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Generative models are a promising technology for the enhancement of quantum simulators. These machine learning methods are capable of reconstructing a quantum state from experimental measurements, and can aid in the calculation of physical observables. In this paper, we employ a recurrent neural network (RNN) to reconstruct the ground state of the spin-1/2 XY model, a prototypical Hamiltonian explored in trapped ion simulators. We explore its performance after enforcing a U(1) symmetry, which was recently shown by Hibat-Allah et al. [Phys. Rev. Research 2, 023358 (2020)] to preserve the autoregressive nature of the RNN. By studying the reconstruction of the XY model ground state from projective measurement data, we show that imposing U(1) symmetry on the RNN significantly increases the efficiency of learning, particularly in the early epoch regime. We argue that this performance increase may result from the tendency of the enforced symmetry to alleviate vanishing and exploding gradients, which helps stabilize the training process. Thus, symmetry-enforced RNNs may be particularly useful for applications of quantum simulators where a rapid feedback between optimization and circuit preparation is necessary, such as in hybrid classical-quantum algorithms., Comment: 10 pages, 8 figures

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

Density functional theory (DFT) calculations and infrared multiple photon dissociation (IRMPD) spectroscopy are employed to probe [TM·(B12H12)]− and [TM·(B12H12)2]2– clusters [TM = Ag(I), Cu(I), Co(II), Ni(II), Zn(II), Cd(II)]. A comparison is made between the charge-transfer properties of the clusters containing the hydrogenated dodecaborate dianions, B12H122–, and the fluorinated analogues, B12F122–, for clusters containing Cd(II), Co(II), Ni(II), and Zn(II). IRMPD of the [TM·(B12H12)]− and [TM·(B12H12)2]2– species yields B12H11– via hydride abstraction and B12H12– in all cases. To further explore the IR-induced charge-transfer properties of the B12X122– (X = H, F) cages, mixed-cage [TM(B12H12)(B12F12)]2– [TM = Co(II), Ni(II), Zn(II), Cd(II)] clusters were investigated. IRMPD of the mixed-cage species yielded appreciable amounts of B12F12– and B12H12– in most cases, indicating that charge-transfer to the central TM cation is a favorable process; formation of B12F12– is the dominant process for the Co(II...

As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter, and devices. Computational techniques can be used to reconstruct a state from data, however the growing number of qubits demands ongoing algorithmic advances in order to keep pace with experiments. In this paper, we present an open-source software package called QuCumber that uses machine learning to reconstruct a quantum state consistent with a set of projective measurements. QuCumber uses a restricted Boltzmann machine to efficiently represent the quantum wavefunction for a large number of qubits. New measurements can be generated from the machine to obtain physical observables not easily accessible from the original data., See https://github.com/PIQuIL/QuCumber

Density functional theory (DFT) calculations and infrared multiple photon dissociation (IRMPD) spectroscopy are employed to probe [TM·(B