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The fundamental, quantum phase noise limited Lorentzian linewidth was directly measured from the beat-note spectra generated by heterodyning PbS(1-x)Se(x) diode lasers with a stable CO gas laser. The experimental results were matched by calculated theoretical line profiles. Linewidths as narrow as 22 kHz full width at half-maximum power were observed.
Quantum phase noise limited Lorentzian power spectral densities were achieved with tunable lead-salt diode lasers. Linewidths as narrow as 22 kHz were observed. A truly programmable infrared synthesizer was produced by frequency-offset-locking the tunable diode lasers to the combination of a stable CO2 (or CO) reference laser and a programmable microwave frequency synthesizer. Absolute frequency accuracy and reproducibility of about + or - 30 kHz (0.000001 kaysers) relative to the primary Cs frequency standard may now be obtained with this technique.
A paper presents a theoretical investigation of subsonic and supersonic effects in a Bose-Einstein condensate (BEC). The BEC is represented by a time-dependent, nonlinear Schroedinger equation that includes terms for an external confining potential term and a weak interatomic repulsive potential proportional to the number density of atoms. From this model are derived Madelung equations, which relate the quantum phase with the number density, and which are used to represent excitations propagating through the BEC. These equations are shown to be analogous to the classical equations of flow of an inviscid, compressible fluid characterized by a speed of sound (g/Po)1/2, where g is the coefficient of the repulsive potential and Po is the unperturbed mass density of the BEC. The equations are used to study the effects of a region of perturbation moving through the BEC. The excitations created by a perturbation moving at subsonic speed are found to be described by a Laplace equation and to propagate at infinite speed. For a supersonically moving perturbation, the excitations are found to be described by a wave equation and to propagate at finite speed inside a Mach cone.
The prescription for introducing a gauge transformation into a quantum transition amplitude, nominally well known, contains an ambiguous feature. It is presumed by some authors that an appropriate transformation of the phase of a wave function will generate the associated gauge transformation. It is shown that this is a necessary but not sufficient step. Examples from the literature are cited to show the consequences of the failure of this procedure. One must distinguish between true gauge transformations and unitary transformations within a fixed gauge.
Accurate values of phase shifts calculated by integration of quantum mechanical Hamilton- Jacobi equation
Phase 1 of this work involved design work on a momentum transfer device. The progress on design and testing will be presented. Phase 2 involved the systematic study of the MPD thruster for dual uses. Though it was designed as a thruster for space vehicles, the characteristics of the plasma make it an excellent candidate for industrial applications. This project sought to characterize the system for use in materials processing and characterization. The surface modification on ZnCdTe, CdTe, and ZnTe will be presented. Phase 3 involved metal colloids and semiconductor quantum dots. One aspect of this project involves a collaborative effort with the Solid State Division of ORNL. The thrust behind this research is to develop ion implantation for synthesizing novel materials (quantum dots wires and wells, and metal colloids) for applications in all optical switching devices, up conversion, and the synthesis of novel refractory materials. The ions of interest are Au, Ag, Cd, Se, In, P, Sb, Ga, and As. The specific materials of interest are: CdSe, CdTe, InAs, GaAs, InP, GaP, InSb, GaSb, and InGaAs. A second aspect of this research program involves using porous glass (25-200 A) for fabricating materials of finite size. The results of some of this work will also be reported.
Quantum mechanical calculations for Lennard- Jones potential show dependence of barrier penetration and resonance effects on phase shifts
Quantum mechanical calculations to evaluate barrier penetration and resonance effects on phase shifts
Phase shift method using integration of quantum- mechanical Hamilton-Jacobi equation applied to particles interacting through Lennard-Jones potential
This paper presents a theory for quantum light propagation in a single-mode fiber which includes the effects of the Kerr nonlinearity, group-velocity dispersion, and linear loss. The theory reproduces the results of classical self-phase modulation, quantum four-wave mixing, and classical solution physics, within their respective regions of validity. It demonstrates the crucial role played by the Kerr-effect material time constant, in limiting the quantum phase shifts caused by the broadband zero-point fluctuations that accompany any quantized input field. Operator moment equations - approximated, numerically, via a terminated cumulant expansion - are used to obtain results for homodyne-measurement noise spectra when dispersion is negligible. More complicated forms of these equations can be used to incorporate dispersion into the noise calculations.
Quantum mechanical inverse problem of elastic scattering theory, discussing Hylleraas solution for determining potential energy function from phase shift
The dynamics of a second-order phase transition during inflation, which is induced by time-variation of spacetime curvature, is studied as a natural mechanism to produce topological defects of typical grand unification scales such as cosmic strings or global textures. It is shown that their distribution is almost scale-invariant with small- and large-scale cutoffs. Also discussed is how these cutoffs are given.
Performance of ideal quantum receiver of coherent signal of random phase
A new formalism for analyzing two-photon devices, such as parametric amplifiers and phase-conjugate mirrors, is proposed in part I, focusing on the properties and the significance of the quadrature-phase amplitudes and two-mode squeezed states. Time-stationary quasi-probability noise is also detailed for the case of Gaussian noise, and uncertainty principles for the quadrature-phase amplitudes are outlined, as well as some important properties of the two-mode states. Part II establishes a mathematical foundation for the formalism, with introduction of a vector notation for compact representation of two-mode properties. Fundamental unitary operators and special quantum states are also examined with an emphasis on the two-mode squeezed states. The results are applied to a previously studied degenerate limit (epsilon = 0).
Experiments in liquid phase epitaxial fabrication of thin GaAs/GaAlAs layers over a planar substrates have been carried out. Layer thicknesses smaller than 300 A were routinely obtained, with the best result being 120 A. Interface sharpness between the layers is approximately 10 A, which is comparable to OMCVD results, but the layers' thicknesses are usually not uniform. Of the experimental parameters, the growth time and the cooling rate seem to have the largest effect on the obtained layer thickness, while the growth temperature and the substrate crystallographic orientation produce less noticeable effects. Quantum effects in the grown layers were observed by photoluminescence measurements.
We demonstrate the phase locking of a 2.7 THz metal-metal waveguide quantum cascade laser (QCL) to an external microwave signal. The reference is the 15th harmonic, generated by a semiconductor superlattice nonlinear device, of a signal at 182 GHz, which itself is generated by a multiplier chain (x 12) from a microwave synthesizer at approx. 15 GHz. Both laser and reference radiations are coupled into a bolometer mixer, resulting in a beat signal, which is fed into a phase-lock loop. The spectral analysis of the beat signal confirms that the QCL is phase locked. This result opens the possibility to extend heterodyne interferometers into the far-infrared range.
Described are the theoretical development and computer implementation of reliable and efficient methods for the analysis of coupled mechanical problems that involve the interaction of mechanical, thermal, phase-change and electromag subproblems. The focus application has been the modeling of superconductivity and associated quantum-state phase change phenomena. In support of this objective the work has addressed the following issues: (1) development of variational principles for finite elements, (2) finite element modeling of the electromagnetic problem, (3) coupling of thermel and mechanical effects, and (4) computer implementation and solution of the superconductivity transition problem. The main accomplishments have been: (1) the development of the theory of parametrized and gauged variational principles, (2) the application of those principled to the construction of electromagnetic, thermal and mechanical finite elements, and (3) the coupling of electromagnetic finite elements with thermal and superconducting effects, and (4) the first detailed finite element simulations of bulk superconductors, in particular the Meissner effect and the nature of the normal conducting boundary layer. The theoretical development is described in two volumes. Volume 1 describes mostly formulation specific problems. Volume 2 describes generalization of those formulations.
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.