
TL;DR
This paper presents a faster black-box quantum state preparation method that significantly reduces the number of amplitude amplification rounds needed, enabling exponential speedups for loading quantum states with important coefficient sets.
Contribution
The authors develop an optimized black-box state loading scheme with two variants that outperform previous methods by reducing rounds and resource requirements.
Findings
Achieves up to exponential speedup over prior methods.
Reduces ancilla and non-Clifford operations per amplification round.
Enables faster loading of important quantum state coefficients.
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 rounds of amplitude amplification, up to only many. We achieve this with…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
