Integrating Julia-ITensors into the Tensor Network Quantum Virtual Machine (TNQVM)

Classical simulation of quantum circuits is vital for verification and analysis of quantum algorithms. Among the various simulation modalities, tensor networks enable efficient representation of large circuits that exhibit limited entanglement. The Tensor Network Quantum Virtual Machine (TNQVM) provides a high-performance simulation backend for the XACC framework, leveraging the ITensor library for scalable tensor network computations. However, the existing C++ ITensor API lacks support for ongoing updates and advanced features. We present a redesigned TNQVM plugin that integrates the actively developed Julia ITensors library, enabling access to new algorithms and performance optimizations. As a demonstration, we apply the upgraded TNQVM to the MaxCut problem using the Quantum Approximate Optimization Algorithm (QAOA), highlighting new functionality, such as entanglement entropy measurements, and offering deeper insight into circuit complexity and entanglement structure.