Texas A&M Researchers Build Cryogenic Quantum Detectors for Dark Matter

Researchers are tackling physics and mathematical bottlenecks using advanced particle detectors, quantum algorithms, and photonic circuits. Scientists at Texas A&M University are building cryogenic semiconductor detectors to search for dark matter, while teams across Europe develop quantum computers and software to factor group representations and simulate plasma.

Building Ultra-Sensitive Cryogenic Detectors at Texas A&M University

Most of the universe remains unexplained. About 95 percent of everything that exists consists of dark matter and dark energy, leaving just 5 percent as familiar matter. Dr. Rupak Mahapatra, an experimental particle physicist at Texas A&M University, compares humanity’s limited grasp of the cosmos to a parable. It’s like trying to describe an elephant by only touching its tail, he noted, explaining that researchers sense something massive but grasp only a tiny part of it.

Mahapatra and his collaborators published findings in Applied Physics Letters detailing semiconductor detectors that rely on cryogenic quantum sensors. Dark matter accounts for roughly 27 percent of the universe’s total energy, holding cosmic structures together, while dark energy drives the accelerating expansion of the universe at about 68 percent. Because neither substance absorbs, reflects, or gives off light, researchers study their gravitational influence.

At Texas A&M, Mahapatra’s team builds detectors designed to register rare interactions from particles that interact weakly with ordinary matter. The challenge is that dark matter interacts so weakly that we need detectors capable of seeing events that might happen once in a year, or even once in a decade, Mahapatra said. For the past 25 years, Mahapatra has also worked with the SuperCDMS experiment, introducing voltage-assisted calorimetric ionization detection in a landmark 2014 paper published in Physical Review Letters to investigate low-mass WIMPs.

Solving Century-Old Math Problems With Quantum Fourier Transforms

While particle physicists hunt for invisible matter, other researchers target computational bottlenecks that limit complex scientific simulations.

Texas A&M Researchers Build Cryogenic Quantum Detectors for Dark Matter — Photo: Frontiersin

Group representations describe all possible transformations or arrangements of a system, such as atoms in a crystal, and can be broken down into fundamental building blocks known as irreducible representations. Counting these multiplicity numbers becomes exceedingly difficult for classical computers as systems grow more complex. The new research proves that quantum algorithms using quantum Fourier transforms perform this factorization efficiently.

This efficiency has direct applications across scientific disciplines. In particle physics, the technique helps calibrate sensitive particle detectors. In material science, it aids in understanding material properties to design new ones, while data science applies it to robust error-correcting codes for data transmission. Researchers emphasize that finding these quantum speedups represents the core goal of quantum computing research.

Developing Glass Photonic Chips and Plasma Simulations in Europe

Efforts to scale quantum hardware are taking distinct physical forms across Europe. Giulia Acconcia coordinates the EU-funded research initiative QLASS, managed by the Fondazione Politecnico di Milano, which brings together research facilities and small-to-medium enterprises in France, Italy, and Germany. The initiative leverages glass chips built by the Italian company Ephos to develop a photonic quantum computer.

Unlike silicon chips that rely on moving electrons, photonic quantum computers use light particles to process information. Ephos manufactures chips featuring up to 200 reconfigurable optical modes using a laser writing technique. You have to use materials that can transmit light, Acconcia explained, This is challenging because you have to confine light, but avoid absorption.

Concurrently, plasma physics researchers explore quantum computing to model fusion energy and high-energy systems. Simulating plasma turbulence, Magnetohydrodynamic instabilities, and wave-particle interactions requires substantial computational resources because plasma dynamics are nonlinear. Integrating quantum computing into plasma research aims to solve large-scale linear equations and optimize complex systems with near-quantum efficiency, supporting the long-term development of sustainable fusion reactors.