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arXiv:2406.06361 · Society for Industrial and Applied Mathematics eBooks

Challenges with Implementing Differentiable Quantum Dynamics

Sri Hari Krishna Narayanan, Michael A. Perlin, Robert J. Lewis-Swan, Jeffrey M. Larson, Matt Menickelly, Jan Hückelheim, Paul Hovland

Published Jan 1, 2026• 0 citations
arXivHTMLPDF

Abstract

Differentiable quantum dynamics require automatic differentiation of a complex-valued initial value problem, which numerically integrates a system of ordinary differential equations from a specified initial condition, as well as the eigendecomposition of a matrix. This work is a survey of existing differentiable programming frameworks for these tasks, finding that no framework natively supports our application requirements fully. We therefore demonstrate a need for broader support of complex-valued, differentiable numerical integration in scientific computing libraries. We further demonstrate that the derivatives of our quantum dynamics application can be computed through a combination of differentiable programming frameworks and handcoding.

DOI
10.1137/1.9781611979039.19

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Differentiable quantum dynamics require automatic differentiation of a complex-valued initial value problem, which numerically integrates a system of ordinary differential equations from a specified initial condition, as well as the eigendecomposition of a matrix. This work is a survey of existing differentiable programming frameworks for these tasks, finding that no framework natively supports our application requirements fully. We therefore demonstrate a need for broader support of complex-valued, differentiable numerical integration in scientific computing libraries. We further demonstrate that the derivatives of our quantum dynamics application can be computed through a combination of differentiable programming frameworks and handcoding.
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The past few years have seen a significant transfer of tools from machine learning to solve quantum physics problems. Automatic differentiation is one standard algorithm used to efficiently compute gradients of loss functions for generic neural networks. In this work we show how to extend automatic differentiation to the case of complex loss function in a way that can be readily implemented in existing frameworks and which is compatible with the common case of real loss functions. We then combine this tool with matrix product states and apply it to compute the ground state and the steady state of a close and an open quantum system. Compared to the traditional density matrix renormalization group algorithm, complex automatic differentiation allows both straightforward GPU accelerations as well as generalizations to different types of tensor and neural networks.
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We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant examples across quantum computing (gradient computation, circuit compilation, and error mitigation), and implement them using the transform framework of PennyLane, a software library for differentiable quantum programming. In this framework, the transforms themselves are differentiable and can be parametrized and optimized, which opens up the possibility of improved quantum resource requirements across a spectrum of tasks.
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