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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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