Classical simulation of quantum computation plays a critical role in numerical studies of quantum algorithms and the validation of quantum devices. Here, we introduce SW_Qsim, a tensor-network-based quantum simulator, which is designed with a two-level parallel structure for efficient implementation on the many-core New Sunway Supercomputer. We propose a minimize-memory contraction path algorithm for rectangular quantum grids to reduce the memory overhead, and provide the memory-limited simulation capacity of SW26010pro. Moreover, tensor operations are carefully optimized on the SW processor to achieve high performance. We design a fault tolerance mechanism to improve the extreme-scale parallel stability. We benchmark SW_Qsim's simulation of RQCs up to 400-qubits, achieving near-linear strong and weak scaling with up to 28.75 million cores, far beyond the previous state of the art. Our work sheds light on the development of efficient quantum algorithms for use in the physical, chemical, and engineering science fields.

We develop a high-performance tensor-based simulator for random quantum circuits(RQCs) on the new Sunway supercomputer. Our major innovations include: (1) a near-optimal slicing scheme, and a path-optimization strategy that considers both complexity and compute density; (2) a three-level parallelization scheme that scales to about 42 million cores; (3) a fused permutation and multiplication design that improves the compute efficiency for a wide range of tensor contraction scenarios; and (4) a mixed-precision scheme to further improve the performance. Our simulator effectively expands the scope of simulatable RQCs to include the 10X10(qubits)X(1+40+1)(depth) circuit, with a sustained performance of 1.2 Eflops (single-precision), or 4.4 Eflops (mixed-precision)as a new milestone for classical simulation of quantum circuits; and reduces the simulation sampling time of Google Sycamore to 304 seconds, from the previously claimed 10,000 years.