Virtual Beamline++: full-chain optical modeling for modern laser design and operation
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Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials
Optical metamaterial device design has enjoyed a long track of success over the past 50 years leading to the manipulation of light over a wide range of wavelengths spanning the ultraviolet to the far infrared. The manipulation of light over such wavelengths has already led to many technological advancements such as the design of selective radiative absorbers for solar energy, daytime passive cooling using deep space, and optical invisibility cloaks for defense applications. Further disruptive advancements in energy, defense, computing, and biomedical fields could be enabled or enhanced by future optical metamaterial devices. These technologies could lead to increased energy efficiency and hence reduced national primary energy consumption, cheap long duration energy storage, and next generation solid-state heat engines. But historically the methods to invent and develop all of these devices have been time- consuming and based mostly on intuition and iteration. Finding an optimal design can take years.
36 MATERIALS SCIENCE↗
Excited State Phenomena in Correlated Nanostructures: Transition Metal Oxide Clusters and Nanocrystals
The primary goal of our research program was to apply advanced computational techniques to predict electronic properties of molecular species. These include transition metal monoxide and dioxide clusters and negatively charged copper oxide clusters which are important systems for their utilization in a variety of technological applications. The enhanced electron-electron interactions inherent in these systems make it challenging to model their excited state properties within a parameter-free approach. The excited state properties in question include the interaction of light with these molecules which results in ejection of electrons from the system, or the absorption of light by the molecular system resulting in a molecule in a higher-energy state. Our overall goal was to investigate the impact of various computational and theoretical approaches on such electronic and optical excitations in these systems. Comparing several variants of theoretical approaches with available experimental data, and high-level quantum chemistry computations helped us identify the level of theory and approximations that are needed to achieve a reasonable balance between accuracy and computational demand.
74 ATOMIC AND MOLECULAR PHYSICS↗
Analytical and numerical modeling of optical second harmonic generation in anisotropic crystals using #SHAARP package
Electric-dipole optical second harmonic generation (SHG) is a second-order nonlinear process that is widely used as a sensitive probe to detect broken inversion symmetry and local polar order. Analytical modeling of the SHG polarimetry of a nonlinear optical material is essential to extract its point group symmetry and the absolute nonlinear susceptibilities. Current literature on SHG analysis involves numerous approximations and a wide range of (in)accuracies. We have developed an open-source package called the Second Harmonic Analysis of Anisotropic Rotational Polarimetry (#SHAARP.si) which derives analytical and numerical solutions of reflection SHG polarimetry from a single interface (.si) for bulk homogeneous crystals with arbitrary symmetry group, arbitrary crystal orientation, complex and anisotropic linear dielectric tensor with frequency dispersion, a general SHG tensor and arbitrary light polarization. #SHAARP.si enables accurate modeling of polarimetry measurements in reflection geometry from highly absorbing crystals or wedge-shaped transparent crystals. The package is extendable to multiple interfaces.
36 MATERIALS SCIENCE↗
Adaptively controlled fast production of defect-free beryllium ion crystals using pulsed laser ablation
Trapped atomic ions find wide applications ranging from precision measurement to quantum information science and quantum computing. Beryllium ions are widely used due to the light mass and convenient atomic structure of beryllium; however, conventional ion loading from thermal ovens exerts undesirable gas loads for a prolonged duration. Here, we demonstrate a method to rapidly produce pure linear chains of beryllium ions with pulsed laser ablation, serving as a starting point for large-scale quantum information processing. Our method is fast compared to thermal ovens, reduces the gas load to only 10 -12 Torr (10 -10 Pa) level, yields a short recovery time of a few seconds, and also eliminates the need for a deep ultraviolet laser for photoionization. We also study the loading dynamics, which show non-Poissonian statistics in the presence of sympathetic cooling. In addition, we apply feedback control to obtain defect-free ion chains with desirable lengths.
47 OTHER INSTRUMENTATION↗
Quantum optical memory for entanglement distribution
Optical photons are powerful carriers of quantum information, which can be delivered in free space by satellites or in fibers on the ground over long distances. Entanglement of quantum states over long distances can empower quantum computing, quantum communications, and quantum sensing. Quantum optical memories are devices designed to store quantum information in the form of stationary excitations, such as atomic coherence, and are capable of coherently mapping these excitations to flying qubits. Quantum memories can effectively store and manipulate quantum states, making them indispensable elements in future long-distance quantum networks. Over the past two decades, quantum optical memories with high fidelities, high efficiencies, long storage times, and promising multiplexing capabilities have been developed, especially at the single-photon level. In this review, we introduce the working principles of commonly used quantum memory protocols and summarize the recent advances in quantum memory demonstrations. We also offer a vision for future quantum optical memory devices that may enable entanglement distribution over long distances.
Lei, Yisheng↗
Quantum optical classifier with superexponential speedup
Abstract Classification is a central task in deep learning algorithms. Usually, images are first captured and then processed by a sequence of operations, of which the artificial neuron represents one of the fundamental units. This paradigm requires significant resources that scale (at least) linearly in the image resolution, both in terms of photons and computational operations. Here, we present a quantum optical pattern recognition method for binary classification tasks. It classifies objects without reconstructing their images, using the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the behaviour of a classical neuron of unit depth. Once trained, it shows a constant $${{\mathcal{O}}}(1)$$ O ( 1 ) complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical artificial neuron.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Probabilistic simulation of quantum circuits using a deep-learning architecture
The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It impacts both our understanding of quantum materials and our ability to emulate quantum circuits. Here we present an exact formulation of quantum dynamics via factorized generalized measurements which maps quantum states to probability distributions with the advantage that local unitary dynamics and quantum channels map to local quasistochastic matrices. This representation provides a general framework for using state-of-the-art probabilistic models in machine learning for the simulation of quantum many-body dynamics. Using this framework, we have developed a practical algorithm to simulate quantum circuits using an attention network based on a powerful neural network ansatz responsible for the most recent breakthroughs in natural language processing. We demonstrate our approach by simulating circuits that build Greenberger-Horne-Zeilinger and linear graph states of up to 60 qubits, as well as a variational quantum eigensolver circuit for preparing the ground state of the transverse field Ising model on several system sizes. Our methodology constitutes a modern machine learning approach to the simulation of quantum physics with applicability both to quantum circuits as well as other quantum many-body systems.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A new class of variable-radii diffraction optics for high-resolution x-ray spectroscopy at the National Ignition Facility (invited)
A new class of crystal shapes has been developed for x-ray spectroscopy of point-like or small (a few mm) emission sources. These optics allow for dramatic improvement in both achievable energy resolution and total throughput of the spectrometer as compared with traditional designs. This class of crystal shapes, collectively referred to as the Variable-Radii Spiral (VR-Spiral), utilize crystal shapes in which both the major and minor radii are variable. A crystal using this novel VR-Spiral shape has now been fabricated for high-resolution Extended X-ray Absorption Fine Structure (EXAFS) experiments targeting the Pb-L 3 (13.0 keV) absorption edge at the National Ignition Facility. The performance of this crystal has been characterized in the laboratory using a microfocus x-ray source, showing that high-resolution high-throughput EXAFS spectra can be acquired using this geometry. Importantly, these successful tests show that the complex three-dimensional crystal shape is manufacturable with the required precision needed to realize the expected performance of better than 5 eV energy resolution while using a 30 mm high crystal. An improved generalized mathematical form for VR-Spiral shapes is also presented allowing improved optimization as compared to the first sinusoidal-spiral based design. Finally, this new formulation allows VR-Spiral spectrometers to be designed at any magnification with optimized energy resolution at all energies within the spectrometer bandwidth.
47 OTHER INSTRUMENTATION↗
Accuracy optimized neural networks do not effectively model optic flow tuning in brain area MSTd
Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints – non-negative weights and sparse coding of optic flow. While the CNNs and NNMF models both accurately estimate the observer's self-motion from purely translational or rotational optic flow, NNMF and the CNNs with nonnegative weights yield substantially less accurate estimates than the other CNNs when tested on more complex optic flow that combines observer translation and rotation. Despite its poor accuracy, NNMF gives rise to tuning properties that align more closely with those observed in primate MSTd than any of the accuracy-optimized CNNs. This work offers a step toward a deeper understanding of the computational properties and constraints that describe the optic flow tuning of primate area MSTd.
60 APPLIED LIFE SCIENCES↗
Colossal Cryogenic Electro‐Optic Response Through Metastability in Strained BaTiO 3 Thin Films
The search for thin film electro-optic materials that can retain superior performance under cryogenic conditions has become critical for quantum computing. Barium titanate thin films show large linear electro-optic coefficients in the tetragonal phase at room temperature, which is severely degraded down to ≈200 pm V −1 in the rhombohedral phase at cryogenic temperatures. There is immense interest in manipulating these phase transformations and retaining superior electro-optic properties down to liquid helium temperature. Utilizing the thermodynamic theory of optical properties, a large low-temperature electro-optic response is designed by engineering the energetic competition between different ferroelectric phases, leading to a low-symmetry monoclinic phase with a massive electro-optic response. The existence of this phase is demonstrated in a strain-tuned BaTiO 3 thin film that exhibits a linear electro-optic coefficient of 2516 ± 100 pm V −1 at 5 K, which is an order of magnitude higher than the best reported performance thus far. Importantly, the electro-optic coefficient increases by 100 × during cooling, unlike the conventional films, where it degrades. Further, at the lowest temperature, significant higher order electro-optic responses also emerge. These results represent a new framework for designing materials with property enhancements by stabilizing highly tunable metastable phases with strain.
barium titanate↗
Realizing Wide-Gamut Human-Centric Display Lighting with K 3 AlP 3 O 9 N:Eu 2+
Abstract Computers, televisions, and smartphones are revolutionized by the invention of InGaN blue light-emitting diode (LED) backlighting. Yet, continual exposure to the intense blue LED emission from these modern displays can cause insomnia and mood disorders. Developing “human-centric” backlighting that uses a violet-emitting LED chip and a trichromatic phosphor mixture to generate color images is one approach that addresses this problem. The challenge is finding a blue-emitting phosphor that possesses a sufficiently small Stokes’ shift to efficiently down-convert violet LED light and produce a narrow blue emission. This work reports a new oxynitride phosphor that meets this demand. K 3 AlP 3 O 9 N:Eu 2+ exhibits an unexpectedly narrow (45 nm, 2206 cm -1 ), thermally robust, and efficient blue photoluminescence upon violet excitation. Computational modeling and temperature-dependent optical property measurements reveal that the narrow emission arises from a rare combination of preferential excitation and site-selective quenching. The resulting chromaticity coordinates of K 3 AlP 3 O 9 N:Eu 2+ lie closer to the vertex of the Rec. 2020 than a blue LED chip and provides access to ≈10% more colors than a commercial tablet when combined with commercial red- and green-emitting phosphors. Alongside the wide gamut, tuning the emission from the violet LED and phosphor blend can reduce blue light emissions to produce next-generation, human-centric displays.
36 MATERIALS SCIENCE↗
Performance of Cloud 3D Solvers in Ice Cloud Shortwave Radiation Closure Over the Equatorial Western Pacific Ocean
Abstract For retrieving cloud optical properties from satellite images or computing these properties from climate model output, computationally efficient treatments of cloud horizontal inhomogeneity include the Monte Carlo Independent Column Approximation (McICA) and the Tripleclouds method. Computationally efficient treatment of cloud horizontal radiation exchanges includes the SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides (SPARTACUS). As a test to derive properties from satellite images, we collocate Moderate Resolution Imaging Spectroradiometer (MODIS) cloud retrievals with near‐nadir Cloud and the Earth's Radiant Energy System (CERES) footprints in July 2008 over an equatorial western Pacific Ocean region to compare the performance of the McICA, Tripleclouds, and SPARTACUS solvers to the conventional plane‐parallel homogeneous (PPH) treatment. PPH overestimates cloud albedo, and the three solvers effectively reduce overestimation with root mean square error of shortwave upwelling irradiance decreasing between 15.72 and 18.53 W m −2 , or about 22%–25%. Although cloud top variability does not get fed into the simulations, all three solvers also reduce the effect of cloud top variability on cloud albedo. Entrapment (energy reflected downward from clouds) and horizontal radiation transfer have opposite effects on the SPARTACUS cloud albedo simulation. The net effect depends on the cloud vertical extent, the unawareness of which limits the performance of the SPARTACUS solver.
54 ENVIRONMENTAL SCIENCES↗
Frequency comb ptychoscopy
Multiheterodyne techniques using frequency combs—radiation sources whose lines are perfectly evenly-spaced—have revolutionized science. By beating sources with the many lines of a comb, their spectra are recovered. Even so, these approaches are fundamentally limited to probing coherent sources, such as lasers. They are unable to measure most spectra that occur in nature. Here we present frequency comb ptychoscopy, a technique that allows for the spectrum of any complex broadband source to be retrieved using a comb. In this approach, the spectrum is reconstructed by unfolding the simultaneous beating of a source with each comb line. We demonstrate this both theoretically and experimentally, at microwave frequencies. This approach can reconstruct the spectrum of nearly any complex source to high resolution, and the speed, resolution, and generality of this technique will allow chip-scale frequency combs to have an impact in a wide swath of new applications, such as remote sensing and passive spectral imaging.
42 ENGINEERING↗
Discovery of two-dimensional binary nanoparticle superlattices using global Monte Carlo optimization
Binary nanoparticle (NP) superlattices exhibit distinct collective plasmonic, magnetic, optical, and electronic properties. Here, we computationally demonstrate how fluid-fluid interfaces could be used to self-assemble binary systems of NPs into 2D superlattices when the NP species exhibit different miscibility with the fluids forming the interface. We develop a basin-hopping Monte Carlo (BHMC) algorithm tailored for interface-trapped structures to rapidly determine the ground-state configuration of NPs, allowing us to explore the repertoire of binary NP architectures formed at the interface. By varying the NP size ratio, interparticle interaction strength, and difference in NP miscibility with the two fluids, we demonstrate the assembly of an array of exquisite 2D periodic architectures, including AB-, AB 2 -, and AB 3 -type monolayer superlattices as well as AB-, AB 2 -, A 3 B 5 -, and A 4 B 6 -type bilayer superlattices. Our results suggest that the interfacial assembly approach could be a versatile platform for fabricating 2D colloidal superlattices with tunable structure and properties.
36 MATERIALS SCIENCE↗
Distributed deep learning training using silicon photonic switched architectures
The scaling trends of deep learning models and distributed training workloads are challenging network capacities in today’s datacenters and high-performance computing (HPC) systems. We propose a system architecture that leverages silicon photonic (SiP) switch-enabled server regrouping using bandwidth steering to tackle the challenges and accelerate distributed deep learning training. In addition, our proposed system architecture utilizes a highly integrated operating system-based SiP switch control scheme to reduce implementation complexity. To demonstrate the feasibility of our proposal, we built an experimental testbed with a SiP switch-enabled reconfigurable fat tree topology and evaluated the network performance of distributed ring all-reduce and parameter server workloads. The experimental results show up to 3.6× improvements over the static non-reconfigurable fat tree. Our large-scale simulation results show that server regrouping can deliver up to 2.3× flow throughput improvement for a 2× tapered fat tree and a further 11% improvement when higher-layer bandwidth steering is employed. The collective results show the potential of integrating SiP switches into datacenters and HPC systems to accelerate distributed deep learning training.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Tunable quantum logic gate on photonic qubits with a ladder emitter
We describe how a ladder emitter can implement a tunable quantum logic gate on photonic qubits encoded in the frequency basis. The ground-to-first excited state of the ladder emitter interacts with the control photon, and the first-to-second excited state transition interacts with the target photon. By controlling the relative detuning between the target photon and the first-to-second excited state transition of the ladder emitter, we enable any controlled-phase operation from 0 to π. We derive analytical formulas for the performance of the gate through the S-matrix formalism as well as describe the mechanism intuitively. This gate is deterministic, does not utilize any active control, and needs only a single ladder emitter, enabling low-footprint and more efficient decomposition of quantum circuits, especially the quantum Fourier transform. We suggest multiple potential systems for physical realization of our proposal, such as lanthanide ions embedded in Purcell-enhanced cavities. We expect these results to motivate further interest in photonic quantum information processing with designer emitters.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Monte Carlo N-Particle forward modeling for density reconstruction of double shell capsule radiographs
In the Double Shell Inertial Confinement Fusion concept, characterizing the shape asymmetry of imploding metal shells is vital for understanding energy-efficient compression and radiative losses of the thermonuclear fuel. The Monte Carlo N-Particle MCNP® code forward models radiography of Double Shell capsule implosions using the Advanced Radiographic Capability at the National Ignition Facility. A procedure is developed for using MCNP to reconstruct density profiles from the radiograph image intensity. For a given Double Shell imploding target geometry, MCNP radiographs predict image contrast, which can help guide experimental design. Finally, in future work, the calculated MCNP synthetic radiographs will be compared with experimental radiographs to determine the radial and azimuthal density profiles of the Double Shell capsules.
47 OTHER INSTRUMENTATION↗