Reconstructing quantum molecular rotor ground states

Abstract

Nanomolecular assemblies of C60 can be synthesized to enclose dipolar molecules. The lowtemperature states of such endofullerenes are described by quantum mechanical rotors, which are candidates for quantum information devices with higher-dimensional local Hilbert spaces. The experimental exploration of endofullerene arrays comes at a time when machine learning techniques are rapidly being adopted to characterize, verify, and reconstruct quantum states from measurement data. In this paper, we develop a strategy for reconstructing the ground state of chains of dipolar rotors using restricted Boltzmann machines (RBMs) adapted to train on data from higherdimensional Hilbert spaces. We demonstrate accurate generation of energy expectation values from an RBM trained on data in the free-rotor eigenstate basis, and explore the learning resources required for various chain lengths and dipolar interaction strengths. Finally, we show evidence for fundamental limitations in the accuracy achievable by RBMs due to the difficulty in imposing symmetries in the sampling procedure. We discuss possible avenues to overcome this limitation in the future, including the further development of autoregressive models such as recurrent neural networks for the purposes of quantum state reconstruction.

Attention-based Quantum Tomography

“…requiring exponentially many parameters or memory size. However, the expressibility of restricted Boltzmann machines and scalability of training is typically restricted to pure, positive quantum states [5,[8][9][10][11], and small mixed states [12], which limits their applicability at the scale of modern noisy quantum computers. In contrast, Ref.…”