Quantum computational advantage via high-dimensional Gaussian boson sampling
Year: 2022
Type: article
Abstract: 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.
Source: Science Advances
Institutions: California Institute of Technology, Joint Center for Quantum Information and Computer Science, Joint Quantum Institute, University of Maryland, College Park, University of Toronto, Xanadu Quantum Technologies (Canada), Polytechnique Montréal, Freie Universität Berlin, Helmholtz-Zentrum Berlin für Materialien und Energie, University of Chicago, Universität Ulm, Center for Integrated Quantum Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,500
OpenAlex ID: W4205363984