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

TL;DR

This paper introduces JuliaITensorTNQVM, a bridge that modernizes TNQVM's tensor network simulation by integrating Julia-based ITensors, enabling access to advanced algorithms and diagnostics while maintaining existing interfaces.

Contribution

It develops an interoperability layer connecting TNQVM's C++ infrastructure with Julia-ITensors, enhancing tensor network simulation capabilities without altering the original programming model.

Findings

Consistent entanglement diagnostics in Page-curve verification.

Accurate QAOA MaxCut simulations on 3-regular graphs.

Supports modern tensor network algorithms and diagnostics.

Abstract

The Tensor Network Quantum Virtual Machine (TNQVM) is a high-performance classical circuit simulation backend for the eXtreme-scale ACCelerator (XACC) framework that leverages the Intelligent Tensor (ITensor) library for tensor network--based quantum circuit simulation. However, TNQVM's original C++ ITensor backend is tied to an older integrated release, limiting access to newer tensor network algorithms, diagnostics, and performance improvements available in the actively developed Julia-based ITensors ecosystem. We introduce JuliaITensorTNQVM, an interoperability layer that bridges TNQVM's C++ visitor infrastructure and the Julia-ITensors runtime through a C-compatible application binary interface. This design preserves the existing XACC/TNQVM programming model while enabling access to modern tensor network capabilities, including entanglement entropy diagnostics exposed directly to XACC. We evaluate the implementation through two studies: a Page-curve verification protocol using Haar-random states, and QAOA MaxCut simulations on 3-regular graphs. Within these tested regimes, results are consistent with expected entanglement behavior and established scaling trends, supporting JuliaITensorTNQVM as a practical modernization path for tensor network simulation in TNQVM.