Speaker
Description
In this talk, I will introduce two tensor-network methods targeting the sampling problem of the Sycamore circuits. The first method computes amplitudes and probabilities for a large number of correlated bitstrings. The obtained results verify the Porter-Thomas distribution of the large and deep quantum circuits of Google and can be used for spoofing the linear cross entropy benchmark (XEB) of Google. The second method can generate one million uncorrelated bit-strings from the sparse state with fidelity roughly 0.0037, for the Sycamore circuit with 53 qubits and 20 cycles. The whole computation has cost about 15 hours on a computational cluster with 512 GPUs. If our algorithm could be implemented with high efficiency on a modern supercomputer with ExaFLOPS performance, we estimate that ideally, the simulation would cost a few dozens of seconds, which is faster than Google's quantum hardware.
