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Credit: Quantum Machines

Clocking Quantum Instability: A New Process Could Open Pathways to Reliable Quantum Computing

In the quest to move quantum computing out of laboratory curiosity and into practical use, an international team of researchers has finally cracked the code on how long it takes for information to be lost.

While quantum computing qubits allow for many operations to run simultaneously, they quickly decay into noise, losing their information. Now, quantum computer researchers under the Niels Bohr Institute in Copenhagen have revealed a method to accurately calculate the lifespan of quantum data in a new paper published in Physical Review X, forming a bridge to the next phase of research.

Quantum Computing Instability

Quantum computing offers a promising solution for exploring the most complex problems in our universe. Due to its ability to perform many complex calculations simultaneously or process entire databases, the technology could theoretically perform certain tasks much faster than traditional computers, which operate sequentially on binary code. Additionally, as scientists tackle complex quantum problems themselves, this allows for a much truer understanding than the approximations required to transform a quantum problem into traditional binary code. 

Yet the qubits that store the data remain a major stumbling block. While they offer many benefits for efficiently solving complex problems, they are extremely unreliable.

“In quantum computers, information is transmitted and stored using so-called qubits (quantum bits). But quantum information can quickly be lost,” said co-author Jeroen Danon, a professor at the Norwegian University of Science and Technology (NTNU) Department of Physics.

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Quantum computing remains locked in the laboratory for now due to instability issues. Credit: Quantum Machines

Unpredictable Instability in Quantum Computing

The greatest problem may not even be the instability itself, but its unpredictable nature. If quantum computing researchers knew exactly how long the data has a lifespan, they could work to mitigate the issue.

“In the widely used superconducting qubits, the time it takes for information to disappear is, on average, reasonable. But it seems to vary randomly over time,” explained Danon. “So, it is really unfortunate that we do not even have any fast but reliable measurement methods to determine how long it takes before information in qubits is lost.”

“It is, of course, absolutely necessary to resolve this problem to be able to get quantum computers to operate more stably than they currently do,” Danon added. “In collaboration with an international team led by the Niels Bohr Institute in Copenhagen, we have developed a new measurement method. It enables us to measure the time it takes to lose information with unparalleled speed and accuracy.”

Clocking Quantum Computing Decay

Previous work has developed methods to determine how long qubit information will persist, but these methods were far too slow. Although a speed of one second may sound fast, it falls far behind the many rapid calculations that make quantum computing useful in the first place. 

“We managed to do it in approximately 10 milliseconds, i.e., more than 100 times faster. And more or less in real time,” Danon said.

This new resolution allows for much tighter monitoring of the data fidelity, illuminating extremely brief and minute changes.

“This will in turn make it easier to identify the underlying causes that make the information disappear,” he said.

With this new work, quantum computing researchers have a valuable new tool to calibrate and test quantum processors. Doing so will enable further research into the quantum processes underlying the technology, enabling further improvements.

The paper, “Real-Time Adaptive Tracking of Fluctuating Relaxation Rates in Superconducting Qubits,” appeared in Physical Review X on February 13, 2026.

Ryan Whalen covers science and technology for The Debrief. He holds an MA in History and a Master of Library and Information Science with a certificate in Data Science. He can be contacted at ryan@thedebrief.org, and follow him on Twitter @mdntwvlf.