Induced photon loss reveals hidden noise affecting superconducting qubits

Nitzan Kahn and colleagues at the Weizmann Institute of Science have established an upper bound of 0.70 x 10^3 rad^2/s on the intrinsic qubit frequency-noise power spectral density at 508 MHz. The team measured this noise not by probing the qubit directly, but by detecting photon loss induced in a coupled superconducting cavity, a method that bypasses bandwidth limitations of conventional sensing.

This approach opens access to a higher-frequency spectral window and may allow for noise characterization even while qubits are actively computing, which could improve coherence and gate performance in these sensitive systems. Identifying and characterizing noise processes is central to advancing qubit performance.

Cavity-Qubit Coupling Drives Noise-Induced Photon Loss

The approach relies on a phenomenon called dressed dephasing, where qubit frequency noise drives an exchange of energy between the qubit and the cavity, creating an additional channel for photon loss. The rate of this induced photon loss is directly proportional to the qubit frequency-noise spectral density, allowing researchers to infer the level of noise present. Extracting this signal, however, requires isolating it from the cavity’s natural decay, a challenge overcome through repeated mid-circuit qubit measurements combined with post-selection techniques.

Validation of the method involved injecting controlled frequency noise, demonstrating a predictable relationship between noise amplitude and the observed cavity lifetime response. This new method extends the range of frequencies accessible for noise characterization beyond the capabilities of standard qubit-based spectroscopy, opening possibilities for detecting and mitigating noise sources previously hidden from view.

The underlying principle hinges on the hybridization between the qubit and the cavity mode, converting longitudinal modulation, caused by frequency noise, into photon loss. “We demonstrated a cavity-based technique for sensing qubit frequency noise through photon loss induced by dressed dephasing,” the researchers state, emphasizing the core innovation of their work.

This technique, they note, uses repeated mid-circuit qubit readout with post-selection to effectively separate the noise-induced loss from the cavity’s intrinsic decay, a critical step in accurate noise assessment. Extracting the precise origins of decoherence in superconducting qubits remains a significant challenge, but a new technique isolates noise-induced photon loss from inherent decay within the qubit’s coupled cavity.

Validation with Injected Noise Confirms Method Accuracy

The sensitivity of this new noise detection method was directly tested by introducing calibrated microwave tones to simulate frequency fluctuations, confirming the expected relationship between injected noise and measured photon loss. Further validation involved repeating the noise characterization across a range of injected noise amplitudes, revealing a linear relationship between the extracted dressed-dephasing rate and the applied noise power as predicted by theoretical models. This linearity confirms the method’s ability to accurately quantify noise levels, and at lower noise powers, the extracted values transitioned into upper bounds limited by the precision of the statistical analysis.

Frequency Noise Limit at 508 MHz Determined

The technique revealed a quantifiable limit on intrinsic qubit frequency noise at 508 MHz, a key parameter for advancing quantum computation. Analysis of photon loss induced by qubit fluctuations established an upper bound of 0.70 x 103 rad2/s at this specific cavity-qubit detuning, a value determined from the lowest-power data points and limited by statistical precision. The method’s sensitivity stems from a detailed understanding of how qubit frequency noise translates into detectable photon loss within the coupled superconducting cavity.

Mid-Circuit Measurements Enable Noise Spectrum Access

By detecting alterations in cavity photons, a new technique places an upper bound of 0.70× 103 rad2/ s on the intrinsic qubit frequency-noise power spectral density at the cavity-qubit detuning of 508 MHz. The protocol’s sensitivity extends into a high-frequency spectral window previously inaccessible to standard qubit-based spectroscopy, offering a more comprehensive view of the noise landscape. The protocol may enable noise characterization during strong driving.

High-Q Cavity Design for Qubit Noise Sensing

A key innovation within the protocol is the implementation of repeated mid-circuit qubit measurements coupled with post-selection, a technique that effectively isolates noise-induced photon loss from the intrinsic decay of the cavity. The high-Q design of the cavity, combined with the precise timing of the qubit measurements, allows for the detection of subtle changes in photon loss induced by the qubit’s frequency noise.

👉 More information
🗞 Qubit Noise Sensing via Induced Photon Loss in a Superconducting Cavity
✍️ Nitzan Kahn, Dror Garti, Uri Goldblatt, Lalit M. Joshi, Fabien Lafont and Serge Rosenblum
🧠 DOI: http://link.aps.org/doi/10.1103/jjcg-z4xb

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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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