New material for quantum computing discovered out of the blue

A common blue pigment used in the £5 note could have an important role to play in the development of a quantum computer, according to a paper published in the journal Nature.

Blue quantum

Phthalocyanine thin film on a flexible plastic substrate, showing the coexistence of long-lived “0” and “1” qubits on the copper spin. The molecules form a regular array together with the metal-free analogues, and the background represents the lattice fringes of the molecular crystals obtained by transmission electron microscopy.

The pigment, copper phthalocyanine (CuPc), which is similar to the light harvesting section of the chlorophyll molecule, is a low-cost organic semiconductor that is found in many household products. Crucially, it can be processed into a thin film that can be readily used for device fabrication, a significant advantage over similar materials that have been studied previously.

Now, researchers from the London Centre for Nanotechnology and the University of British Columbia have shown that the electrons in CuPc can remain in ‘superposition’ – an intrinsically quantum effect where the electron exists in two states at once – for surprisingly long times, showing this simple dye molecule has potential as a medium for quantum technologies.

The development of quantum computing requires precise control of tiny individual “qubits”, the quantum analogs of the classical binary bits, ‘0’ and ‘1’, which underpin all of our computation and communications technologies today. What distinguishes the “qubits” from classical bits is their ability to exist in superposition states.

The decay time of such superpositions tells us how useful a candidate qubit could be in quantum technologies. If this time is long, quantum data storage, manipulation and transmission become possible.

Our research shows that a common blue dye has more potential for quantum computing than many of the more exotic molecules that have been considered previously.

Dr Marc Warner

Lead author Marc Warner from the London Centre for Nanotechnology, said: “In theory, a quantum computer can easily solve problems that a normal, classical, computer would not be able to answer in the lifetime of the universe. We just don’t know how to build one yet.

“Our research shows that a common blue dye has more potential for quantum computing than many of the more exotic molecules that have been considered previously.”

CuPc possesses many other attributes that could exploit the spin of electrons, rather than their charge, to store and process information which are highly desirable in a more conventional quantum technology. For example, the pigment strongly absorbs visible light and is easy to modify chemically and physically, so its magnetic and electrical properties can be controlled.

Dr Warner added: “The properties of copper phthalocyanine make it of interest for the emerging field of quantum engineering, which seeks to exploit the quantum properties of matter to perform tasks like information processing or sensing more effectively than has ever been possible.”

Structure and morphology of phthalocyanine films. a) Structure of a metal phthalocyanine (MPc). b) Picture of a 2.5 cm 2 CuPc film deposited onto a 100 cm 2 Kapton sheet. c) Structure of PTCDA, used as a templating layer. Atomic force microscopy images of d) a 60 nm CuPc film deposited by OMBD at room temperature on Kapton, leading to a-phase crystallites; e) a b-polymorph obtained after annealing for 2 h at 320 °C; and f) a templated film deposited at room temperature onto a PTCDA first layer. Schematics of the unit cells of g) a-CuPc, h) b-CuPc, and i) templated CuPc, where f is the angle between the stacking axis and the molecular planes.


Structure and morphology of phthalocyanine films. a) Structure of a metal phthalocyanine (MPc). b) Picture of a 2.5 cm 2 CuPc film deposited onto a 100 cm 2 Kapton sheet. c) Structure of PTCDA, used as a templating layer. Atomic force microscopy images of d) a 60 nm CuPc film deposited by OMBD at room temperature on Kapton, leading to a-phase crystallites; e) a b-polymorph obtained after annealing for 2 h at 320 °C; and f) a templated film deposited at room temperature onto a PTCDA first layer. Schematics of the unit cells of g) a-CuPc, h) b-CuPc, and i) templated CuPc, where f is the angle between the stacking axis and the molecular planes.

sources:
https://www.ucl.ac.uk/news/2013/oct/new-material-quantum-computing-discovered-out-blue
https://www.researchgate.net/publication/1922170_Molecular_Thin_Films_a_New_Type_of_Magnetic_Switch
https://www.nature.com/articles/s41598-017-13271-w

Emergent dynamics of neuromorphic nanowire networks

The human brain is a product of evolution, tuned and reshaped by an ever-changing environment. The brain’s neuronal system is able to achieve the ability to recognize, conceptualize and memorize objects in the physical world. Using environmental information we establish logical associations that ultimately allows us not only to survive, but also to solve highly complex problems1.However, in an increasingly connected and interactive world, the volume of information to process has exponentially increased, and in order to extract and synthesize meaningful information, computerized approaches, such as machine learning and its various incarnations have gained tremendous popularity2.

Typically, Artificial Neural Networks (ANNs) attain this goal by a very delicate and case-selective combination of learning strategies3. Data containing complex or contextual associations between objects normally requires an heuristic sampling which limits their ability to synthesize information. Conventional CMOS architectures also restrains the amount of data that is efficiently processed with ANNs due to power consumption bottlenecks.
Interest in the creation of synthetic neurons that could increase the processing abilities of ANNs has increased considerably with the discovery of nanomaterials with memristive properties4. A memristive device is a non-linear two-terminal device in which the resistance shows resilience to change (i.e. memory), manifested in hysteretic behavior when the energy change is reversed or reduced, also termed as resistive switching. The memristor thus has two important neurosynapse-like properties, plasticity and retention. Traditional integrate-and-fire models, that emulate the electrical behavior of neurons using passive circuit elements, can be simulated exclusively with these elements5,6,7. Memristive devices have been successfully embedded into various CMOS architectures, enabling the realization of synthetic neural networks(SNN). SNNs imitate the topology of an ANN in a physical layout, typically stacking memristive terminals in cross-bar configurations8,9. Using voltage pulses to configure the internal state, or weight, of individual memristors; memorization, learning and classification abilities have been achieved10,11,12,13. However promising, this approach remains reliant upon CMOS technology and inherits some of its limitations: large cost-efficiency ratio, high power consumption, and subpar performance with respect to computerized ANNs …..

Figure 1

Morphological and structural properties of PVP-coated Ag nanowires and nanowire network. (a) Optical micrograph image of nanowire network layout after drop-cast deposition on a SiO2 substrate. (b) SEM image of nanowire interconnectivity in a selected area of the network. (c) HR-TEM image showing the atomic planes of the [100] facet of a Ag nanowire with the nanometric PVP layer embedded on the lateral surface of the nanowire. Figures (d,e) sketch the detail of the insulating junctions formed by the polymeric PVP layer between the Ag surfaces of overlapping nanowires. (f) Scheme of the measurement system. Two tungsten probes, separated by distance d = 500 μm, act as electrodes, contacting the nanowire network deposited on SiO2. The scale bars for figures (ac) are 100 μm, 10 μm and 2 nm, respectively.

Read full posthttps://www.nature.com/articles/s41598-019-51330-6

Preana: Game Theory Based Prediction with Reinforcement Learning.

In this article, we have developed a game theory based prediction tool, named Preana, based on a promising model developed by Professor Bruce Beuno de Mesquita. The first part of this work is dedicated to exploration of the specifics of Mesquita’s algorithm and reproduction of the factors and features that have not been revealed in literature. In addition, we have developed a learning mechanism to model the players’ reasoning ability when it comes to taking risks. Preana can pre-dict the outcome of any issue with multiple steak-holders who have conflicting interests in eco-nomic, business, and political sciences. We have utilized game theory, expected utility theory, Me-dian voter theory, probability distribution and reinforcement learning. We were able to repro-duce Mesquita’s reported results and have included two case studies from his publications and compared his results to that of Preana. We have also applied Preana on Irans 2013 presidential election to verify the accuracy of the prediction made by Preana.

Sources:
https://www.scirp.org/pdf/NS_2014082511264293.pdf
https://www.scirp.org/journal/paperinformation.aspx?paperid=49058
https://duckduckgo.com/?q=Bruce+Bueno+de+Mesquita&t=h_&ia=web

Biological learning curves outperform existing ones in artificial intelligence algorithms

Recently, deep learning algorithms have outperformed human experts in various tasks across several domains; however, their characteristics are distant from current knowledge of neuroscience. The simulation results of biological learning algorithms presented herein outperform state-of-the-art optimal learning curves in supervised learning of feedforward networks. The biological learning algorithms comprise asynchronous input signals with decaying input summation, weights adaptation, and multiple outputs for an input signal. In particular, the generalization error for such biological perceptrons decreases rapidly with increasing number of examples, and it is independent of the size of the input. This is achieved using either synaptic learning, or solely through dendritic adaptation with a mechanism of swinging between reflecting boundaries, without learning steps. The proposed biological learning algorithms outperform the optimal scaling of the learning curve in a traditional perceptron. It also results in a considerable robustness to disparity between weights of two networks with very similar outputs in biological supervised learning scenarios. The simulation results indicate the potency of neurobiological mechanisms and open opportunities for developing a superior class of deep learning algorithms.

figure1

read full article: https://www.nature.com/articles/s41598-019-48016-4