalum-silicon chip outperform existing designs, but it’s also easier to mass-produce. “Our results are really pushing the state of the art,” she said.
Michel Devoret, chief scientist for hardware at Google Quantum AI, which partially funded the research, said that the challenge of extending the lifetimes of quantum computing circuits had become a “graveyard” of ideas for many physicists. “Nathalie really had the guts to pursue this strategy and make it work,” said Devoret, a recipient of the 2025 Nobel Prize in physics.
The research was primarily funded by the U.S. Department of Energy National Quantum Information Science Research Centers and the Co-design Center for Quantum Advantage (C2QA) — a center that Houck directed from 2021 to 2025, and where he is now chief scientist. The paper’s co-lead authors are postdoctoral researcher Faranak Bahrami and graduate student Matthew P. Bland.
How Tantalum Reduces Energy Loss and Error Rates
Houck, the Anthony H.P. Lee ’79 P11 P14 Professor of Electrical and Computer Engineering, said a quantum computer’s power hinges on two factors. The first is the total number of qubits that are strung together. The second is how many operations each qubit can perform before errors take over. By improving the quality of individual qubits, the new paper advances both. Specifically, a longer-lasting qubit helps resolve the industry’s greatest obstacles: scaling and error correction.
The most common source of error in these qubits is energy loss. Tiny, hidden surface defects in the metal can trap and absorb energy as it moves through the circuit. This causes the qubit to rapidly lose energy during a calculation, introducing errors that multiply as more qubits are added to a chip. Tantalum typically has fewer of these defects than more commonly used metals like aluminum. Fewer errors also make it easier for engineers to correct those that do occur.
Chasing Down Hidden Defects and Substrate Losses
Houck and de Leon, who is an associate professor of electrical and computer engineering, first introduced the use of tantalum for superconducting chips in 2021 in collaboration with Princeton chemist Robert Cava, the Russell Wellman Moore Professor of Chemistry. Despite having no background in quantum computing, Cava, an expert on superconducting materials, had been inspired by a talk de Leon had delivered a few years earlier, and the two struck up an ongoing conversation about qubit materials. Eventually, Cava pointed out that tantalum could provide more benefits and fewer downsides. “Then she went and did it,” Cava said, referring to de Leon and the broader team. “That’s the amazing part.”
Researchers from all three labs followed Cava’s intuition and built a superconducting tantalum circuit on a sapphire substrate. The design demonstrated a significant boost in coherence time, in line with the world record.
Tantalum’s main advantage is that it’s exceptionally robust and can survive the harsh cleaning needed for removing contamination from the fabrication process. “You can put tantalum in acid, and still the properties don’t change,” said Bahrami, co-lead author on the new paper.
Once the contaminants were removed, the team then came up with a way to measure the next sources of energy loss. Most of the remaining loss came from the sapphire substrate. They replaced the sapphire with silicon, a material that is widely available with extremely high purity.
Combining these two materials while refining manufacturing and measurement techniques has led to one of the largest single improvements in the transmon’s history. Houck called the work “a major breakthrough on the path to enabling useful quantum computing.”
Because the improvements scale exponentially with system size, Houck said that swapping the current industry best for Princeton’s design would enable a hypothetical 1,000-qubit computer to work roughly 1 billion times better.
Toward Billion-Fold Improvements in Scaled Quantum Systems
The work brings together deep expertise in quantum device design and materials science. Houck’s group specializes in building and optimizing superconducting circuits; de Leon’s lab focuses on quantum metrology and the materials and fabrication processes that underpin qubit performance; and Cava’s research team has spent three decades at the forefront of superconducting materials. Combining their expertise has yielded results that couldn’t have been accomplished alone. These results have now attracted industry attention.
Devoret, the Google scientist, who is also a professor of physics at the University of California-Santa Barbara, said that partnerships between universities and industry are important for advancing the frontiers of technology. “There is a rather harmonious relationship between industry and academic research,” he said. University labs are well positioned to focus on the fundamental aspects that limit the performance of a quantum computer, while industry scales up those advances into large-scale systems.
University–Industry Collaboration Accelerates Quantum Innovation
“We’ve shown that it’s possible in silicon,” said de Leon. “The fact that we’ve shown what the critical steps are, and the important underlying characteristics that will enable these kinds of coherence times, now makes it pretty easy for anyone who’s working on scaled processors to adopt.”
Reference: “Millisecond lifetimes and coherence times in 2D transmon qubits” by Matthew P. Bland, Faranak Bahrami, Jeronimo G. C. Martinez, Paal H. Prestegaard, Basil M. Smitham, Atharv Joshi, Elizabeth Hedrick, Shashwat Kumar, Ambrose Yang, Alexander C. Pakpour-Tabrizi, Apoorv Jindal, Ray D. Chang, Guangming Cheng, Nan Yao, Robert J. Cava, Nathalie P. de Leon and Andrew A. Houck, 5 November 2025, Nature.
DOI: 10.1038/s41586-025-09687-4
The paper “Millisecond lifetimes and coherence times in 2D transmon qubits” was published in Nature on Nov. 5. Besides de Leon, Houck, Cava, Bahrami, and Bland, authors include Jeronimo G.C. Martinez, Paal H. Prestegaard, Basil M. Smitham, Atharv Joshi, Elizabeth Hedrick, Alex Pakpour-Tabrizi, Shashwat Kumar, Apoorv Jindal, Ray D. Chang, Ambrose Yang, Guangming Cheng and Nan Yao. This work was primarily supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage (C2QA), and was partially supported by Google Quantum AI.
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