1.2 Half-Quanta Noise Measured in K-Band Amplifier at 400 mK

Researchers have achieved a noise level of just 1.2 half-quanta in a K-band amplifier operating at 400 mK, a performance level approaching the theoretical limit of detectability and crucial for experiments probing the universe’s most elusive components. The amplifier, developed by V. Piccirillo and colleagues at the University of Manchester, demonstrates a wider bandwidth capability than many current low-noise amplifiers by achieving four distinct resonances between 23.3 and 26.3 GHz within the K-band using a single design. Measurements reveal 30 dB of signal gain, corresponding to 29 dB of insertion gain, at the extremely cold temperature, minimizing thermal noise. These results are particularly relevant to high-frequency axion dark matter experiments and motivate further exploration of higher frequencies in quantum technologies.

Resonant Superconducting Amplifier Design with Split-Ring Resonators

A novel amplifier design is approaching the theoretical limit of detectability, measuring just 1.2 half-quanta of added noise. Researchers have developed a resonant superconducting amplifier utilizing complementary split-ring resonators, fabricated on a niobium titanium-coated sapphire substrate and embedded within a waveguide. This architecture allows for operation across four narrow frequency bands within the K-band, spanning from 18 to 27 GHz, a significant advancement for applications demanding precise signal amplification. The design leverages the kinetic inductance of the superconducting film to achieve narrow-band performance, crucial for experiments seeking to isolate faint signals from substantial background noise. Measurements conducted at 400 mK, using a sorption cooler, confirmed the presence of four distinct resonances between 23.3 and 26.3 GHz, spaced at 1-GHz intervals, with a maximum transmission on resonance of −1 dB.

The ability to achieve four resonances within a single amplifier design represents a considerable simplification for experimental setups, reducing the need for multiple amplification stages. This compact design, coupled with the exceptionally low noise figure of 1.2 half-quanta, positions the amplifier as a promising candidate for detecting extremely weak signals, not only in the search for dark matter but also in astrophysical observations requiring the detection of faint spectroscopic lines.

Researchers have developed a K-band amplifier, operating between 18 and 27 GHz, that demonstrates performance approaching the theoretical limits of detectability. The amplifier achieves resonances from 23.3 to 26.3 GHz, spaced at 1-GHz intervals; this wider bandwidth capability simplifies experimental setups compared to many existing low-noise amplifiers. The most striking result is the exceptionally low noise performance, with the team measuring an added noise of just 1.2 half-quanta at 400 mK.

Noise Performance & Relevance to Axion Dark Matter Searches

Crucially, measurements revealed an added noise of just 1.2 half-quanta. This level of sensitivity is vital for capturing the faintest signals from the cosmos. The amplifier’s design achieves four distinct resonant frequencies, spanning 23.3 to 26.3 GHz with 1-GHz spacing, within the K-band, offering a wider bandwidth than many existing low-noise amplifiers. This broader operational range simplifies experimental setups and allows for more versatile applications. These results are particularly relevant to the ongoing quest to detect axions, a leading candidate for dark matter, and the amplifier’s performance also extends to detecting faint spectroscopic lines in astrophysics, promising new insights into distant celestial objects.

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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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