Reconstructing quantum molecular rotor ground states
| Autor: | Ejaaz Merali, Isaac J. S. De Vlugt, Roger G. Melko, Dmitri Iouchtchenko, Pierre-Nicholas Roy |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Chemical Physics (physics.chem-ph)
Quantum Physics Basis (linear algebra) Computer science Density matrix renormalization group Boltzmann machine Hilbert space FOS: Physical sciences 02 engineering and technology Disordered Systems and Neural Networks (cond-mat.dis-nn) Condensed Matter - Disordered Systems and Neural Networks 021001 nanoscience & nanotechnology 01 natural sciences symbols.namesake Quantum state Physics - Chemical Physics 0103 physical sciences symbols Statistical physics Quantum information 010306 general physics 0210 nano-technology Ground state Quantum Physics (quant-ph) Quantum |
| Popis: | Nanomolecular assemblies of C$_{60}$ can be synthesized to enclose dipolar molecules. The low-temperature 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 higher-dimensional 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. 11 pages, 7 figures |
| Databáze: | OpenAIRE |
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