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Scheme for Entering Binary Data Into a Quantum Computer
A quantum algorithm provides for the encoding of an exponentially large number of classical data bits by use of a smaller (polynomially large) number of quantum bits (qubits). The development of this algorithm was prompted by the need, heretofore not satisfied, for a means of entering real-world binary data into a quantum computer. The data format provided by this algorithm is suitable for subsequent ultrafast quantum processing of the entered data. Potential applications lie in disciplines (e.g., genomics) in which one needs to search for matches between parts of very long sequences of data. For example, the algorithm could be used to encode the N-bit-long human genome in only log2N qubits. The resulting log2N-qubit state could then be used for subsequent quantum data processing - for example, to perform rapid comparisons of sequences.
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.
Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders
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.
Explicit expressions of the potential and its derivatives at the origin in terms of the scattering data
The quantum mechanical theory of scattering of a particle by a spherically symmetrical potential is presented. As in the inverse scattering problem, the input of the calculation is the scattering and bound-state data, and the output is data on the potential. The results discussed are explicit expressions for the values of the potential and its derivatives at the origin in terms of the scattering and boundstate data. Various methods to obtain these results are outlined. The presentation is aimed at introducing these various approaches. The simplest scattering problem (nonrelativistic S-wave scattering on a holomorphic potential without bound states) is used as the basis for discussion, and technicalities are omitted whenever possible without loss of clarity. A complete compilation is given of the results obtained to date in this field, including the treatment of higher partial waves and the Klein-Gordon and Dirac equations.
Measurement of tropospheric 300 nm solar ultraviolet flux for determination of O/1D/ photoproduction rate
The tropospheric importance of the OH radical, and the reaction scheme that leads to its formation, are now being widely investigated. Ozone photolysis at wavelengths no greater than 318 nm produces O(1D), a small fraction of which then reacts with water vapor to yield OH. Although experimental data are available for the O(1D) quantum yield, as well as the O3 absorption cross section, all previous tropospheric photochemical models have had to use theoretical calculations to determine the UV flux. Discussed in this paper are aircraft spectral measurements of the solar UV flux at two altitudes - 2 and 6 km. These results have been compared with three theoretical approaches. The measured experimental fluxes have been combined here with recent quantum yield data to calculate the O(1D) photoproduction rate for various albedo values. This rate is larger than that used in models by about a factor of 2 for reasonable values of assumed albedo.
Absorption and photoluminescence of ultrathin pseudomorphic InAs/GaAs quantum wells
Absorption data are presented for 2-4-monolayer InAs/GaAs single quantum wells obtained at 77 K using a polarization-based measurement technique. The special contribution of the optical loss features arising from bulk GaAs was minimized using the polarization selectivity of absorption in single quantum wells. The double structure observed in the spectra is attributed to transitions involving confined heavy holes and both confined and unconfined electron states.
Quantum Leap: Evaluating the Feasibility of Quantum Machine Learning Using NASA Earth Observational Data
This study explores the feasibility of leveraging quantum machine learning (QML) to analyze NASA Earth Observational (EO) data for climate change research, with a particular focus on the phenomenon of ”crop frosting” which has become more prevalent due to climate change. We implemented and evaluated two QML models, the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC), in both simulated and real quantum computing environments using a 127 qubit IBM quantum processor. Our study emphasizes the scientific rigor in comparing these quantum models with a classical Support Vector Machine (SVM) classifier, highlighting their performance in processing climate data. The results offer valuable insights into the potential scientific advantages, limitations, and scalability of QML for analyzing EO datasets, thus paving the way for more advanced climate modeling and predictive analytics using quantum computing. We showcased how Environmental Interaction Knowledge Graphs (EIKGs) and Digital Twins (DTs) can be integrated into this study. This research underscores the transformative potential of Classical and QML leveraging KGs and DT to address the multifaceted challenges posed by climate change.
Scaling Properties of Algorithms in Nanotechnology
At the present time, several technologies are pressing the limits of microminiature manufacturing. In semiconductor technology, for example, the Intel Pentium Pro (which is used in the Department of Energy's ASCI 'red' parallel supercomputer system) and the DEC Alpha 21164 (which is used in the CRAY T3E) both are fabricated using 0.35 micron process technology. Recently Texas Instruments (TI) announced the availability of 0.25 micron technology chips by the end of 1996 and plans to have 0.18 micron devices in production within two years. However, some significant challenges lie down the road. These include the skyrocketing cost of manufacturing plants, the 0.1 micron foreseeable limit of the photolithography process, quantum effects, data communication bandwidth limitations, heat dissipation, and others. Some related microminiature technologies include micro-electromechanical systems (MEMS), opto-electronic devices, quantum computing, biological computing, and others. All of these technologies require the fabrication of devices whose sizes are approaching the nanometer level. As such they are often collectively referred to with the name 'nanotechnology'. Clearly nanotechnology in this general sense is destined to be a very important technology of the 21st century. The ultimate dream in this arena is 'molecular nanotechnology', in other words the fabrication of devices and materials with most or all atoms and molecules in a pre-programmed position, possibly placed there by 'nano-robots'. This futuristic capability will probably not be achieved for at least two decades. However, it appears that somewhat less ambitious variations of molecular nanotechnology, such as devices and materials based on 'buckyballs' and 'nanotubes' may be realized significantly sooner, possibly within ten years or so. Even at the present time, semiconductor devices are approaching the regime where quantum chemical effects must be considered in design.
Hidden Statistics Approach to Quantum Simulations
Recent advances in quantum information theory have inspired an explosion of interest in new quantum algorithms for solving hard computational (quantum and non-quantum) problems. The basic principle of quantum computation is that the quantum properties can be used to represent structure data, and that quantum mechanisms can be devised and built to perform operations with this data. Three basic non-classical properties of quantum mechanics superposition, entanglement, and direct-product decomposability were main reasons for optimism about capabilities of quantum computers that promised simultaneous processing of large massifs of highly correlated data. Unfortunately, these advantages of quantum mechanics came with a high price. One major problem is keeping the components of the computer in a coherent state, as the slightest interaction with the external world would cause the system to decohere. That is why the hardware implementation of a quantum computer is still unsolved. The basic idea of this work is to create a new kind of dynamical system that would preserve the main three properties of quantum physics superposition, entanglement, and direct-product decomposability while allowing one to measure its state variables using classical methods. In other words, such a system would reinforce the advantages and minimize limitations of both quantum and classical aspects. Based upon a concept of hidden statistics, a new kind of dynamical system for simulation of Schroedinger equation is proposed. The system represents a modified Madelung version of Schroedinger equation. It preserves superposition, entanglement, and direct-product decomposability while allowing one to measure its state variables using classical methods. Such an optimal combination of characteristics is a perfect match for simulating quantum systems. The model includes a transitional component of quantum potential (that has been overlooked in previous treatment of the Madelung equation). The role of the transitional potential is to provide a jump from a deterministic state to a random state with prescribed probability density. This jump is triggered by blowup instability due to violation of Lipschitz condition generated by the quantum potential. As a result, the dynamics attains quantum properties on a classical scale. The model can be implemented physically as an analog VLSI-based (very-large-scale integration-based) computer, or numerically on a digital computer. This work opens a way of developing fundamentally new algorithms for quantum simulations of exponentially complex problems that expand NASA capabilities in conducting space activities. It has been illustrated that the complexity of simulations of particle interaction can be reduced from an exponential one to a polynomial one.
Laboratory instrumentation and techniques for characterizing multi-junction solar cells for space applications
Multi-junction solar cells are attractive for space applications because they can be designed to convert a larger fraction of AMO into electrical power at a lower cost than single-junction cells. The performance of multi-junction cells is much more sensitive to the spectral irradiance of the illuminating source than single-junction cells. The design of high efficiency multi-junction cells for space applications requires matching the optoelectronic properties of the junctions to AMO spectral irradiance. Unlike single-junction cells, it is not possible to carry out quantum efficiency measurements using only a monochromatic probe beam and determining the cell short-circuit current assuming linearity of the quantum efficiency. Additionally, current-voltage characteristics can not be calculated from measurements under non-AMO light sources using spectral-correction methods. There are reports in the literature on characterizing the performance of multi junction cells by measuring and convoluting the quantum efficiency of each junction with the spectral irradiance; the technique is of limited value for the characterization of cell performance under AMO power-generating conditions. We report the results of research to develop instrumentation and techniques for characterizing multi junction solar cells for space . An integrated system is described which consists of a standard lamp, spectral radiometer, dual-source solar simulator, and personal computer based current-voltage and quantum efficiency equipment. The spectral radiometer is calibrated regularly using the tungsten-halogen standard lamp which has a calibration based on NIST scales. The solar simulator produces the light bias beam for current-voltage and cell quantum efficiency measurements. The calibrated spectral radiometer is used to 'fit' the spectral irradiance of the dual-source solar simulator to WRL AMO data. The quantum efficiency apparatus includes a monochromatic probe beam for measuring the absolute cell quantum efficiency at various voltage biases, including the voltage bias corresponding to the maximum-power point under AMO light bias. The details of the procedures to 'fit' the spectral irradiance to AMO will be discussed. An assessment of the role of the accuracy of the 'fit' of the spectral irradiance and probe beam intensity on measured cell characteristics will be presented. quantum efficiencies were measured with both spectral light bias and AMO light bias; the measurements show striking differences. Spectral irradiances were convoluted with cell quantum efficiencies to calculate cell currents as function of voltage. The calculated currents compare with measured currents at the 1% level. Measurements on a variety of multi-junction cells will be presented. The dependence of defects in junctions on cell quantum efficiencies measured under light and voltage bias conditions will be presented. Comments will be made on issues related to standards for calibration, and limitations of the instrumentation and techniques. Expeditious development of multi-junction solar cell technology for space presents challenges for cell characterization in the laboratory.
Low-cost DH and quantum well laser array development
The intial results of a program aimed at developing low-cost diode laser arrays for use as solid-state laser pumps are reported. MOCVD is used to demonstrate excellent run-to-run reproducibility in emission wavelength, threshold current density, and quantum efficiency. For this first experimental series, J(th) values of approximately 1310 Amps/sq cm were obtained for broad-area unthinned devices from the growth runs. Differential quantum efficiencies of between 41 percent and 47 percent were measured on the non-facet-coated devices from all four runs. Single quantum well, separate confinement heterostructure lasers fabricated from wafers grown in the same MOCVD reactor exhibited near single-mode emission, with J(th) values of approximately 300 Amps/sq cm. Photoluminescence data confirm quantum well widths of 80 A and 150 A for two different MOCVD growth runs.
An Experimental and Theoretical Study of Nitrogen-Broadened Acetylene Lines
We present experimental nitrogen-broadening coefficients derived from Voigt profiles of isotropic Raman Q-lines measured in the 2 band of acetylene (C2H2) at 150 K and 298 K, and compare them to theoretical values obtained through calculations that were carried out specifically for this work. Namely, full classical calculations based on Gordon's approach, two kinds of semi-classical calculations based on Robert Bonamy method as well as full quantum dynamical calculations were performed. All the computations employed exactly the same ab initio potential energy surface for the C2H2N2 system which is, to our knowledge, the most realistic, accurate and up-to-date one. The resulting calculated collisional half-widths are in good agreement with the experimental ones only for the full classical and quantum dynamical methods. In addition, we have performed similar calculations for IR absorption lines and compared the results to bibliographic values. Results obtained with the full classical method are again in good agreement with the available room temperature experimental data. The quantum dynamical close-coupling calculations are too time consuming to provide a complete set of values and therefore have been performed only for the R(0) line of C2H2. The broadening coefficient obtained for this line at 173 K and 297 K also compares quite well with the available experimental data. The traditional Robert Bonamy semi-classical formalism, however, strongly overestimates the values of half-width for both Qand R-lines. The refined semi-classical Robert Bonamy method, first proposed for the calculations of pressure broadening coefficients of isotropic Raman lines, is also used for IR lines. By using this improved model that takes into account effects from line coupling, the calculated semi-classical widths are significantly reduced and closer to the measured ones.
Photophysical and photochemical properties of sterically hindered aryldiazonium salts
Sterically hindered aryldiazonium salts absorption spectra and quantum yield data interpretation, considering photochemical properties
The excitation function of OH(A2Sigma/+/ - X2Pi) fluorescence produced through photodissociation of H2O
The absolute cross section for production of OH(A2Sigma/+/ X2Pi) fluorescence through photodissociative excitation of H2O was obtained using the University of Wisconsin synchrotron radiation source. The absolute fluorescence cross section function shows three broad features, roughly centered at 710, 400, and 240 A; the maximum value fluorescence cross section was found to be 3.4 x 10 to the -20th sq cm at 703 A. The broad features in the 700-A and 400-A regions are considered to be due to the second and the third excited states of H2O, respectively; it is argued that the broad feature at 240 A may have an origin similar to that of the 400-A broad feature. From present and previous data on quantum yields, it is estimated that part of the yield may be attributable to the production of neutral ground state OH(X) and H(1 2S) fragments. Such a process could provide a significant source of high kinetic energy OH and H observed in cometary atmospheres.
Annihilation physics of exotic galactic dark matter particles
Various theoretical arguments make exotic heavy neutral weakly interacting fermions, particularly those predicted by supersymmetry theory, attractive candidates for making up the large amount of unseen gravitating mass in galactic halos. Such particles can annihilate with each other, producing secondary particles of cosmic-ray energies, among which are antiprotons, positrons, neutrinos, and gamma-rays. Spectra and fluxes of these annihilation products can be calculated, partly by making use of positron electron collider data and quantum chromodynamic models of particle production derived therefrom. These spectra may provide detectable signatures of exotic particle remnants of the big bang.
Monte Carlo calculation of dynamical properties of the two-dimensional Hubbard model
A new method is introduced for analytically continuing imaginary-time data from quantum Monte Carlo calculations to the real-frequency axis. The method is based on a least-squares-fitting procedure with constraints of positivity and smoothness on the real-frequency quantities. Results are shown for the single-particle spectral-weight function and density of states for the half-filled, two-dimensional Hubbard model.
Application of the Stogryn-Hirschfelder treatment of weak dimers to planetary atmospheres
The thermodynamics of carbon dioxide dimerization is treated as an example of the evaluation of weak dimer populations in planetary atmospheres (e.g., Mars and Venus). Two approaches considered are the Stogryn-Hirschfelder (1960) treatment using the Lennard-Jones interaction and calculations based on recent quantum chemical data. Several improvements of these treatments are developed. It is indicated that carbon dioxide dimers may be less or more abundant at the surfaces of Mars or Venus, respectively, than is suggested by previous calculations.