Waveguide-based Squeezed Light Sources
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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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Results for “Quantum network”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Optimized Source of Non-Gaussian States for Use in Hybrid Discrete/Continuous Variable Quantum Information Processing Schemes
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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Volumetric Fiber-to-Chip Mode Converters
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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Commercial Production of Low-Loss Quantum Frequency Conversion and Electro-Optical Modulation Devices Enabled by Wafer-Level Processing of Reverse-Proton-Exchange Lithium Niobate Waveguides
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42 ENGINEERING↗
Quantum Loop Topography for Machine Learning
Despite rapidly growing interest in harnessing machine learning in the study of quantum many-body systems, training neural networks to identify quantum phases is a nontrivial challenge. The key challenge is in efficiently extracting essential information from the many-body Hamiltonian or wave function and turning the information into an image that can be fed into a neural network. When targeting topological phases, this task becomes particularly challenging as topological phases are defined in terms of nonlocal properties. Here, we introduce quantum loop topography (QLT): a procedure of constructing a multidimensional image from the “sample” Hamiltonian or wave function by evaluating two-point operators that form loops at independent Monte Carlo steps. The loop configuration is guided by the characteristic response for defining the phase, which is Hall conductivity for the cases at hand. Feeding QLT to a fully connected neural network with a single hidden layer, we demonstrate that the architecture can be effectively trained to distinguish the Chern insulator and the fractional Chern insulator from trivial insulators with high fidelity. In addition to establishing the first case of obtaining a phase diagram with a topological quantum phase transition with machine learning, the perspective of bridging traditional condensed matter theory with machine learning will be broadly valuable.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Efficient quantum state tomography with mode-assisted training
Neural networks (NNs) representing quantum states are typically trained using Markov chain Monte Carlo based methods. However, unless specifically designed, such samplers only consist of local moves, making the slow-mixing problem prominent even for extremely simple quantum states. Here, we propose to use mode-assisted training that provides global information via the modes of the NN distribution. Further, applied to quantum state tomography using restricted Boltzmann machines, this method improves the quality of reconstructed quantum states by orders of magnitude. The method is applicable to other types of NNs and may efficiently tackle problems previously unmanageable.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Quantum graph learning and algorithms applied in quantum computer sciences and image classification
Graph and network theory play a fundamental role in quantum computer sciences, including quantum information and computation. Random graphs and complex network theory are pivotal in predicting novel quantum phenomena, where entangled links are represented by edges. Quantum algorithms have been developed to enhance solutions for various network problems, giving rise to quantum graph computing and quantum graph learning (QGL). Here, in this review, we explore graph theory and graph learning methods as powerful tools for quantum computers to generate efficient solutions to problems beyond the reach of classical systems. We delve into the development of quantum complex network theory and its applications in quantum computation, materials discovery, and research. We also discuss quantum machine learning (QML) methodologies for effective image classification using qubits, quantum gates, and quantum circuits. Additionally, the paper addresses the challenges of QGL and algorithms, emphasizing the steps needed to develop flexible QGL solvers. This review presents a comprehensive overview of the fields of QGL and QML, highlights recent advancements, and identifies opportunities for future research.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Demonstration of a Quantum Key Distribution Trusted Node on an Electric Utility Fiber Network
We present a trusted node quantum key distribution system deployed at an electric utility. We show the QKD distance limitation can be extended using trusted nodes with realistic utility fiber infrastructure, demonstrating the possibility of quantum technologies in providing information security to electric grid devices.
Evans, Phil↗
Quantum neuromorphic computing
Quantum neuromorphic computing physically implements neural networks in brain-inspired quantum hardware to speed up their computation. In this perspective article, we show that this emerging paradigm could make the best use of the existing and near future intermediate size quantum computers. Some approaches are based on parametrized quantum circuits and use neural network-inspired algorithms to train them. Other approaches, closer to classical neuromorphic computing, take advantage of the physical properties of quantum oscillator assemblies to mimic neurons and synapses to compute. In this work, we discuss the different implementations of quantum neuromorphic networks with digital and analog circuits, highlight their respective advantages, and review exciting recent experimental results.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage.
Broughton, Michael↗
Quantum Perturbation Theory Using Tensor Cores and a Deep Neural Network
In this work, time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer is dominated by tensor contractions, i.e., dense matrix–matrix multiplications, in mixed-precision arithmetics, which achieves close to peak performance. Quantum response calculations are demonstrated and analyzed using self-consistent charge density-functional tight-binding theory as well as coupled-perturbed Hartree–Fock theory. For linear response calculations, a novel parameter-free convergence criterion is presented that is well-suited for numerically noisy low-precision floating point operations and we demonstrate a peak performance of almost 200 Tflops using the Tensor cores of two Nvidia A100 GPUs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quantum confinement in self-assembled two-dimensional nanoporous honeycomb networks at close-packed metal surfaces
Quantum confinement of a two-dimensional electron gas by supramolecular nanoporous networks is investigated using the boundary elements method based on Green’s functions for finite geometries and electron plane wave expansion for periodic systems. The “particle in a box” picture was analyzed for cases with selected symmetries that model previously reported architectures constructed from organic and metal-organic scattering centers confining surface state electrons of Ag(111) and Cu(111). First, by analyzing a series of cases with systematically defined parameters (scattering geometry, potentials, and effective broadening), we demonstrate how the scattering processes affect the properties of the confined electrons. For the features of the local density of states reported by scanning tunneling spectroscopy (STS), we disentangle the contributions of lifetime broadening and splitting of quantum well states due to coupling of neighboring quantum dots. For each system, we analyze the local electron density distribution and relate it to the corresponding band structure as calculated within the plane-wave expansion framework. Then, we address two experimental investigations, where in one case only STS data and in the other case mainly angle-resolved photoemission spectroscopy (ARPES) data were reported. In both cases, the experimental findings can be successfully simulated. Furthermore, the missing information can be complemented because our approach allows to correlate the information obtained by STS with that of ARPES. The combined analysis of several observations suggests that the scattering potentials created by the network originate primarily from the adsorbate-induced changes of the local surface dipole barrier.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Dynamic characteristics of undoped and p-doped Fabry-Perot InAs/InP quantum dash based ridge waveguide lasers for access/metro networks
In this paper, we report the characteristics of InAs/InP quantum dashes (QDash) based lasers emitting around 1.55 μm. An unprecedented high modal gain of ∼100 cm{sup −1} is obtained for an optimized active structure by stacking 12 QDash layers. Directly modulated lasers allowed achieving a modulation bandwidth of ∼10 GHz and a Henry factor around 5. Thanks to p-type doping, the Henry factor value is reduced down to 2.7 while the modulation bandwidth still amounts to ∼10 GHz. This shows that doping of the active region is important to improve the dynamic characteristics of QDash lasers.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
A quantum eigenvalue solver based on tensor networks
Electronic ground states are of central importance in chemical simulations, but have remained beyond the reach of efficient classical algorithms except in cases of weak electron correlation or one-dimensional spatial geometry. We introduce a hybrid quantum-classical eigenvalue solver that constructs a wavefunction ansatz from a linear combination of matrix product states in rotated orbital bases, enabling the characterization of strongly correlated ground states with arbitrary spatial geometry. The energy is converged via a gradient-free generalized sweep algorithm based on quantum subspace diagonalization, with a potentially exponential speedup in the off-diagonal matrix element contractions upon translation into compact quantum circuits of linear depth in the number of qubits. Chemical accuracy is attained in numerical experiments for both a stretched water molecule and an octahedral arrangement of hydrogen atoms, achieving substantially better correlation energies compared to a unitary coupled-cluster benchmark, with orders of magnitude reductions in quantum resource estimates and a surprisingly high tolerance to shot noise. This proof-of-concept study suggests a promising new avenue for scaling up simulations of strongly correlated chemical systems on near-term quantum hardware.
chemistry↗
Spectral control of nonclassical light pulses using an integrated thin-film lithium niobate modulator
Abstract Manipulating the frequency and bandwidth of nonclassical light is essential for implementing frequency-encoded/multiplexed quantum computation, communication, and networking protocols, and for bridging spectral mismatch among various quantum systems. However, quantum spectral control requires a strong nonlinearity mediated by light, microwave, or acoustics, which is challenging to realize with high efficiency, low noise, and on an integrated chip. Here, we demonstrate both frequency shifting and bandwidth compression of heralded single-photon pulses using an integrated thin-film lithium niobate (TFLN) phase modulator. We achieve record-high electro-optic frequency shearing of telecom single photons over terahertz range (±641 GHz or ±5.2 nm), enabling high visibility quantum interference between frequency-nondegenerate photon pairs. We further operate the modulator as a time lens and demonstrate over eighteen-fold (6.55 nm to 0.35 nm) bandwidth compression of single photons. Our results showcase the viability and promise of on-chip quantum spectral control for scalable photonic quantum information processing.
Optics↗
Learning to classify quantum phases of matter with a few measurements
We study the identification of quantum phases of matter, at zero temperature, when only part of the phase diagram is known in advance. Following a supervised learning approach, we show how to use our previous knowledge to construct an observable capable of classifying the phase even in the unknown region. By using a combination of classical and quantum techniques, such as tensor networks, kernel methods, generalization bounds, quantum algorithms, and shadow estimators, we show that, in some cases, the certification of new ground states can be obtained with a polynomial number of measurements. An important application of our findings is the classification of the phases of matter obtained in quantum simulators, e.g. cold atom experiments, capable of efficiently preparing ground states of complex many-particle systems and applying simple measurements, e.g. single qubit measurements, but unable to perform a universal set of gates.
quantum machine learning↗
Quantum Link: Building the U.S. Quantum Superhighway
The Quantum Link is an ambitious project by Argonne, Fermilab and the University of Chicago to bring the property of entanglement into the real world. Entangling objects 30 miles apart, between Argonne and Fermi, the Quantum Link seeks to answer questions like, “What is the meaning of entanglement?” and “Can you use entangled states, separated 30 miles apart, as a type of superhighway to transfer information from one point to the other?” Using an optical fiber network built 12 years ago by the state of Illinois to link hi-speed data centers, the project gives researchers a real-world view of what a quantum network might look like.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
QuGAN: A Quantum State Fidelity based Generative Adversarial Network
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.
Stein, Samuel A.↗