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Devices and techniques for using a prism to couple IR radiation to a quantum-well sensor with a polarization substantially perpendicular to the quantum-well layers.
Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.
Mechatronics is the synergistic application of mechanics, electronics, control engineering, and computer science in the development of electromechanical products and systems, through integrated design. This paper proposes to extend the mechatronics course beyond traditional engineering topics, and to modernize the mechatronics instructions with complementary quantum engineering topics. With the recent rapid advances in quantum technologies such as quantum communications, sensing, computers, and algorithms, it is imperative to train the next generation of engineers and prepare them for their future careers in the ever-changing industry in such areas. Furthermore, due to such progress and advances in the fields associated with quantum mechanics, the integration of quantum technologies with classical mechanical systems will be inevitable both in terms of educational and technological standpoints in future. To address the educational needs of the future engineers in such areas of significant importance, quantum entanglement and quantum cryptography experiments, as two fundamental topics in quantum mechanics, are brought into the mechatronics course in an initiative that is reported in this paper. The integrated quantum and mechatronics topics also provides opportunities for open discussions on exploring the interface of quantum technologies and classical engineering systems, which can potentially push the engineering boundaries beyond classical possibilities by accessing the quantum advantages. An innovative online remote demonstration of such quantum experiments are developed and presented to the students. This course has been offered to undergraduate students once with successful results. The students were able to remotely access the experiments, perform the experiments and collect data. The successful result of such quantum experiments is also reflected in a course survey, presented in this paper, even though the quantum mechanics topics offered in this course are unfamiliar to engineering students and hence more challenging. The paper reports, and aims to promote, the integration of selected quantum technology topics with the mechatronics course for training engineering students in this rapidly growing area.
Quantum Entanglement Molecular Absorption Spectrum Simulator (QE-MASS) is a computer program for simulating two photon molecular-absorption spectroscopy using quantum-entangled photons. More specifically, QE-MASS simulates the molecular absorption of two quantum-entangled photons generated by the spontaneous parametric down-conversion (SPDC) of a fixed-frequency photon from a laser. The two-photon absorption process is modeled via a combination of rovibrational and electronic single-photon transitions, using a wave-function formalism. A two-photon absorption cross section as a function of the entanglement delay time between the two photons is computed, then subjected to a fast Fourier transform to produce an energy spectrum. The program then detects peaks in the Fourier spectrum and displays the energy levels of very short-lived intermediate quantum states (or virtual states) of the molecule. Such virtual states were only previously accessible using ultra-fast (femtosecond) laser systems. However, with the use of a single-frequency continuous wave laser to produce SPDC photons, and QEMASS program, these short-lived molecular states can now be studied using much simpler laser systems. QE-MASS can also show the dependence of the Fourier spectrum on the tuning range of the entanglement time of any externally introduced optical-path delay time. QE-MASS can be extended to any molecule for which an appropriate spectroscopic database is available. It is a means of performing an a priori parametric analysis of entangled photon spectroscopy for development and implementation of emerging quantum-spectroscopic sensing techniques. QE-MASS is currently implemented using the Mathcad software package.
A proposed integrated circuit based on quantum-dot cellular automata (QCA) would function as a bit-serial adder. This circuit would serve as a prototype building block for demonstrating the feasibility of quantum-dots computing and for the further development of increasingly complex and increasingly capable quantum-dots computing circuits. QCA-based bit-serial adders would be especially useful in that they would enable the development of highly parallel and systolic processors for implementing fast Fourier, cosine, Hartley, and wavelet transforms. The proposed circuit would complement the QCA-based circuits described in "Implementing Permutation Matrices by Use of Quantum Dots" (NPO-20801), NASA Tech Briefs, Vol. 25, No. 10 (October 2001), page 42 and "Compact Interconnection Networks Based on Quantum Dots" (NPO-20855), which appears elsewhere in this issue. Those articles described the limitations of very-large-scale-integrated (VLSI) circuitry and the major potential advantage afforded by QCA. To recapitulate: In a VLSI circuit, signal paths that are required not to interact with each other must not cross in the same plane. In contrast, for reasons too complex to describe in the limited space available for this article, suitably designed and operated QCA-based signal paths that are required not to interact with each other can nevertheless be allowed to cross each other in the same plane without adverse effect. In principle, this characteristic could be exploited to design compact, coplanar, simple (relative to VLSI) QCA-based networks to implement complex, advanced interconnection schemes. To enable a meaningful description of the proposed bit-serial adder, it is necessary to further recapitulate the description of a quantum-dot cellular automation from the first-mentioned prior article: A quantum-dot cellular automaton contains four quantum dots positioned at the corners of a square cell. The cell contains two extra mobile electrons that can tunnel (in the quantum-mechanical sense) between neighboring dots within the cell. The Coulomb repulsion between the two electrons tends to make them occupy antipodal dots in the cell. For an isolated cell, there are two energetically equivalent arrangements (denoted polarization states) of the extra electrons. The cell polarization is used to encode binary information. Because the polarization of a nonisolated cell depends on Coulomb-repulsion interactions with neighboring cells, universal logic gates and binary wires could be constructed, in principle, by arraying QCA of suitable design in suitable patterns. Again, for reasons too complex to describe here, in order to ensure accuracy and timeliness of the output of a QCA array, it is necessary to resort to an adiabatic switching scheme in which the QCA array is divided into subarrays, each controlled by a different phase of a multiphase clock signal. In this scheme, each subarray is given time to perform its computation, then its state is frozen by raising its inter-dot potential barriers and its output is fed as the input to the successor subarray. The successor subarray is kept in an unpolarized state so it does not influence the calculation of preceding subarray. Such a clocking scheme is consistent with pipeline computation in the sense that each different subarray can perform a different part of an overall computation. In other words, QCA arrays are inherently suitable for pipeline and, moreover, systolic computations. This sequential or pipeline aspect of QCA would be utilized in the proposed bit-serial adders.
No abstract available
Effects of electron irradiation on some of the optical properties in organic CdSe nanocrystals coated in trioctylphosphine oxide (TOPO) and biologically compatible CdSe nanocrystals coated in mercaptoacetic acid, as CdSe as CdSe nanocrystals conjugated with the protein are investigated using the technique of cathodoluminescence. Effects of varying the beam energy and temperatures were examined and faster degradation at cryogenic temperatures and higher beam energies was found under some conditions.
A quantum well infrared photodetector (QWIP) that provides two-color image sensing. Two different quantum wells are configured to absorb two different wavelengths. The QWIPs are arrayed in a focal plane array (FPA). The two-color QWIPs are selected for readout by selective electrical contact with the two different QWIPs or by the use of two different wavelength sensitive gratings.
Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.
Inertial sensors reviewing ring laser, electrostatic fluid sphere, magnetic resonance, superconductivity gyroscope, relativistic rotating tensor, vortex rate and quantum mechanical effect sensing
We show that the supersymmetric structure (in the sense of supersymmetric quantum mechanics) appears in Helmholtz optics describing light propagation in waveguides. For the case of elliptical waveguides, with the accuracy of paraxial approximation it admits a simple physical interpretation. The supersymmetry connects light beams of different colors. The difference in light frequencies for the supersymmetric beams is determined by the transverse gradient of the refractive index. These beams have the save wavelength in the propagation direction and can form a stable interference pattern.
A quantum-mechanical transport equation (QMTE) is derived which should be applicable to a wide range of problems involving the interaction of radiation with atoms or molecules which are also subject to collisions with perturber atoms. The equation follows the time evolution of the macroscopic atomic density matrix elements of atoms located at classical position R and moving with classical velocity v. It is quantum mechanical in the sense that all collision kernels or rates which appear have been obtained from a quantum-mechanical theory and, as such, properly take into account the energy-level variations and velocity changes of the active (emitting or absorbing) atom produced in collisions with perturber atoms. The present formulation is better suited to problems involving high-intensity external fields, such as those encountered in laser physics.
A heterogeneous material construction has been devised for sensing coils of superconducting quantum interference device (SQUID) magnetometers that are subject to a combination of requirements peculiar to some advanced applications, notably including low-field magnetic resonance imaging for medical diagnosis. The requirements in question are the following: The sensing coils must be large enough (in some cases having dimensions of as much as tens of centimeters) to afford adequate sensitivity; The sensing coils must be made electrically superconductive to eliminate Johnson noise (thermally induced noise proportional to electrical resistance); and Although the sensing coils must be cooled to below their superconducting- transition temperatures with sufficient cooling power to overcome moderate ambient radiative heat leakage, they must not be immersed in cryogenic liquid baths. For a given superconducting sensing coil, this combination of requirements can be satisfied by providing a sufficiently thermally conductive link between the coil and a cold source. However, the superconducting coil material is not suitable as such a link because electrically superconductive materials are typically poor thermal conductors. The heterogeneous material construction makes it possible to solve both the electrical- and thermal-conductivity problems. The basic idea is to construct the coil as a skeleton made of a highly thermally conductive material (typically, annealed copper), then coat the skeleton with an electrically superconductive alloy (typically, a lead-tin solder) [see figure]. In operation, the copper skeleton provides the required thermally conductive connection to the cold source, while the electrically superconductive coating material shields against Johnson noise that originates in the copper skeleton.
An improvement in the design and fabrication of sensing coils of superconducting quantum interference device (SQUID) magnetometers has been proposed to increase sensitivity. It has been estimated that, in some cases, it would be possible to increase sensitivity by about half or to reduce measurement time correspondingly. The pertinent aspects of the problems of design and fabrication can be summarized as follows: In general, to increase the sensitivity of a SQUID magnetometer, it is necessary to maximize the magnetic flux enclosed by the sensing coil while minimizing the self-inductance of this coil. It is often beneficial to fabricate the coil from a thicker wire to reduce its self-inductance. Moreover, to optimize the design of the coil with respect to sensitivity, it may be necessary to shape the wire to other than a commonly available circular or square cross-section. On the other hand, it is not practical to use thicker superconducting wire for the entire superconducting circuit, especially if the design of a specific device requires a persistent-current loop enclosing a remotely placed SQUID sensor. It may be possible to bond a thicker sensing-coil wire to thinner superconducting wires leading to a SQUID sensor, but it could be difficult to ensure reliable superconducting connections, especially if the bonded wires are made of different materials. The main idea is to mold the sensing coil in place, to more nearly optimum cross sectional shape, instead of making the coil by winding standard pre-fabricated wire. For this purpose, a thin superconducting wire loop that is an essential part of the SQUID magnetometer would be encapsulated in a form that would serve as a mold. A low-melting-temperature superconducting metal (e.g., indium, tin, or a lead/tin alloy) would be melted into the form, which would be sized and shaped to impart the required cross section to the coil thus formed.
Achievable RMS errors in estimating the phase, frequency, and intensity of a direct-detected intensity-modulated optical pulse train are presented. For each parameter, the Cramer-Rao-Bound (CRB) is derived and the performance of the Maximum Likelihood estimator is illustrated. Approximations to the CRBs are provided, enabling an intuitive understanding of estimator behavior as a function of the signaling parameters. The results are compared to achievable RMS errors in estimating the same parameters from a sinusoidal waveform in additive white Gaussian noise. This establishes a framework for a performance comparison of radio frequency (RF) and optical science. Comparisons are made using parameters for state-of-the-art deep-space RF and optical links. Degradations to the achievable errors due to clock phase noise and detector jitter are illustrated.
GaAs based Quantum Well Infrared Photodetector (QWIP) technology has shown remarkable success in advancing low cost, highly uniform, high-operability, large format multi-color focal plane arrays. QWIPs afford greater flexibility than the usual extrinsically doped semiconductor IR detectors. The wavelength of the peak response and cutoff can be continuously tailored over a range wide enough to enable light detection at any wavelength range between 6 and 20 micron. The spectral band-width of these detectors can be tuned from narrow (Deltalambda/lambda is approximately 10%) to wide (Deltalambda/lambda is approximately 40%) allowing various applications. Furthermore, QWIPs offer low cost per pixel and highly uniform large format focal plane arrays due to mature GaAs/AlGaAs growth and processing technologies. The other advantages of GaAs/AlGaAs based QWIPS are higher yield, lower l/f noise and radiation hardness (1.5 Mrad). In this presentation, we will discuss our recent demonstrations of 640x512 pixel narrow-band, broad-band, multi-band focal plane arrays, and the current status of the development of 1024x1024 pixel long-wavelength infrared QWIP focal plane arrays.
A new gradiometer scheme uses middle loops as sensing elements in lowfield superconducting quantum interference device (SQUID) magnetic resonance imaging (MRI). This design of a second order gradiometer increases its sensitivity and makes it more uniform, compared to the conventional side loop sensing scheme with a comparable matching SQUID. The space between the two middle loops becomes the imaging volume with the enclosing cryostat built accordingly.