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.
翻译:随着更强大的量子计算机的出现,对更强大的量子计算机的需求已经增加。随着目标系统Tensor网络规模的扩大,资源量的急剧增长成为我们代表量子因子化的量子体国家的最佳框架。随着高压网络的扩大,需要高压计算机操纵的中等发压器体积也随之增加。中等电路的模拟无法适应当地的记忆,而配制粒子收缩的解决方案也十分稀少。在此工作中,我们展示了RosneT,这是一个分布式、核心区块外高温代数的图书馆。我们使用PyCOMPS的编程模型来将龙头操作转换成由COMS运行的时间所处理的一组任务,目标是在现有和即将到来的Exascale超级计算机中执行处决。我们报告的结果证实了我们的方法在对高达53公尺的量子量子电路的模拟中显示出良好的可伸缩性。