@article {2983, title = {Quantum computational advantage via high-dimensional Gaussian boson sampling}, journal = {Science Advances}, volume = {8}, year = {2022}, month = {1/5/2022}, pages = {eabi7894}, abstract = {

A programmable quantum computer based on fiber optics outperforms classical computers with a high level of confidence. Photonics is a promising platform for demonstrating a quantum computational advantage (QCA) by outperforming the most powerful classical supercomputers on a well-defined computational task. Despite this promise, existing proposals and demonstrations face challenges. Experimentally, current implementations of Gaussian boson sampling (GBS) lack programmability or have prohibitive loss rates. Theoretically, there is a comparative lack of rigorous evidence for the classical hardness of GBS. In this work, we make progress in improving both the theoretical evidence and experimental prospects. We provide evidence for the hardness of GBS, comparable to the strongest theoretical proposals for QCA. We also propose a QCA architecture we call high-dimensional GBS, which is programmable and can be implemented with low loss using few optical components. We show that particular algorithms for simulating GBS are outperformed by high-dimensional GBS experiments at modest system sizes. This work thus opens the path to demonstrating QCA with programmable photonic processors.

}, doi = {10.1126/sciadv.abi7894}, url = {https://www.science.org/doi/abs/10.1126/sciadv.abi7894}, author = {Abhinav Deshpande and Arthur Mehta and Trevor Vincent and Nicolas Quesada and Marcel Hinsche and Marios Ioannou and Lars Madsen and Jonathan Lavoie and Haoyu Qi and Jens Eisert and Dominik Hangleiter and Bill Fefferman and Ish Dhand} } @article {2773, title = {Quantum Computational Supremacy via High-Dimensional Gaussian Boson Sampling}, year = {2021}, month = {2/24/2021}, abstract = {

Photonics is a promising platform for demonstrating quantum computational supremacy (QCS) by convincingly outperforming the most powerful classical supercomputers on a well-defined computational task. Despite this promise, existing photonics proposals and demonstrations face significant hurdles. Experimentally, current implementations of Gaussian boson sampling lack programmability or have prohibitive loss rates. Theoretically, there is a comparative lack of rigorous evidence for the classical hardness of GBS. In this work, we make significant progress in improving both the theoretical evidence and experimental prospects. On the theory side, we provide strong evidence for the hardness of Gaussian boson sampling, placing it on par with the strongest theoretical proposals for QCS. On the experimental side, we propose a new QCS architecture, high-dimensional Gaussian boson sampling, which is programmable and can be implemented with low loss rates using few optical components. We show that particular classical algorithms for simulating GBS are vastly outperformed by high-dimensional Gaussian boson sampling experiments at modest system sizes. This work thus opens the path to demonstrating QCS with programmable photonic processors.

}, url = {https://arxiv.org/abs/2102.12474}, author = {Abhinav Deshpande and Arthur Mehta and Trevor Vincent and Nicolas Quesada and Marcel Hinsche and Marios Ioannou and Lars Madsen and Jonathan Lavoie and Haoyu Qi and Jens Eisert and Dominik Hangleiter and Bill Fefferman and Ish Dhand} }