Frequency Comb Cooling Project

Abstract

Laser cooling of atoms and molecules is an enabling technology for a number of applications, including atomic clocks, navigational sensors and quantum information processing. So far applications of this powerful technique have been limited to a handful of species as the conventional laser cooling requires closed (cycling) transitions. Here we will theoretically investigate the possibility of extending laser cooling techniques to a much wider class of multilevel atoms and molecules. The basic idea is to employ controllable coherent trains of laser pulses (frequency combs). Recently the power and spectral coverage of frequency combs have grown considerably with projected average powers above 10 kW. We will take advantage of this emerging technology. In the first project, we will exploit spectral selectivity of the combs and develop a time sequence protocol to move population across multiple levels with the goal of optimizing Doppler cooling for multi-level systems. In the second project, we will investigate stimulated optical force to slow heteronuclear molecules using ro-vibrational transitions inside the ground electronic state. Further, we propose to apply concepts from quantum-control and learning algorithms for finding optimal pulse sequences for cooling molecules even when the knowledge about their internal structures is imprecise.

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Document Details

Document Type
Technical Report
Publication Date
Mar 18, 2014
Accession Number
ADA606976

Entities

People

  • Andrei Derevianko

Organizations

  • University of Nevada, Reno

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Agreements
  • Atomic Clocks
  • Cooling
  • Department Of Defense
  • Electronic States
  • Emerging Technology
  • Frequency
  • Frequency Combs
  • Information Processing
  • Laser Cooling
  • Laser Pulses
  • Lasers
  • Mathematics
  • Molecules
  • Quantum Information
  • Sequences

Fields of Study

  • Engineering
  • Physics

Readers

  • Neural Network Machine Learning.
  • Optical Physics and Photonics.
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.

Technology Areas

  • Directed Energy
  • Microelectronics
  • Quantum Computing