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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Positron Emission Tomography01:29

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Electron Microscope Tomography and Single-particle Reconstruction01:07

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
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Imaging Studies III: Computed Tomography01:27

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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¹H NMR of Labile Protons: Temporal Resolution01:10

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Protons bonded to heteroatoms such as nitrogen and oxygen exhibit a range of chemical shift values. This is due to the varying degree of hydrogen bonding between the proton and the heteroatom in other molecules. The extent of hydrogen bonding affects the electron density around the proton, thereby giving different chemical shift values for the protons in the proton NMR spectrum.
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Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy
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Learning Temporal Quantum Tomography.

Quoc Hoan Tran1, Kohei Nakajima1,2

  • 1Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan.

Physical Review Letters
|January 14, 2022
PubMed
Summary
This summary is machine-generated.

We present a new machine learning method for quantum device tomography, enabling efficient characterization of quantum states over time. This approach addresses challenges in quantum control and temporal processing for future quantum technologies.

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Area of Science:

  • Quantum Information Science
  • Machine Learning
  • Quantum Computing

Background:

  • Verifying quantum state preparation is crucial for quantum device development.
  • Standard quantum tomography is resource-intensive and not suitable for temporal processing.
  • Tomography for quantum devices with temporal dynamics remains an open challenge.

Purpose of the Study:

  • To develop a practical and approximate tomography method for quantum devices with temporal processing.
  • To enable efficient characterization of quantum states in time-varying systems.
  • To establish a framework for evaluating the temporal processing capabilities of quantum devices.

Main Methods:

  • Utilizing a recurrent machine learning framework for approximate quantum tomography.
  • Employing a quantum reservoir system with repeated quantum interactions.
  • Training a recurrent relation between quantum channels using measurement data and linear readout.

Main Results:

  • Demonstrated the efficacy of the developed algorithms on representative quantum learning tasks.
  • Successfully applied the recurrent machine learning approach to temporal quantum state characterization.
  • Proposed a quantum memory capacity metric for evaluating temporal processing.

Conclusions:

  • The recurrent machine learning framework offers a practical solution for quantum device tomography with temporal dynamics.
  • This method advances the ability to quantify and verify control in preparing quantum states over time.
  • The proposed quantum memory capacity provides a valuable tool for assessing near-term quantum devices.