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Trapped Ion Quantum Computing Quantum Machine Learning Quantum Simulation

Fast Black-Box Quantum State Preparation

arXiv
Authors: Johannes Bausch

Year

2020

Paper ID

20507

Status

Preprint

Abstract Read

~2 min

Abstract Words

243

Citations

N/A

Abstract

Quantum state preparation is an important ingredient for other higher-level quantum algorithms, such as Hamiltonian simulation, or for loading distributions into a quantum device to be used e.g. in the context of optimization tasks such as machine learning. Starting with a generic "black box" method devised by Grover in 2000, which employs amplitude amplification to load coefficients calculated by an oracle, there has been a long series of results and improvements with various additional conditions on the amplitudes to be loaded, culminating in Sanders et al.'s work which avoids almost all arithmetic during the preparation stage. In this work, we construct an optimized black box state loading scheme with which various important sets of coefficients can be loaded significantly faster than in O\(sqrt N\) rounds of amplitude amplification, up to only O(1) many. We achieve this with two variants of our algorithm. The first employs a modification of the oracle from Sanders et al., which requires fewer ancillas $log2 g$ vs $g+2$ in the bit precision $g$, and fewer non-Clifford operations per amplitude amplification round within the context of our algorithm. The second utilizes the same oracle, but at slightly increased cost in terms of ancillas $g+log2g$ and non-Clifford operations per amplification round. As the number of amplitude amplification rounds enters as multiplicative factor, our black box state loading scheme yields an up to exponential speedup as compared to prior methods. This speedup translates beyond the black box case.

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.
  • Quantum state preparation is an important ingredient for other higher-level quantum algorithms, such as Hamiltonian simulation, or for loading distributions into a quantum...

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