Early pioneers to reversible computation

Reversible computing is one of the most intensively developing research areas nowadays. We present a survey of less known or forgotten papers to show that a transfer of ideas between different disciplines is possible

Coding-theorem Like Behaviour and Emergence of the Universal Distribution from Resource-bounded Algorithmic Probability

Previously referred to as `miraculous' in the scientific literature because of its powerful properties and its wide application as optimal solution to the problem of induction/inference, (approximations to) Algorithmic Probability (AP) and the associated Universal Distribution are (or should be) of the greatest importance in science. Here we investigate the emergence, the rates of emergence and convergence, and the Coding-theorem like behaviour of AP in Turing-subuniversal models of computation. We investigate empirical distributions of computing models in the Chomsky hierarchy. We introduce measures of algorithmic probability and algorithmic complexity based upon resource-bounded computation, in contrast to previously thoroughly investigated distributions produced from the output distribution of Turing machines. This approach allows for numerical approximations to algorithmic (Kolmogorov-Chaitin) complexity-based estimations at each of the levels of a computational hierarchy. We demonstrate that all these estimations are correlated in rank and that they converge both in rank and values as a function of computational power, despite fundamental differences between computational models. In the context of natural processes that operate below the Turing universal level because of finite resources and physical degradation, the investigation of natural biases stemming from algorithmic rules may shed light on the distribution of outcomes. We show that up to 60\% of the simplicity/complexity bias in distributions produced even by the weakest of the computational models can be accounted for by Algorithmic Probability in its approximation to the Universal Distribution.Comment: 27 pages main text, 39 pages including supplement. Online complexity calculator: http://complexitycalculator.com

Scanning and Sequential Decision Making for Multi-Dimensional Data - Part I: the Noiseless Case

We investigate the problem of scanning and prediction ("scandiction", for short) of multidimensional data arrays. This problem arises in several aspects of image and video processing, such as predictive coding, for example, where an image is compressed by coding the error sequence resulting from scandicting it. Thus, it is natural to ask what is the optimal method to scan and predict a given image, what is the resulting minimum prediction loss, and whether there exist specific scandiction schemes which are universal in some sense. Specifically, we investigate the following problems: First, modeling the data array as a random field, we wish to examine whether there exists a scandiction scheme which is independent of the field's distribution, yet asymptotically achieves the same performance as if this distribution was known. This question is answered in the affirmative for the set of all spatially stationary random fields and under mild conditions on the loss function. We then discuss the scenario where a non-optimal scanning order is used, yet accompanied by an optimal predictor, and derive bounds on the excess loss compared to optimal scanning and prediction. This paper is the first part of a two-part paper on sequential decision making for multi-dimensional data. It deals with clean, noiseless data arrays. The second part deals with noisy data arrays, namely, with the case where the decision maker observes only a noisy version of the data, yet it is judged with respect to the original, clean data.Comment: 46 pages, 2 figures. Revised version: title changed, section 1 revised, section 3.1 added, a few minor/technical corrections mad

NASA Space Engineering Research Center for VLSI System Design

This annual report outlines the activities of the past year at the NASA SERC on VLSI Design. Highlights for this year include the following: a significant breakthrough was achieved in utilizing commercial IC foundries for producing flight electronics; the first two flight qualified chips were designed, fabricated, and tested and are now being delivered into NASA flight systems; and a new technology transfer mechanism has been established to transfer VLSI advances into NASA and commercial systems

Paper Session III-A - Data Compression and Error-Protection Coding

Data Compression and Error-protection coding are two of the now widely heard but not well understood terms associated with the Information Super Highway. But this was not always so. Their familiarity is a consequence of developments which were initiated nearly 50 years ago with the introduction of modern information theory by Claude Shannon. Both concepts and techniques which can dramatically improve the representation, storage and communication of digital data - the underlying component of modern information systems. Although often invisible to individual users, the commercial applications of compression and coding, which affect Our daily lives now, have become extremely broad. Few of these applications can claim they were not directly or indirectly influenced by prior investments in this technology by NASA and the military. This paper describes important specific ongoing NASA direct technology transfers of data compression and error-protection coding techniques/technology. First jointly used to improve the return of Voyager images from Uranus and Neptune by a factor of 4, these techniques and their NASA sponsored custom high-speed microcircuits are now independently enjoying widespread use. A simplified laymen\u27s description of these techniques and their performance characteristics is followed by a status on their technology transfer

Full-State Quantum Circuit Simulation by Using Data Compression

Quantum circuit simulations are critical for evaluating quantum algorithms and machines. However, the number of state amplitudes required for full simulation increases exponentially with the number of qubits. In this study, we leverage data compression to reduce memory requirements, trading computation time and fidelity for memory space. Specifically, we develop a hybrid solution by combining the lossless compression and our tailored lossy compression method with adaptive error bounds at each timestep of the simulation. Our approach optimizes for compression speed and makes sure that errors due to lossy compression are uncorrelated, an important property for comparing simulation output with physical machines. Experiments show that our approach reduces the memory requirement of simulating the 61-qubit Grover's search algorithm from 32 exabytes to 768 terabytes of memory on Argonne's Theta supercomputer using 4,096 nodes. The results suggest that our techniques can increase the simulation size by 2 to 16 qubits for general quantum circuits.Comment: Published in SC2019. Please cite the SC versio