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Quantum Machine Learning

Circuit-based quantum random access memory for classical data with continuous amplitudes

arXiv
Authors: Tiago M. L. de Veras, Ismael C. S. de Araujo, Daniel K. Park, Adenilton J. da Silva

Year

2020

Paper ID

19264

Status

Preprint

Abstract Read

~2 min

Abstract Words

111

Citations

N/A

Abstract

Loading data in a quantum device is required in several quantum computing applications. Without an efficient loading procedure, the cost to initialize the algorithms can dominate the overall computational cost. A circuit-based quantum random access memory named FF-QRAM can load M n-bit patterns with computational cost O(CMn) to load continuous data where C depends on the data distribution. In this work, we propose a strategy to load continuous data without post-selection with computational cost O(Mn). The proposed method is based on the probabilistic quantum memory, a strategy to load binary data in quantum devices, and the FF-QRAM using standard quantum gates, and is suitable for noisy intermediate-scale quantum computers.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2020 reference point for readers tracking recent quantum research.
  • Loading data in a quantum device is required in several quantum computing applications.

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