Dataset Information

Multi-level quantum noise spectroscopy.

ABSTRACT: System noise identification is crucial to the engineering of robust quantum systems. Although existing quantum noise spectroscopy (QNS) protocols measure an aggregate amount of noise affecting a quantum system, they generally cannot distinguish between the underlying processes that contribute to it. Here, we propose and experimentally validate a spin-locking-based QNS protocol that exploits the multi-level energy structure of a superconducting qubit to achieve two notable advances. First, our protocol extends the spectral range of weakly anharmonic qubit spectrometers beyond the present limitations set by their lack of strong anharmonicity. Second, the additional information gained from probing the higher-excited levels enables us to identify and distinguish contributions from different underlying noise mechanisms.

SUBMITTER: Sung Y

PROVIDER: S-EPMC7878521 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multi-level quantum noise spectroscopy.

Sung Youngkyu Y Vepsäläinen Antti A Braumüller Jochen J Yan Fei F Wang Joel I-Jan JI Kjaergaard Morten M Winik Roni R Krantz Philip P Bengtsson Andreas A Melville Alexander J AJ Niedzielski Bethany M BM Schwartz Mollie E ME Kim David K DK Yoder Jonilyn L JL Orlando Terry P TP Gustavsson Simon S Oliver William D WD

Nature communications 20210211 1

System noise identification is crucial to the engineering of robust quantum systems. Although existing quantum noise spectroscopy (QNS) protocols measure an aggregate amount of noise affecting a quantum system, they generally cannot distinguish between the underlying processes that contribute to it. Here, we propose and experimentally validate a spin-locking-based QNS protocol that exploits the multi-level energy structure of a superconducting qubit to achieve two notable advances. First, our pr ...[more]

PMID: 33574240

Similar Datasets

Project description:Parallel imaging using phased array coils facilitates accelerated magnetic resonance imaging (MRI) and spectroscopy (MRS). Parallel data reconstruction requires the combination of data from individual coil elements, but limited combination algorithms currently exist for higher-order phased arrays and MRS data. Here, we present a systematic framework for identifying coil proximity-related signal inhomogeneities and noise levels in phased array coils that may affect sensitivity of parallel MRS. Single-voxel MRS was acquired in nine voxel positions in a brain spectroscopy phantom on a 3T whole-body MR scanner using commercially available 64-, 32-, and 20-channel phased array coils. Spectra produced by individual coil elements were combined using both a signal-to-noise ratio (SNR) threshold and based on the position of individual coil elements. SNR and metabolite Cramer-Rao lower bounds (CRLBs) from the final combined spectra were used as metrics to compare combination strategies and the effects of the phased array geometry and individual coil proximity. Comparisons were performed using one-way repeated measures ANOVA and post-hoc Tukey's range test (p<0.05). The 32-channel phased array coil produced the highest overall SNR compared to the 64-channel (p=0.0009) or 20-channel coils (p=0.003). Low SNR spectra from individual coil elements in the 64-channel coil can reduce the overall SNR when simply combining spectra from all elements. SNR varied significantly as a function of voxel position (F=58.3, p<0.0001) and SNR threshold for all phased arrays (p<0.05 for 64-, 32-, and 20-channel coils). Metabolite CRLBs were dependent on the combination strategy. We demonstrate the importance of the sampling voxel position and coil proximity on overall SNR in parallel MRS data acquisition, with significant SNR improvements after selectively filtering individual spectra based on pre-determined SNR thresholds which must be optimized for each phased array coil element and volume of interest.

Dataset Information

Multi-level quantum noise spectroscopy.

Publications

Multi-level quantum noise spectroscopy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets