论文标题
ROSNET：用于核心量子计算模拟的块张量代数库
RosneT: A Block Tensor Algebra Library for Out-of-Core Quantum Computing Simulation
论文作者
论文摘要
随着功能更强大的量子计算机的出现，对更大量子模拟的需求增强了。随着资源的数量呈指数增长，目标系统张量网络的大小作为一个最佳框架出现，我们在张量分解中代表量子状态。随着张量网络的范围增加，需要HPC工具进行操作的中间张量的大小也会增加。中型电路的模拟不能适合本地记忆，并且张紧量的分布式收缩解决方案很少。在这项工作中，我们介绍了Rosnet，这是一个用于分布式的，核心块张量代数的库。我们使用PYCOMPSS编程模型将张量操作转换为由Compss运行时处理的任务集合，以针对现有和即将到来的Exascale超级计算机中的执行。我们报告结果验证我们的方法，显示了对最多53吨量子电路的模拟良好的可扩展性。
With the advent of more powerful Quantum Computers, the need for larger Quantum Simulations has boosted. As the amount of resources grows exponentially with size of the target system Tensor Networks emerge as an optimal framework with which we represent Quantum States in tensor factorizations. As the extent of a tensor network increases, so does the size of intermediate tensors requiring HPC tools for their manipulation. Simulations of medium-sized circuits cannot fit on local memory, and solutions for distributed contraction of tensors are scarce. In this work we present RosneT, a library for distributed, out-of-core block tensor algebra. We use the PyCOMPSs programming model to transform tensor operations into a collection of tasks handled by the COMPSs runtime, targeting executions in existing and upcoming Exascale supercomputers. We report results validating our approach showing good scalability in simulations of Quantum circuits of up to 53 qubits.
