Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip
Abstract
Quantum phase estimation is a fundamental subroutine in many quantum algorithms, including Shor's factorization algorithm and quantum simulation. However, so far results have cast doubt on its practicability for near-term, non-fault tolerant, quantum devices. Here we report experimental results demonstrating that this intuition need not be true. We implement a recently proposed adaptive Bayesian approach to quantum phase estimation and use it to simulate molecular energies on a Silicon quantum photonic device. The approach is verified to be well suited for pre-threshold quantum processors by investigating its superior robustness to noise and decoherence compared to the iterative phase estimation algorithm. This shows a promising route to unlock the power of quantum phase estimation much sooner than previously believed.
Cite
@article{arxiv.1703.05169,
title = {Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip},
author = {Stefano Paesani and Andreas A. Gentile and Raffaele Santagati and Jianwei Wang and Nathan Wiebe and David P. Tew and Jeremy L. O'Brien and Mark G. Thompson},
journal= {arXiv preprint arXiv:1703.05169},
year = {2017}
}