Quantum semi-supervised generative adversarial network for enhanced data classification

In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN). The system is composed of a quantum generator and a classical discriminator/classifier (D/C). The goal is to train both the generator and the D/C, so that the latter may get a high classification accuracy for a given dataset. Hence the qSGAN needs neither any data loading nor to generate a pure quantum state, implying that qSGAN is much easier to implement than many existing quantum algorithms. Also the generator can serve as a stronger adversary than a classical one thanks to its rich expressibility, and it is expected to be robust against noise. These advantages are demonstrated in a numerical simulation.

DIRECTION OF IMPROVEMENT OF THE WORKING BODIES OF HAMMER

Among the many types of grinding machines used in enterprises of grain processing, food and other industries, an important place belongs to hammer crushers. This is explained by the possibility of their use for grinding materials of various origins (plant, animal, mineral, as well as various wastes of food production), various particle size distribution (lumpy, briquetted, granular, fibrous), having different strength properties (hard, medium hard, soft, brittle, viscous), capable of being crushed once or requiring double sequential crushing. In addition, hammer crushers are characterized by relative simplicity of design, and most importantly - they destroy materials in the most rational way - by impact loading as a result of two successive strokes: a hammer on a particle and particles on the surface of the deck. The execution of these attacks at right angles creates the conditions for the intensification of the destruction process. However, the wear of the hammers leads to a rounding of their impact faces, which makes the angles of attack in the active zone of the hammers significantly different from the direct one. Under the influence of shock-frictional loads, their working surfaces quickly wear out, which leads to a decrease in productivity and crushing efficiency, to an increase in energy consumption, a disturbance in the balance of hammer rotors, an increase in the vibroacoustic activity of crushers, and other negative consequences The invention of the “Hammer of the crusher” provides for the use of ring-shaped multi-toothed hammers, the durability of which, when using traditional materials and conventional heat treatment, is increased by 4...5 times, eliminates the need for periodic rearrangement of hammers, stabilizes the operation of crushers. The possibility of self-sharpening ring-shaped multi-toothed hammers after reversing the hammer rotor simplifies the maintenance of crushers, eliminates personnel errors when replacing worn hammers. If multi-toothed hammers are subjected to liquid non-electrolysis boration and heat treatment using an optical quantum generator, then their durability can increase by more than ten times.

A simple quantum generator of random numbers

Cryptography techniques rely on chains of random numbers used to generate safe encryption keys. Since random number generator algorithms are in fact pseudo-random their behavior can be predicted if the generation method is known and as such they cannot be used for perfectly safe communications. In this article, we present a perfectly random generator based on quantum measurement processes. The main advantage of such a generator is that using quantum mechanics, its behavior cannot be predicted in any way. We verify the randomness of our generator and compare it to commonly used pseudo-random generators.

Laser influence to biosystems

In this paper a continous (cw) lasers in visible region were applied in order to study the influence of quantum generator to certain plants. The aim of such projects is to analyse biostimulation processes of living organizms which are linked to defined laser power density thresholds (exposition doses). The results of irradiation of corn and wheat seeds using He-Ne laser in the cw regime of 632.8nm, 50mW are presented and compared to results for other laser types. The dry and wet plant seeds were irradiated in defined time intervals and the germination period plant was monitored by days. Morphological data (stalk thickness, height, cob lenght) for chosen plants were monitored. From the recorded data, for the whole vegetative period, we performed appropriate statistical data processing. One part of experiments were the measurements of coefficient of reflection in visible range. Correlation estimations were calculated and discussed for our results. Main conclusion was that there were a significant increments in plant's height and also a cob lenght elongation for corn.

On learning finite-state quantum sources

We examine the complexity of learning the distributions produced by finite-state quantum sources. We show how prior techniques for learning hidden Markov models can be adapted to the {\em quantum generator} model to find that the analogous state of affairs holds: information-theoretically, a polynomial number of samples suffice to approximately identify the distribution, but computationally, the problem is as hard as learning parities with noise, a notorious open question in computational learning theory.