2026-08-14 –, Room 3
We introduce qruise-toolset, a differentiable quantum simulation toolbox with a Python interface and a Julia simulation backend. The toolbox enables researchers and companies to build faithful digital twin models of their hardware for fast calibration and prototyping via closed-loop quantum optimal control strategies at the pulse level. Moreover, the realistic behaviour of the control stack and the pulse delivery via the signal chain is an indispensable part of the toolbox, allowing the user to explore the limitations of the control stack components.
qruise-toolset is a fully differentiable simulation toolbox for quantum simulation and the quantum optimal control problem. It enables fast prototyping and optimisation of the hardware of interest by building a digital twin of the system. The automatic differentiation framework in qruise-toolset is provided via LLVM intermediate representation of the quantum simulation problem, which is then ingested by Enzyme.jl. This allows the user to benefit from the performance Julia JIT compilation offers and still stick to the convenience that the Python programming language offers. This approach uplifts the requirement of using automatic differentiation packages such as PyTorch, TensorFlow or JAX that are mostly suited for deep learning neural network architectures.
Yousof is a scientific software developer at Qruise GmbH.