(MURI) SUPERCONDUCTING RESERVOIR COMPUTERS FOR QUANTUM MEMORY AND INFORMATION PROCESSING

Abstract

QRAM, as it is typically envisaged, is impractical to realize at the large scales necessary for most machine-learning tasks in the near term. This is because existing QRAM proposals require either a prohibitively large number of qubits or prohibitively long qubit coherence times. Our approach to constructing QRAM is a radical departure from the existing approaches; we introduce and leverage two key ideas- that we can apply dissipation engineering and dissipative-stabilization techniques to realize QRAMs that are far more robust to decoherence, and that we can trade off space and time in the design of QRAMs to realize a QRAM that both uses a more practically realistic number of qubits and has a more practically realistic coherence time requirement. While the proof-of-concept and demonstrations of the proposed research is carried out on the superconducting circuit platform, our results will be transferable with minor hardware-informed modifications to optical and other electronic platforms as well. If successful, the research (1) will provide state-of-the-art computational hardware for tasks that are critical to the DoD using noisy intermediate scale superconducting quantum devices that will be available in the next five years, and (2) will lay the foundations, the protocols and fundamental limitations imposed by quantum mechanics of a general information processing framework that is optimally suited for today s optical and electronic systems for processing analog information directly acquired through physical sensors with minimal pre-processing.

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 07, 2023
Source ID
FA95502210203

Entities

People

  • Hakan E. Türeci

Organizations

  • Air Force Office of Scientific Research
  • Trustees of Princeton University
  • United States Air Force

Tags

Fields of Study

  • Physics

Readers

  • Computational Fluid Dynamics (CFD)
  • Neural Network Machine Learning.
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Microelectronics
  • Quantum Computing
  • Quantum Science - Quantum Dots
  • Space