Using NISQ Computing Devices to Simulate, Optimize, and Design Near-Term Quantum Communication Networks

Computing the dynamics of quantum many-bodied systems is a key challenge in developing applications for future quantum communication networks. Quantum computers show promise in their ability to efficiently simulate comlex quantum networks. We develop a hybrid quantum-classical computing framework that uses NISQ computing devices to  simulate, optimize, and design near-term quantum communication networks. We implement our framework in a publicly available python library that uses the PennyLane quantum machine learning framework to intergrate quantum computing APIs with machine learning libraries. We demonstrate our hybrid computing framework's ability to simulate and optimize small quantum networks and analyze its performance on both quantum hardware and classical simulator. For small networks with fewer than 20 qubits, we find that classical simulation is most efficent. For larger networks, we discuss how parallelization across many NISQ computing devices can yield efficient optimization and simulation. Finally, we explore how this software can be used to help design quantum internet applications.

Work funded by NSF award DMR-1747426