Princeton’s new quantum chip marks a major step toward quantum advantage

"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."

In a Nov. 5 article published in Nature, the Princeton team reported that their qubit maintains coherence for more than 1 millisecond. This performance is triple the longest lifetime documented in laboratory experiments and nearly fifteen times greater than the standard used in industrial quantum processors. To confirm the result, the team constructed a functioning quantum chip based on the new qubit, demonstrating that the design can support error correction and scale toward larger systems.

The researchers noted that their qubit is compatible with the architectures used by major companies such as Google and IBM. According to their analysis, replacing key components in Google's Willow processor with Princeton's approach could increase its performance by a factor of 1,000. Houck added that as quantum systems incorporate more qubits, the advantages of this design increase even more rapidly.

Why Better Qubits Matter for Quantum Computing

Quantum computers show promise for solving problems that traditional computers cannot address. Yet their current abilities remain limited because qubits lose their information before complex calculations can be completed. Extending coherence time is therefore essential for building practical quantum hardware. Princeton's improvement represents the largest single gain in coherence time in more than ten years.

Many labs are pursuing different qubit technologies, but Princeton's design builds on a widely used approach known as the transmon qubit. Transmons, which operate as superconducting circuits held at extremely low temperatures, are known for being resistant to environmental interference and compatible with modern manufacturing tools.

Despite these strengths, increasing the coherence time of transmon qubits has proven difficult. Recent results from Google showed that material defects now pose the main barrier to improving their newest processor.

Tantalum and Silicon: A New Materials Strategy

The Princeton team developed a two-part strategy to address these material challenges. First, they incorporated tantalum, a metal known for helping delicate circuits retain energy. Second, they replaced the standard sapphire substrate with high-purity silicon, a material foundational to the computing industry. Growing tantalum directly on silicon required solving several technical problems related to how the two materials interact, but the researchers succeeded and uncovered significant advantages in the process.

Nathalie de Leon, co-director of Princeton's Quantum Initiative and co-principal investigator of the project, said the tantalum-silicon design not only performs better than previous approaches but is also simpler to manufacture at scale. "Our results are really pushing the state of the art," she said.

Michel Devoret, chief scientist for hardware at Google Quantum AI, which provided partial funding, described the difficulty of extending the lifetime of quantum circuits. He noted that the challenge had become a "graveyard" of attempted solutions. "Nathalie really had the guts to pursue this strategy and make it work," said Devoret, the 2025 Nobel Prize winner in physics.

The project received primary funding from the U.S. Department of Energy National Quantum Information Science Research Centers and the Co-design Center for Quantum Advantage (C2QA), a center directed by Houck from 2021 to 2025 and where he now serves as chief scientist. The paper lists postdoctoral researcher Faranak Bahrami and graduate student Matthew P. Bland as co-lead authors.

How Tantalum Improves Qubit Stability

Houck, the Anthony H.P. Lee '79 P11 P14 Professor of Electrical and Computer Engineering, explained that a quantum computer's capability depends on two main factors. One is the total number of qubits that can be linked together. The other is how many operations each qubit can complete before errors accumulate. Improving the durability of a single qubit strengthens both of these factors. Longer coherence time directly supports scaling and more reliable error correction.

Energy loss is the most common cause of failure in these systems. Microscopic surface defects in the metal can trap energy and disrupt the qubit during calculations. These disruptions multiply as more qubits are added. Tantalum is especially beneficial because it typically contains fewer of these defects than metals like aluminum. With fewer defects, the system produces fewer errors and simplifies the process of correcting the ones that remain.

Houck and de Leon introduced tantalum for superconducting chips in 2021 with help from Princeton chemist Robert Cava, the Russell Wellman Moore Professor of Chemistry. Cava, who specializes in superconducting materials, became interested in the problem after hearing one of de Leon's talks. Their conversations eventually led him to suggest tantalum as a promising material. "Then she went and did it," Cava said. "That's the amazing part."

Researchers across all three labs followed this idea and built a tantalum-based superconducting circuit on a sapphire substrate. The result showed a significant improvement in coherence time, approaching the previous world record.

Bahrami noted that tantalum stands out because it is extremely durable and can withstand the harsh cleaning used to remove contamination during fabrication. "You can put tantalum in acid, and still the properties don't change," she said.

Once contaminants were removed, the team evaluated the remaining energy losses. They found that the sapphire substrate was responsible for most of the remaining problems. Switching to high-purity silicon eliminated that source of loss, and the combination of tantalum and silicon, along with refined fabrication techniques, produced one of the biggest improvements ever achieved in a transmon qubit. Houck described the outcome as "a major breakthrough on the path to enabling useful quantum computing."

Houck added that because the benefits of the design increase exponentially as systems grow, replacing today's industry-leading qubits with the Princeton version could allow a theoretical 1,000-qubit computer to operate about 1 billion times more effectively.

Silicon-Based Design Supports Industry-Scale Growth

The project draws from three areas of expertise. Houck's group focuses on the design and optimization of superconducting circuits. De Leon's lab specializes in quantum metrology along with the materials and fabrication methods that determine qubit performance. Cava's group has spent decades developing superconducting materials. By combining their strengths, the team produced results that none of the groups could have achieved individually. Their success has already attracted attention from the quantum industry.

Devoret said collaborations between universities and companies are essential for moving advanced technologies forward. "There is a rather harmonious relationship between industry and academic research," he said. University researchers can investigate the fundamental limits of quantum performance, while industry partners apply those findings to large-scale systems.

"We've shown that it's possible in silicon," de Leon said. "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."

The paper "Millisecond lifetimes and coherence times in 2D transmon qubits" was published in Nature on Nov. 5. Along with de Leon, Houck, Cava, Bahrami, and Bland, the 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 research received primary support from the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage (C2QA), and partial support from Google Quantum AI.