Simulation of Jiuzhang 2.0 quantum advantage experiments using 2 GB of RAM

Paper from Javier Martínez Cifuentes, Oliver Thomson Brown, Nicolás Quesada, Raul Garcia-Patron Sanchez and myself on a new, trivially parallelisable, approximate bitstring sampling algorithm. In this particular case, we have used our new method to simulate the 144-mode Jiuzhang 2.0 Gaussian boson sampling experiments: specifically, in all standard statistical tests of our 144-bit samples (against a ground truth which incorporates photon loss as the only imperfection), we performed better than samples obtained from the physical hardware. Previous work by other groups had achieved a goal very similar to the above, but required 144 A100 GPUs to run - out of the range of most users. The trivial parallelism of our algorithm, along with the requirement of only 2GB of RAM, allows for the use of anything from a single core of a CPU to multiple GPUs on a compute cluster node. Additionally, if we were to scale to the 144 GPUs mentioned above, our current implementation (which may be subject to further improvements) would generate samples four times faster than the previous approach. The idea of the algorithm is to generate the 144-bit sample one bit at a time by efficiently approximating the probability of the next bit being a 0 or 1 conditioned on the previously set bits. This probability, which is controlled by the statistical properties of the target distribution, is then used to bias the probability that next bit is randomly set to 0 or 1. After all 144 bits are set in this way, the sample has been generated, and the iteration starts again. Our scheme is already potentially applicable to other scenarios in which cheaply generating bitstrings with known statistical properties is practically useful, and in the near future, we wish to generalise the algorithm to a more general class of boson samplers. We plan to release the source code once it’s in a less messy state! submitted by /u/turbodiesel4598 [link] [comments]