Neural-network quantum states for periodic systems in continuous space
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This final technical report provides an overview of the key milestones achieved by Advanced Research Systems (ARS), a DOE SBIR/STTR Grant Recipient. ARS’s main objective was to develop a prototype of the Closed Cycle Cryogenic Quantum Probe Station (Avalanche). Designed for near field spectroscopy (Tip Enhanced Photoluminescence, TEPL and Tip Enhanced Raman Spectroscopy TERS) and far field spectroscopy (micro-Raman, micro-PL) is in the final stage of completion. The critical components have been tested and the final System Test of the AFM is scheduled for August 2025.
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Here we report the discovery of CeLiBi 2 , the first example of a material in the tetragonal CeTX 2 ( T = transition metal; X = pnictogen) family wherein an alkali cation replaces the typical transition metal. Magnetic susceptibility and neutron powder diffraction measurements are consistent with a crystal-field Γ 6 ground-state Kramers doublet that orders antiferromagnetically below T N = 3.4 K with an incommensurate propagation wave vector k = ( 0, 0.0724(4), 0.5) that generates a nanometric modulation of the magnetic structure. The best model of the ordered state is an elliptical cycloid with Ce moments primarily residing in the ab plane. This is highly unusual, as all other Γ 6 CeTX 2 members order ferromagnetically. Further, we observe an atypical hard-axis metamagnetic transition at 2 T in magnetostriction, magnetization, and resistivity measurements. CeLiBi 2 is a rare example of a highly conductive material with dominant skew scattering leading to a large anomalous Hall effect. Quantum oscillations with five frequencies arise in magnetostriction and magnetic susceptibility data to T = 30 K and μ 0 H = 55 T, which indicate small Fermi pockets of light carriers with effective masses as low as 0.07 m e . Density functional theory calculations indicate that square-net Dirac-like Bi-p bands are responsible for these ultralight carriers. Together, our results show that CeLiBi 2 enables multiple atypical magnetic and electronic properties in a single clean material.
In the recent years, Generative Adversarial Networks (GANs) have been arguably one of the largest strides forward in Deep Learning. Many papers illustrate the use of GANs to accomplish extremely impressive goals, such as text-to-image or image augmentation. Specifically, GANs have seen this success in the computer vision domain. However, GANs are not without their own set of problems. GANs are computationally expensive, sometimes computationally prohibitive, and can suffer a multitude of convergence problems. As research on classical GANs continues to push the topic further, a branch of GANs, namely Quantum GANs, has seen research interest in the past years. In this work, we intend to push this research further with an illustration of a Quantum GAN architecture that provide stable convergence of the model and is extended onto real data sets. Furthermore, unlike many other Quantum GANs out there, our model's GAN architecture runs the discriminator and the generator primarily on Quantum hardware through the use of a Quantum-based similarity metric. When compared to the very few other Quantum GAN papers, our architecture leads to significantly better results in almost all aspects.
Trapped atomic ions are a versatile platform for studying interactions between spins and bosons by coupling the internal states of the ions to their motion. Measurement of complex motional states with multiple modes is challenging, because all motional state populations can only be measured indirectly through the spin state of ions. Here we present a general method to determine the Fock state distributions and to reconstruct the density matrix of an arbitrary multimode motional state. Further, we experimentally verify the method using different entangled states of multiple radial modes in a five-ion chain. This method can be extended to any system with Jaynes-Cummings-type interactions.
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We introduce an approach to treat localized correlated electronic states in the otherwise weakly correlated host medium. Here, the environment is dynamically downfolded on the correlated subspace. It is captured via renormalization of one and two quasiparticle interaction terms which are evaluated using many-body perturbation theory. We outline the strategy on how to take the dynamical effects into account by going beyond the static limit approximation. Further, we introduce an efficient stochastic implementation that enables treating the host environment with a large number of electrons at a minimal computational cost. For a small explicitly correlated subspace, the dynamical effects are critical. We demonstrate the methodology by reproducing optical excitations in the negatively charged NV center defect in diamond, that agree with experimental results.
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In this work, we present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism exploiting an artificial neural network representation of the groundstate wave function. A memory-efficient version of the stochastic reconfiguration algorithm is developed to train the network by minimizing the expectation value of the Hamiltonian. We benchmark this approach against widely used nuclear many-body methods by solving a model used to describe pairing in nuclei for different types of interaction and different values of the interaction strength. Despite its polynomial computational cost, our method outperforms coupled-cluster and provides energies that are in excellent agreement with the numerically-exact full configuration interaction values.
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