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Boson sampling is expected to be one of an important milestones that will demonstrate quantum supremacy. The present work establishes the benchmarking of Gaussian boson sampling (GBS) with threshold detection based on the Sunway TaihuLight supercomputer. To achieve the best performance and provide a competitive scenario for future quantum computing studies, the selected simulation algorithm is fully optimized based on a set of innovative approaches, including a parallel scheme and instruction-level optimizing method. Furthermore, data precision and instruction scheduling are handled in a sophisticated manner by an adaptive precision optimization scheme and a DAG-based heuristic search algorithm, respectively. Based on these methods, a highly efficient and parallel quantum sampling algorithm is designed. The largest run enables us to obtain one Torontonian function of a 100 x 100 submatrix from 50-photon GBS within 20 hours in 128-bit precision and 2 days in 256-bit precision.
Radiation damage to the steel material of reactor pressure vessels is a major threat to the nuclear reactor safety. It is caused by the metal atom cascade collision, initialized when the atoms are struck by a high-energy neutron. The paper presents M
Gaussian boson sampling is a promising scheme for demonstrating a quantum computational advantage using photonic states that are accessible in a laboratory and, thus, offer scalable sources of quantum light. In this contribution, we study two-point p
Boson Sampling has emerged as a tool to explore the advantages of quantum over classical computers as it does not require a universal control over the quantum system, which favours current photonic experimental platforms.Here, we introduce Gaussian B
Gaussian Boson sampling (GBS) provides a highly efficient approach to make use of squeezed states from parametric down-conversion to solve a classically hard-to-solve sampling problem. The GBS protocol not only significantly enhances the photon gener
A new algorithm which is called Store-zechin, and utilizes stored data repetitively for calculating the permanent of an n * n matrix is proposed. The analysis manifests that the numbers of multiplications and additions taken by the new algorithm are