Speaker
Description
We introduce qruise-toolset, a differentiable quantum simulation toolbox with a Python interface and a Julia simulation backend. It is specifically tailored for simulating open quantum systems and solving quantum optimal control problems. The toolset allows the user to benefit from the performance that the Julia JIT compilation offers and still feel at home with the convenience that the Python programming language provides. Moreover, we lift the requirement of using automatic differentiation packages such as PyTorch, TensorFlow or JAX, which are mostly suited for deep neural network architectures. We bring the world of automatic differentiation in Julia to Python thanks to the Enzyme.jl package, through the LLVM intermediate representation of the quantum simulation problem. We have seen performance gains up to 50x for the function calls. The general simulation of large quantum systems with 15 qubits can be 2x faster than conventional quantum simulation packages such as QuTiP. More importantly, this unlocks powerful new workflows around data-driven learning of digital twins of complex quantum systems, thus closing the Sim2Real gap.
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