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Bulletin of the American Physical Society

Qaintum: A Julia-based Simulation Framework for Quantum Circuits

Qunsheng Huang, Ismael Medina, Esther Cruz, Shin Ho Cho, Christian B. Mendl

Published Mar 17, 2021• 0 citations
OpenAlex

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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.
QXTools is a framework for simulating quantum circuits using tensor network methods.Weak simulation is the primary use case where given a quantum circuit and input state QXTools will efficiently calculate the probability amplitude of a given output configuration or set of configurations.Given this ability one can sample from the output distribution using random sampling approaches.QXTools is intended to be used by researchers interested in simulating circuits larger than those possible with full wave-function simulators or those interested in research and development of tensor network circuit simulation methods.See Brennan et al. (2021) for more complete background and scaling results and Brayford et al. ( 2021) for details about deploying in containerised environments.
We present Q u a n t u m T o o l b o x . j l , an open-source Julia package for simulating open quantum systems. Designed with a syntax familiar to users of Q u T i P (Quantum Toolbox in Python), it harnesses Julia's high-performance ecosystem to deliver fast and scalable simulations. The package includes a suite of time-evolution solvers supporting distributed computing and GPU acceleration, enabling efficient simulation of large-scale quantum systems. We also show how Q u a n t u m T o o l b o x . j l can integrate with automatic differentiation tools, making it well-suited for gradient-based optimization tasks such as quantum optimal control. Benchmark comparisons demonstrate substantial performance gains over existing frameworks. With its flexible design and computational efficiency, Q u a n t u m T o o l b o x . j l serves as a powerful tool for both theoretical studies and practical applications in quantum science.