Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “quantum information with hybrid systems”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

6 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Quantum-Classical Hybrid for Information Processing

Based upon quantum-inspired entanglement in quantum-classical hybrids, a simple algorithm for instantaneous transmissions of non-intentional messages (chosen at random) to remote distances is proposed. The idea is to implement instantaneous transmission of conditional information on remote distances via a quantum-classical hybrid that preserves superposition of random solutions, while allowing one to measure its state variables using classical methods. Such a hybrid system reinforces the advantages, and minimizes the limitations, of both quantum and classical characteristics. Consider n observers, and assume that each of them gets a copy of the system and runs it separately. Although they run identical systems, the outcomes of even synchronized runs may be different because the solutions of these systems are random. However, the global constrain must be satisfied. Therefore, if the observer #1 (the sender) made a measurement of the acceleration v(sub 1) at t =T, then the receiver, by measuring the corresponding acceleration v(sub 1) at t =T, may get a wrong value because the accelerations are random, and only their ratios are deterministic. Obviously, the transmission of this knowledge is instantaneous as soon as the measurements have been performed. In addition to that, the distance between the observers is irrelevant because the x-coordinate does not enter the governing equations. However, the Shannon information transmitted is zero. None of the senders can control the outcomes of their measurements because they are random. The senders cannot transmit intentional messages. Nevertheless, based on the transmitted knowledge, they can coordinate their actions based on conditional information. If the observer #1 knows his own measurements, the measurements of the others can be fully determined. It is important to emphasize that the origin of entanglement of all the observers is the joint probability density that couples their actions. There is no centralized source, or a sender of the signal, because each receiver can become a sender as well. An observer receives a signal by performing certain measurements synchronized with the measurements of the others. This means that the signal is uniformly and simultaneously distributed over the observers in a decentralized way. The signals transmit no intentional information that would favor one agent over another. All the sequence of signals received by different observers are not only statistically equivalent, but are also point-by-point identical. It is important to assume that each agent knows that the other agent simultaneously receives the identical signals. The sequences of the signals are true random, so that no agent could predict the next step with the probability different from those described by the density. Under these quite general assumptions, the entangled observers-agents can perform non-trivial tasks that include transmission of conditional information from one agent to another, simple paradigm of cooperation, etc. The problem of behavior of intelligent agents correlated by identical random messages in a decentralized way has its own significance: it simulates evolutionary behavior of biological and social systems correlated only via simultaneous sensoring sequences of unexpected events.

Zak, Michail↗
NASA NTRS2024

Dual-Resonator Kinetic-Inductance Detector for Distinction between Signal and 1/ƒ Frequency Noise

Astronomical kinetic inductance detectors (KIDs), similar to quantum information devices, experience performance-limiting noise from materials. In particular, 1/ƒ (frequency) noise arises from two-level system defects (TLSs) in the circuit dielectrics and material interfaces and can be a dominant noise mechanism. Here, we present a dual-resonator KID (DuRKID), which is designed for improved noise-equivalent power relative to standard 1/ƒ -noise-limited KIDs. In this study we present the DuRKID schematic, a fabricated example, our first measurement results, a theoretical model including 1/ƒ noise, and a system-noise model containing additional noise sources. The circuit consists of two superconducting resonators sharing an electrical capacitance bridge of four capacitors, each of which hosts TLSs. The device is intended to operate using hybridization of the modes, which causes TLSs to either couple to one mode or the other, depending upon which capacitor they reside in. In contrast, the signal will affect a resonator inductance and, due to mode hybridization, this causes correlated frequency changes in both modes. Therefore, one can better distinguish the photon signal from the TLS frequency noise. To achieve hybridization, a TiN inductor is current biased to allow tuning of one bare-resonator mode into degeneracy with the other. Measurements show that the resonator modes hybridize as expected. The inter-resonator coupling and unintentional coupling of the two resonators to transmission lines are also characterized in measurements. A quantum information-science model allows device-parameter extraction from experimental data and a 1/ƒ noise analysis with uncorrelated noise. A system-noise analysis of the DuRKID, with comparisons to standard KIDs, is performed with generation-recombination noise and amplifier noise. The study reveals that the DuRKID can exhibit a large performance advantage over TLS-limited KID detectors.

N Forouzani↗
NASA NTRS2016

Explorations of Quantum-Classical Approaches to Scheduling a Mars Lander Activity Problem

An effective approach to solving problems involving mixed (continuous and discrete) variables and constraints, such as hybrid systems, is to decompose them into subproblems and integrate dedicated solvers geared toward those subproblems. Here, we introduce a new framework based on a tree search algorithm to solve hybrid discrete-continuous problems that incorporates: (1) a quantum annealer that samples from the configuration space for the discrete portion and provides information about the quality of the samples, and (2) a classical computer that makes use of information from the quantum annealer to prune and focus the search as well as check a continuous constraint. We consider four variants of our algorithm, each with progressively more guidance from the results provided by the quantum annealer. We empirically test our algorithm and compare the variants on a simplified Mars Lander task scheduling problem. Variants with more guidance from the quantum annealer have better performance.

scheduling↗
NASA NTRS2011

NASA Tech Briefs, March 2011

Topics covered include: Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation; Airborne Radar Interferometric Repeat-Pass Processing; Plug-and-Play Environmental Monitoring Spacecraft Subsystem; Power-Combined GaN Amplifier with 2.28-W Output Power at 87 GHz; Wallops Ship Surveillance System; Source Lines Counter (SLiC) Version 4.0; Guidance, Navigation, and Control Program; Single-Frame Terrain Mapping Software for Robotic Vehicles; Auto Draw from Excel Input Files; Observation Scheduling System; CFDP for Interplanetary Overlay Network; X-Windows Widget for Image Display; Binary-Signal Recovery; Volumetric 3D Display System with Static Screen; MMIC Replacement for Gunn Diode Oscillators; Feature Acquisition with Imbalanced Training Data; Mount Protects Thin-Walled Glass or Ceramic Tubes from Large Thermal and Vibration Loads; Carbon Nanotube-Based Structural Health Monitoring Sensors; Wireless Inductive Power Device Suppresses Blade Vibrations; Safe, Advanced, Adaptable Isolation System Eliminates the Need for Critical Lifts; Anti-Rotation Device Releasable by Insertion of a Tool; A Magnetically Coupled Cryogenic Pump; Single Piezo-Actuator Rotary-Hammering Drill; Fire-Retardant Polymeric Additives; Catalytic Generation of Lift Gases for Balloons; Ionic Liquids to Replace Hydrazine; Variable Emittance Electrochromics Using Ionic Electrolytes and Low Solar Absorptance Coatings; Spacecraft Radiator Freeze Protection Using a Regenerative Heat Exchanger; Multi-Mission Power Analysis Tool; Correction for Self-Heating When Using Thermometers as Heaters in Precision Control Applications; Gravitational Wave Detection with Single-Laser Atom Interferometers; Titanium Alloy Strong Back for IXO Mirror Segments; Improved Ambient Pressure Pyroelectric Ion Source; Multi-Modal Image Registration and Matching for Localization of a Balloon on Titan; Entanglement in Quantum-Classical Hybrid; Algorithm for Autonomous Landing; Quantum-Classical Hybrid for Information Processing; Small-Scale Dissipation in Binary-Species Transitional Mixing Layers; Superpixel-Augmented Endmember Detection for Hyperspectral Images; Coding for Parallel Links to Maximize the Expected Value of Decodable Messages; and Microwave Tissue Soldering for Immediate Wound Closure.

Source record↗
NASA NTRS2011

Magnetic Reconnection: Theoretical and Observational Perspectives: Preface

Magnetic reconnection is a fundamental plasma-physical process by which energy stored in a magnetic field is converted, often explosively, into heat and the kinetic energy of the charged particles that constitute the plasma. It occurs in a variety of astrophysical settings, ranging from the solar corona to pulsar magnetospheres and winds, as well as in laboratory fusion experiments, where it is responsible for sawtooth crashes. First proposed by R.G. Giovanelli in the late I 940s as the mechanism responsible for solar flares, magnetic reconnection was invoked at the beginning of the space age to explain not just solar flares but also the transfer of energy, mass, and momentum from the solar wind to Earth's magnetosphere and the subsequent storage and release of the transferred energy in the magnetotai\. During the half century or so that has followed the seminal theoretical works by J.W. Dungey, P.A. Sweet, E.N. Parker, and H.E. Petschek, in-situ measurements by Earth-orbiting satellites and remote-sensing observations of the solar corona have provided a growing body of evidence for the occurrence of reconnection at the Sun, in the solar wind, and in the near-Earth space environment. The last thirty years have also seen the development of laboratory reconnection experiments at a number of institutions. In parallel with the efforts of experimentalists in both space and laboratory plasma physics, theorists have investigated, analytically and with the help of increasingly powerful MHD, hybrid, and kinetic numerical simulations, the structure of the diffusion region, the factors controlling the rate, onset, and cessation of reconnection, and the detailed physics that enables the demagnetization of the ions and electrons and the topological reconfiguration of the magnetic field. Moreover, the scope of theoretical reconnection studies has been extended well beyond solar system and laboratory plasmas to include more exotic astrophysical plasma systems whose strong (10(exp 14)-10(exp 15) G) magnetic fields require that models of reconnection in these systems incorporate quantum electrodynamical, special relativistic, and radiative effects. The papers collected in this topical issue of Space Science Reviews cover different aspects of recent theoretical and observational work on magnetic reconnection in solar and space physics, astrophysics, and laboratory plasma physics. They derive from presentations given at a workshop on magnetic reconnection held in the Yosemite National Park, February 8-12,2010. The intent of the workshop was to stimulate, through a combination of tutorial talks, shorter focused talks, and extensive informal discussions, an interdisciplinary dialogue among members of the different research communities working on the problem of magnetic reconnection. One of the motivating considerations for holding the workshop was its relevance to NASA's Magnetospheric Multiscale (MMS) mission, scheduled for launch in 2014. The four identically instrumented MMS spacecraft are designed to study reconnect ion in Earth's magnetosphere and, specifically, to probe the electron diffusion region in order to determine the microphysical processes that enable the change in the topology of the magnetic field. Building on the achievements of the multi spacecraft Cluster and THEMIS missions, MMS will use the magnetosphere as an astrophysical plasma laboratory in which to test, through in-situ measurement of the plasma, energetic particles, and electric and magnetic fields, various models and theories that have emerged during the past twenty years, a period of extraordinarily productive theoretical and observational work.

Lewis, W. S.↗
NASA NTRSDate not supplied

Quantum-Assisted Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

The quantum computing community has been searching for suitable applications to demonstrate the potential of near-term quantum devices. Quantum machine learning is a potential candidate, particularly using models that cannot be efficiently simulated with classical computers [1, 2]. This work focuses on a transition phase of quantum computers where the quantum machine learning model is still simulable classically but projected not to be simulable as the size of the model grows. Ultimately quantum computers may have advantages for high-dimensional real-world problems. Due to the limited number of qubits in current noisy intermediate-scale quantum (NISQ) devices, the direct application of quantum computers in high dimensional data is not feasible. To remedy this problem, an encoder-decoder architecture can be utilized. The encoder model would transform the high-dimensional data into a compact representation, to a level that small quantum computers can be used today (or in the near future), and the decoder would take the quantum processed outputs back to the high-dimensional space. Addressing the two challenges of quantum machine learning, this work investigates a hybrid supervised generative model with a quantum Ising Born machine embedded as the latent distribution. The model contains four main parts (Figure 1.a.): (1) a U-NET architecture responsible for learning segmentation flow, (2) a Prior network responsible for learning an encoded latent distribution of the input data, (3) a Born machine which represents the latent distribution, and (4) a Posterior network in charge of learning the joint encoded latent distribution of inputs and target data. The initial model, proposed by [3], is optimized by (1) maximizing the overlap of the prior and posterior latent distributions, and (2) minimizing the segmentation loss. The proposed model is designed to be investigated in a simulation environment applied to the real-world application of wildfire segmentation. Specifically, the model is designed to solve the patchy wildfire segmentations of Moderate Resolution Imaging Spectroradiometer (MODIS) by taking the MODIS observations and using Visible Infrared Imaging Radiometer Suite’s (VIIRS) consistent wildfire product as the target. The model solves patchy wildfire segmentations and provides insight into the epistemic errors sourced from model variation. The model utilizes the Born machine as a QUBO solver to represent the latent space as a Bernoulli distribution. The proposed configuration allows the variational segmentation model to leverage the true quantum probabilistic nature and derive a more expressive latent configuration, increasing the model performance in describing wildfire segmentations. The quantum probabilistic information of the Born machine is directly incorporated in the Kullback-Leibler divergence loss in the prior and posterior distributions, forcing the Bernoulli latent distribution to maximize the overlap of input and joint input-target distributions. The proposed model is then trained and compared with a baseline only consisting of direct Bernoulli latent distribution with no Born machine representing the latent space. The models are evaluated based on the segmentation metrics, such as precision, recall, intersect of union, with uncertainty boundaries accounting for the stochastic nature of the model. Our findings show that even in low latent-dimensional space (due to the limit in computational power of the classical quantum simulator), we are able to effectively capture the latent representation and hence the model performs better than the baseline. The findings are a projection for scaling the model into higher dimensional latent space with the Born machine surpassing the baseline performance. Figure 1. Sub-figure (a) demonstrates the architecture for the training phase. The model consists of a Prior and Posterior network that encode inputs and joint input-target data into compact representations, respectively. The Born machine represents the latent distribution, and the U-NET branch learns the segmentation patterns of the data. The stochasticity is introduced to the U-NET through its last layer to create meaningful but stochastic segmentations. Sub-figure (b) represents the inference phase where the model takes the stochastic behavior from the prior network and injects that into the U-NET. Each attempt of inference will generate different but similar segmentations from the same distribution of the wildfire event. REFERENCES [1] Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020). The Born supremacy: quantum advantage and training of an Ising Born machine. npj Quantum Information, 6(1), 1-11. [2] Liu, J. G., & Wang, L. (2018). Differentiable learning of quantum circuit born machines. Physical Review A, 98(6), 062324. [3] Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., ... & Ronneberger, O. (2018). A probabilistic u-net for segmentation of ambiguous images. Advances in neural information processing systems, 31.

quantum machine learning↗