Quantum Physics, Quantum Sensors

Kalman Filter Reduces Magnetic Field Drift in Quantum Gas Experiments

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August 1, 2026 by Ivy Delaney

Researchers have devised a new method for stabilizing magnetic fields in ultracold atom experiments by utilizing the atoms themselves as a magnetometer. The team, including scientists from Vilnius University and the National Institute of Standards and Technology, overcame limitations of conventional sensors, typically positioned several centimeters away from atomic systems, by employing a pair of measurements to determine magnetic field strength directly within the experiment. This procedure, demonstrated with rubidium 87, incorporates a Kalman filter that reduced long-term drift as high as approximately 70 nanotesla per hour, exchanging it for a slight increase in shot-to-shot variability.

A technique allows for magnetic field stabilization within ultracold atom experiments, bypassing limitations of conventional sensors. Traditional magnetic field sensors, such as Hall probes, are typically positioned at least several centimeters away from the atomic system due to the magnetic fields they generate and physical limitations of the vacuum apparatus. This direct approach utilizes the ultracold atoms themselves as a magnetometer, employing a pair of measurements to determine the Zeeman splitting, and thus the magnetic field, of rubidium 87.

The team developed expressions to quantify the balance between measurement noise, dynamic range, and potential atom loss during the process. This innovative method was demonstrated using partial-transfer absorption imaging, allowing for precise monitoring of the magnetic environment surrounding the atoms. This stabilization was achieved with a minimal increase in shot-to-shot variability, moving from 1.8(2) to 2.0(2) nanotesla.

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Magnetic Fields NIST ultracold atoms
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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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