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Quantum networks bring new precision to dark matter searches

Quantum networks bring new precision to dark matter searches
(Top left) Composition of the universe, showing that dark matter accounts for about 27%. (Top right) Proposed quantum sensor network, where superconducting qubits are connected in different graph structures. (Bottom) Estimation results demonstrating agreement with the true value, along with a comparison against quantum bounds. Credit: Âé¶¹ÒùÔºical Review D (2025). DOI: 10.1103/rv43-54zq

Detecting dark matter—the mysterious substance that holds galaxies together—is one of the greatest unsolved problems in physics. Although it cannot be seen or touched directly, scientists believe dark matter leaves weak signals that could be captured by highly sensitive quantum devices.

In a published in Âé¶¹ÒùÔºical Review D, researchers at Tohoku University propose a way to boost the sensitivity of quantum sensors by connecting them in carefully designed network structures. These quantum sensors use the rules of quantum physics to detect extremely small signals, making them far more sensitive than ordinary sensors. Using these, accurately detecting the faint clues left behind from dark matter could finally become possible.

The study focuses on , which are tiny electric circuits cooled to very low temperatures. These qubits are normally used as building blocks of quantum computers, but here they act as powerful quantum sensors. Just as a team working together can achieve more than a single person, linking many of these superconducting qubits in an optimized network allows them to detect weak dark matter signals much more effectively than any single sensor could on its own.

The team tested different network patterns, such as ring, line, star, and fully connected graphs, using systems of four and nine qubits. They then applied variational quantum metrology (a method similar to training a machine-learning model) to optimize how the quantum states were prepared and measured. To refine the results, Bayesian estimation was used to filter out noise, much like sharpening a blurry image.

The findings were striking: optimized networks consistently outperformed traditional methods, even when realistic noise was introduced. This shows the approach can work on today's quantum devices.

"Our goal was to figure out how to organize and fine-tune quantum sensors so they can detect dark matter more reliably," said Dr. Le Bin Ho, lead author of the study. "The network structure plays a key role in enhancing sensitivity, and we've shown it can be done using relatively simple circuits."

Beyond , these quantum sensor networks could advance technologies such as quantum radar, gravitational wave detection, and ultra-precise timekeeping. Furthermore, they may one day improve GPS accuracy, enhance brain imaging with MRI, or help detect hidden underground structures.

"This research shows that carefully designed quantum networks can push the boundaries of what is possible in precision measurement," Dr. Ho added. "It opens the door to using not just in laboratories, but in real-world tools that require extreme sensitivity."

Looking ahead, the team plans to extend this approach to larger networks and explore ways to make the sensors more resistant to noise.

More information: Adriel I. Santoso et al, Optimized quantum sensor networks for ultralight dark matter detection, Âé¶¹ÒùÔºical Review D (2025).

Journal information: Âé¶¹ÒùÔºical Review D

Provided by Tohoku University

Citation: Quantum networks bring new precision to dark matter searches (2025, October 17) retrieved 17 October 2025 from /news/2025-10-quantum-networks-precision-dark.html
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