DOE funds Khatiwada’s work on quantum dark matter detectors

Credit: JJ Starr, Fermilab · news.fnal.gov

Rakshya Khatiwada of Fermi National Accelerator Laboratory and Illinois Institute of Technology has received a U.S. Department of Energy Early Career Award to pursue a novel approach for detecting dark matter. Khatiwada will employ a four-qubit entangled system to determine if quantum entanglement can improve detection beyond that of individual qubits, tackling a mystery where dark matter comprises five times more of the universe’s matter than ordinary matter.

“These entangled qubits share a single quantum state,” Khatiwada explained, “So, if they all encounter the same dark matter signal, their response is enhanced.” The project aims to detect candidate particles like axions and dark photons.

Entangled Qubit System for Dark Matter Signal Detection

This approach uses the interconnectedness of entangled particles, where the quantum state of each qubit is linked to the others, potentially amplifying faint signals from dark matter interactions. The system will be exposed to a simulated dark matter signal, allowing researchers to assess the sensitivity enhancement provided by entanglement.

Detecting dark matter requires innovative instrumentation because of the exceedingly faint signals expected from interactions with ordinary matter and electromagnetic fields; Khatiwada explained, “Searching for dark matter requires a different approach.” This focus allows for the development of detectors tuned to the expected properties of these candidates, increasing the likelihood of detection. Khatiwada’s prior work with quantum amplifiers for the Axion Dark Matter eXperiment (ADMX) informed her current approach, demonstrating the potential of quantum sensing for detecting these elusive particles.

Resonant-cavity experiments like ADMX are sensitive to narrow bands of dark matter mass, necessitating precise tuning to scan a broad range of masses over time. The Superconducting Quantum Materials and Systems Center (SQMS) at Fermilab, supported by the Department of Energy Office of Science, provides important infrastructure and expertise for this project, alongside collaborative efforts with over 30 partner institutions.

SQMS uses Fermilab’s accelerator technology to engineer multiqubit quantum processor platforms based on superconducting qubits. Khatiwada’s recent work at Fermilab has focused on achieving very low-noise performance with superconducting qubits in the QUIET and LOUD facilities, and she is also applying super intelligence and machine learning to study extremely difficult-to-detect noise sources.

Her ongoing efforts to characterize and mitigate noise are critical for the success of the entanglement project, as even the faintest signals can be obscured by background interference. “We already see some signatures of dark matter from astrophysical observations like gravitational lensing and cosmic microwave background radiation,” Khatiwada added.

Khatiwada expressed enthusiasm for applying her accumulated knowledge to this new endeavor; “I am really excited about applying these ideas that I have been working on to entanglement-based sensing for dark matter.” This combination of advanced facilities and collaborative expertise positions Fermilab as an ideal location for pursuing this ambitious research program.

We already see some signatures of dark matter from astrophysical observations like gravitational lensing and cosmic microwave background radiation.

Rakshya Khatiwada, Physicist at Fermi National Accelerator Laboratory and Illinois Institute of Technology
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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