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At least 595 records · Page 33

Chaotic roots of the modular multiplication dynamical system in Shor's algorithm

Shor's factoring algorithm, believed to provide an exponential speedup over classical computation, relies on finding the period of an exactly periodic quantum modular multiplication operator. This exact periodicity is the hallmark of an integrable system, which is paradoxical from the viewpoint of quantum chaos, given that the classical limit of the modular multiplication operator is a highly chaotic system that occupies the “maximally random” Bernoulli level of the classical ergodic hierarchy. In this work, we approach this apparent paradox from a quantum dynamical systems viewpoint, and consider whether signatures of ergodicity and chaos may indeed be encoded in such an “integrable” quantization of a chaotic system. We show that Shor's modular multiplication operator, in specific cases, can be written as a superposition of quantized 𝐴-baker's maps exhibiting more typical signatures of quantum chaos and ergodicity. This work suggests that the integrability of Shor's modular multiplication operator may stem from the interference of other “chaotic” quantizations of the same family of maps, and paves the way for deeper studies on the interplay of integrability, ergodicity, and chaos in and via quantum algorithms.

Dynamical systems↗

Coherent scattering of an atom in the field of a standing wave under conditions of initial quantum correlation of subsystems

Coherent scattering of a two-level atom in the field of a quantized standing wave of a micromaser is considered under conditions of initial quantum correlation between the atom and the field. Such a correlation can be produced by a broadband parametric source. The interaction leading to scattering of the atom from the nonuniform field occurs in the dispersion limit or in the wing of the absorption line of the atom. Apart from the quantized field, the atom simultaneously interacts with two classical counterpropagating waves with different frequencies, which are acting in the plane perpendicular to the atom’s propagation velocity and to the wavevector of the standing wave. Joint action of the quantized field and two classical waves induces effective two-photon and Raman resonance interaction on the working transition. The effective Hamiltonian of the interaction is derived using the unitary transformation method developed for a moving atom. A strong effect is detected, which makes it possible to distinguish the correlated initial state of the atom and the field in the scattering of atom from the state of independent systems. For all three waves, scattering is not observed when systems with quantum correlation are prepared using a high-intensity parametric source. Conversely, when the atom interacts only with the nonuniform field of the standing wave, scattering is not observed in the case of the initial factorized state.

74 ATOMIC AND MOLECULAR PHYSICS↗

Lion Cub: Minimizing Communication Overhead in Distributed Lion

Communication overhead is a key challenge in distributed deep learning, especially on slower Ethernet intercon nects, and given current hardware trends, communication is likely to become a major bottleneck. While gradient compression techniques have been explored for SGD and Adam, the Lion optimizer has the distinct advantage that its update vectors are the output of a sign operation, enabling straightforward quantization. However, simply compressing updates for communication and using techniques like majority voting fails to lead to end-to-end speedups due to inefficient communication algorithms and reduced convergence. We analyze three factors critical to distributed learning with Lion: optimizing communication methods, identifying effective quantization methods, and assessing the necessity of momentum synchronization. Our findings show that quantization techniques adapted to Lion and selective momentum synchronization can significantly reduce communication costs while maintaining convergence. We combine these into Lion Cub, which enables up to 5x speedups in end-to-end training compared to Lion. This highlights Lion’s potential as a communication-efficient solution for distributed training.

97 MATHEMATICS AND COMPUTING↗

Can We Detect the Quantum Nature of Weak Gravitational Fields?

A theoretical framework for the quantization of gravity has been an elusive Holy Grail since the birth of quantum theory and general relativity. While generations of scientists have attempted to find solutions to this deep riddle, an alternative path built upon the idea that experimental evidence could determine whether gravity is quantized has been decades in the making. The possibility of an experimental answer to the question of the quantization of gravity is of renewed interest in the era of gravitational wave detectors. We review and investigate an important subset of phenomenological quantum gravity, detecting quantum signatures of weak gravitational fields in table-top experiments and interferometers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Performance of convolution coding concatenated with MFSK modulation in a Gaussian channel

The improvement in db due to concatenation over conventional M-ary coding is studied to reduce the probability of a bit error and to increase the available bit rate for the same system parameters of error rate, transmitter power, and range. The results of calculations for orthogonal modulation with noncoherent detection and Q-level correlator quantization are presented. It is shown that the correlator outputs are quantized to one of the Q levels, and the receiver output is a vector consisting of a list of the M correlator quantum levels. The channel has Q(M) possible outputs and M possible inputs. Optimum output is approached by increasing fine quantization

Choudhury, A. K.↗

Control system optimization studies. Volume 2: High frequency cutoff filter analysis

The problem of digital implementation of a cutoff filter is approached with consideration to word length, sampling rate, accuracy requirements, computing time and hardware restrictions. Computing time and hardware requirements for four possible programming forms for the linear portions of the filter are determined. Upper bounds for the steady state system output error due to quantization for digital control systems containing a digital network programmed both in the direct form and in the canonical form are derived. This is accomplished by defining a set of error equations in the z domain and then applying the final value theorem to the solution. Quantization error was found to depend upon the digital word length, sampling rate, and system time constants. The error bound developed may be used to estimate the digital word length and sampling rate required to achieve a given system specification. From the quantization error accumulation, computing time and hardware point of view, and the fact that complex poles and zeros must be realized, the canonical form of programming seems preferable.

Fong, M. H.↗

Digital television system design study

The use of digital techniques for transmission of pictorial data is discussed for multi-frame images (television). Video signals are processed in a manner which includes quantization and coding such that they are separable from the noise introduced into the channel. The performance of digital television systems is determined by the nature of the processing techniques (i.e., whether the video signal itself or, instead, something related to the video signal is quantized and coded) and to the quantization and coding schemes employed.

Huth, G. K.↗

Evaluation of NASA speech encoder

Techniques developed by NASA for spaceflight instrumentation were used in the design of a quantizer for speech-decoding. Computer simulation of the actions of the quantizer was tested with synthesized and real speech signals. Results were evaluated by a phometician. Topics discussed include the relationship between the number of quantizer levels and the required sampling rate; reconstruction of signals; digital filtering; speech recording, sampling, and storage, and processing results.

Source record↗

Radiometric resolution for monitoring vegetation: How many bits are needed?

The significance of the various number of radiometric quantizing levels required for satellite monitoring of vegetation resources was evaluated by using in situ collected spectral reflectance data, an atmospheric radiative transfer simulation model, and a satellite sensor simulation model. Reflectance data were converted to radiance data; passed through a model atmosphere to an altitude of 706 km; and subsequently quantized at 16, 32, 64, 128, 256, and 512 digital count levels for Thematic Mapper bands TM3(0.63 - 0.69 microns) and TM4(0.76 - 0.90 microns). The simulated digital count data were regressed against the in situ biological data to quantify the relationship between quantizing levels.

Tucker, C. J.↗

Effect of lead-aircraft ground-speed on self-spacing performance using a cockpit display of traffic information

A simulator investigation was conducted to determine the effect of the lead-aircraft ground-speed quantization level on self-spacing performance using a Cockpit Display of Traffic Information (CDTI). The study utilized a simulator employing cathode-ray tubes for the primary flight and navigation displays and highly augmented flight control modes. The pilot's task was to follow, and self-space on, a lead aircraft which was performing an idle-thrust profile descent to an instrument landing system (ILS) approach and landing. The spacing requirement was specified in terms of both a minimum distance and a time interval. The results indicate that the ground-speed quantization level, lead-aircraft scenario, and pilot technique had a significant effect on self-spacing performance. However, the ground-speed quantization level only had a significant effect on the performance when the lead aircraft flew a fast final approach.

Kelly, J. R.↗

Performance Simulation for Unit-memory Convolutional Codes with Byte-oriented Viterbi Decoding Algorithm

A software package developed to simulate the performance of the byte-oriented Viterbi decoding algorithm for unit-memory (UM) codes on both 3-bit and 4-bit quantized AWGN channels is described. The simulation is shown to require negligible memory and less time than that for the RTMBEP algorith, although they both provide similar performance in terms of symbol-error probability. This makes it possible to compute the symbol-error probability of large codes and to determine the signal-to-noise ratio required to achieve a bit error rate (BER) of 0.000001 for corresponding concatenated systems. A (7, 10/48) UM code, 10-bit Reed-Solomon code combination achieves the required BER at 1.08 dB for a 3-bit quantized channel and at 0.91 dB for a 4-bit quantized channel.

Vo, Q. D.↗

Optimal block cosine transform image coding for noisy channels

The two dimensional block transform coding scheme based on the discrete cosine transform was studied extensively for image coding applications. While this scheme has proven to be efficient in the absence of channel errors, its performance degrades rapidly over noisy channels. A method is presented for the joint source channel coding optimization of a scheme based on the 2-D block cosine transform when the output of the encoder is to be transmitted via a memoryless design of the quantizers used for encoding the transform coefficients. This algorithm produces a set of locally optimum quantizers and the corresponding binary code assignment for the assumed transform coefficient statistics. To determine the optimum bit assignment among the transform coefficients, an algorithm was used based on the steepest descent method, which under certain convexity conditions on the performance of the channel optimized quantizers, yields the optimal bit allocation. Comprehensive simulation results for the performance of this locally optimum system over noisy channels were obtained and appropriate comparisons against a reference system designed for no channel error were rendered.

Vaishampayan, V.↗

The capacity of the Hopfield associative memory

Techniques from coding theory are applied to study rigorously the capacity of the Hopfield associative memory. Such a memory stores n-tuple of + or - 1s. The components change depending on a hard-limited version of linear functions of all other components. With symmetric connections between components, a stable state is ultimately reached. By building up the connection matrix as a sum-of-outer products of m fundamental memories, it may be possible to recover a certain one of the m memories by using an initial n-tuple probe vector less than a Hamming distance n/2 away from the fundamental memory. If m fundamental memories are chosen at random, the maximum asymptotic value of m in order that most of the m original memories are exactly recoverable is n/(2 log n). With the added restriction that every one of the m fundamental memories be recoverable exactly, m can be no more than n/(4 log n) asymptotically as n approaches infinity. Extensions are also considered, in particular to capacity under quantization of the outer-product connection matrix. This quantized memory-capacity problem is closely related to the capacity of the quantized Gaussian channel.

Mceliece, Robert J.↗

Optimal block cosine transform image coding for noisy channels

The two dimensional block transform coding scheme based on the discrete cosine transform was studied extensively for image coding applications. While this scheme has proven to be efficient in the absence of channel errors, its performance degrades rapidly over noisy channels. A method is presented for the joint source channel coding optimiaation of a scheme based on the 2-D block cosine transorm when the output of the encoder is to be transmitted via a memoryless design of the quantizers used for encoding the transform coefficients. This algorithm produces a set of locally optimum quantizers and the corresponding binary code assignment for the assumed transform coefficient statistics. To determine the optimum bit assignment among the transform coefficients, an algorithm was used based on the steepest descent method, which under certain convexity conditions on the performance of the channel optimized quantizers, yields the optimal bit allocation. Comprehensive simulation results for the performance of this locally optimum system over noise channels were obtained and appropriate comparisons against a reference system designed for no channel error were rendered.

Vaishampayan, Vinay A.↗

A neural net based architecture for the segmentation of mixed gray-level and binary pictures

A neural-net-based architecture is proposed to perform segmentation in real time for mixed gray-level and binary pictures. In this approach, the composite picture is divided into 16 x 16 pixel blocks, which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 x 16 neural net. For compression purposes, each image block is further divided into 4 x 4 subblocks; a one-bit nonparametric quantizer is used to encode 16 x 16 character and 4 x 4 image blocks; and the binary map and quantizer levels are obtained through a neural net segmentor over each block. The efficiency of the neural segmentation in terms of computational speed, data compression, and quality of the compressed picture is demonstrated. The effect of weight quantization is also discussed. VLSI implementations of such adaptive neural nets in CMOS technology are described and simulated in real time for a maximum block size of 256 pixels.

Tabatabai, Ali↗

Digital halftoning methods for selectively partitioning error into achromatic and chromatic channels

A method is described for reducing the visibility of artifacts arising in the display of quantized color images on CRT displays. The method is based on the differential spatial sensitivity of the human visual system to chromatic and achromatic modulations. Because the visual system has the highest spatial and temporal acuity for the luminance component of an image, a technique which will reduce luminance artifacts at the expense of introducing high-frequency chromatic errors is sought. A method based on controlling the correlations between the quantization errors in the individual phosphor images is explored. The luminance component is greatest when the phosphor errors are positively correlated, and is minimized when the phosphor errors are negatively correlated. The greatest effect of the correlation is obtained when the intensity quantization step sizes of the individual phosphors have equal luminances. For the ordered dither algorithm, a version of the method can be implemented by simply inverting the matrix of thresholds for one of the color components.

Mulligan, Jeffrey B.↗

Compressing subbanded image data with Lempel-Ziv-based coders

A method of improving the compression of image data using Lempel-Ziv-based coding is presented. Image data is first processed with a simple transform, such as the Walsh Hadamard Transform, to produce subbands. The subbanded data can be rounded to eight bits or it can be quantized for higher compression at the cost of some reduction in the quality of the reconstructed image. The data is then run-length coded to take advantage of the large runs of zeros produced by quantization. Compression results are presented and contrasted with a subband compression method using quantization followed by run-length coding and Huffman coding. The Lempel-Ziv-based coding in conjunction with run-length coding produces the best compression results at the same reconstruction quality (compared with the Huffman-based coding) on the image data used.

Glover, Daniel↗

Applications of wavelet-based compression to multidimensional Earth science data

A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

Bradley, Jonathan N.↗