Quantum- and classical simulation of quantum networks

Quantum networks (QN) represent the next generation of the internet, making use of novel quantum features such as entanglement that do not have any classical counterpart. Such QN enable provable secure communication, enhanced quantum information processing by connecting quantum computers, and open the possibility for distributed sensor networks to measure non-local quantities with enhanced precision. Small-scale prototype QN and building blocks have already been demonstrated, but many challenges remain to scale them up to more nodes, longer distances and full functionality. But also from theoretical side, the investigation of complex and large networks has only begun. Network protocols need to be developed and simulated, and the performance of large networks should to be tested even before they are built. This project aims at designing new tools to investigate, simulate and further develop QN and protocols. We concentrate on so-called entanglement-based networks, where entangled resource states are generated between different, spatially separated nodes and then manipulated locally upon request. In contrast to classical networks, this can in principle be done even before a network request arrives. We will study these processes taking noise and imperfections into account, and design optimized quantum networks. We will also consider genuine quantum networks, where the classical control plane that orchestrates operations and protocols is replaced by a quantum control plane. This makes QN genuine quantum, and allows one to do things like sending information or state preparation in coherent superposition. This may offer new and unexpected possibilities e.g. to reduce the influence of noise and imperfections, or to enhance performance of network benchmarking protocols. In this context we will also design and study network protocols such as quantum ping to decide if quantum states are still operational. The simulation of networks, and their optimization and usage for multiplexed tasks is another subject of our studies. This applies to the (multiplexed) generation of desired entangled states shared between different nodes in a network, but also to state manipulation using measurements that we will study using different methods, including machine learning. We will also investigate the design of self- calibrating networks that optimize coherent superpositions of paths.