Distributional Property Testing in a Quantum World

A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. We also introduce a novel access model for quantum distributions, enabling the coherent preparation of quantum samples, and propose a general framework that can naturally handle both classical and quantum distributions in a unified manner. Our framework generalizes and improves previous quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon / von Neumann entropy of distributions. For classical distributions our algorithms significantly improve the precision dependence of some earlier results. We also show that in our framework procedures for classical distributions can be directly lifted to the more general case of quantum distributions, and thus obtain the first speed-ups for testing properties of density operators that can be accessed coherently rather than only via sampling

Distributional property testing in a quantum world

A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. We also introduce a novel access model for quantum distributions, enabling the coherent preparation of quantum samples, and propose a general framework that can naturally handle both classical and quantum distributions in a unified manner. Our framework generalizes and improves previous quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon/von Neumann entropy of distributions. For classical distributions our algorithms significantly improve the precision dependence of some earlier results. We also show that in our framework procedures for classical distributions can be directly lifted to the more general case of quantum distributions, and thus obtain the first speed-ups for testing properties of density operators that can be accessed coherently rather than only via sampling

Distributional property testing in a quantum world

A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. In particular, we give fast quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon / von Neumann entropy of distributions. The distributions can be either classical or quantum, however our quantum algorithms require coherent quantum access to a process preparing the samples. Our results build on the recent technique of quantum singular value transformation, combined with more standard tricks such as divide-and-conquer. The presented approach is a natural fit for distributional property testing both in the classical and the quantum case, demonstrating the first speed-ups for testing properties of density operators that can be accessed coherently rather than only via sampling; for classical distributions our algorithms significantly improve the precision dependence of some earlier results

HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting

Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on dictio-nary matching to map the temporal MRF signals to quantitative tissue parameters. Such approaches suffer from inherent discretization errors, as well as high computational complexity as the dictionary size grows. To alleviate these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting approach, referred to as HYDRA. Methods: HYDRA involves two stages: a model-based signature restoration phase and a learning-based parameter restoration phase. Signal restoration is implemented using low-rank based de-aliasing techniques while parameter restoration is performed using a deep nonlocal residual convolutional neural network. The designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady state precession (FISP) sequences. In test mode, it takes a temporal MRF signal as input and produces the corresponding tissue parameters. Results: We validated our approach on both synthetic data and anatomical data generated from a healthy subject. The results demonstrate that, in contrast to conventional dictionary-matching based MRF techniques, our approach significantly improves inference speed by eliminating the time-consuming dictionary matching operation, and alleviates discretization errors by outputting continuous-valued parameters. We further avoid the need to store a large dictionary, thus reducing memory requirements. Conclusions: Our approach demonstrates advantages in terms of inference speed, accuracy and storage requirements over competing MRF method

Characterization of the Fe metalloproteome of a ubiquitous marine heterotroph, Pseudoalteromonas (BB2-AT2): multiple bacterioferritin copies enable significant Fe storage

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Mazzotta, M. G., McIlvin, M. R., & Saito, M. A. Characterization of the Fe metalloproteome of a ubiquitous marine heterotroph, Pseudoalteromonas (BB2-AT2): multiple bacterioferritin copies enable significant Fe storage. Metallomics, (2020), doi:10.1039/d0mt00034e.Fe is a critical nutrient to the marine biological pump, which is the process that exports photosynthetically fixed carbon in the upper ocean to the deep ocean. Fe limitation controls photosynthetic activity in major regions of the oceans, and the subsequent degradation of exported photosynthetic material is facilitated particularly by marine heterotrophic bacteria. Despite their importance in the carbon cycle and the scarcity of Fe in seawater, the Fe requirements, storage and cytosolic utilization of these marine heterotrophs has been less studied. Here, we characterized the Fe metallome of Pseudoalteromonas (BB2-AT2). We found that with two copies of bacterioferritin (Bfr), Pseudoalteromonas possesses substantial capacity for luxury uptake of Fe. Fe : C in the whole cell metallome was estimated (assuming C : P stoichiometry ∼51 : 1) to be between ∼83 μmol : mol Fe : C, ∼11 fold higher than prior marine bacteria surveys. Under these replete conditions, other major cytosolic Fe-associated proteins were observed including superoxide dismutase (SodA; with other metal SOD isoforms absent under Fe replete conditions) and catalase (KatG) involved in reactive oxygen stress mitigation and aconitase (AcnB), succinate dehydrogenase (FrdB) and cytochromes (QcrA and Cyt1) involved in respiration. With the aid of singular value decomposition (SVD), we were able to computationally attribute peaks within the metallome to specific metalloprotein contributors. A putative Fe complex TonB transporter associated with the closely related Alteromonas bacterium was found to be abundant within the Pacific Ocean mesopelagic environment. Despite the extreme scarcity of Fe in seawater, the marine heterotroph Pseudoalteromonas has expansive Fe storage capacity and utilization strategies, implying that within detritus and sinking particles environments, there is significant opportunity for Fe acquisition. Together these results imply an evolved dedication of marine Pseudoalteromonas to maintaining an Fe metalloproteome, likely due to its dependence on Fe-based respiratory metabolism.M. G. M. was supported by the Camille and Henry Dreyfus Foundation Environmental Chemistry Postdoctoral Fellowship. We thank Kay Bidle (Rutgers University) for providing a culture of Pseudoalteromonas (BB2-AT2). We also thank Dawn Moran (WHOI) and Noelle Held (WHOI-MIT) for culturing assistance. We appreciate the Captain and Crew of the R/V Kilo Moana, and the many involved in the METZYME expedition sampling efforts. Discussions with Kevin Waldron (Newcastle University), Alison Butler (University of California, Santa Barbara), Lauren Manck (Scripps Institution of Oceanography), Randie Bundy (University of Washington) and Jake Gebbie (WHOI) were much appreciated. Funding for this research was provided by the Gordon and Betty Moore Foundation (3782), and NSF-OCE 1658030, 1736599, 1657766, 1924554, 1850719, 1924554