QUANTUM COMPUTER

A BREAKTHROUGH IS COMING

"Quantum computers can keep up with the (true), complexity of nature." “Accurate modeling, has applications for engineering, medicine, energy production and much more”: Richard Feynman

From quantamagazine

Although a quantum computer (QC), prototype already works and is marketed  (D-Wave S.), installed in modules of 5 x 5m and heights of 7m., insulated with fiberglass from the environment, with 50 qbits inserted in silica receivers, protected by a near zero absolute cooling, with softwares adapted to electrons, with environmental insulation, to avoid interference and errors. What is missing ... What is left over? There is an excess of optimism, such as that of Geordie Rose, co-founder of D-Wave System, who has made some predictions: One) 2020, using a QC, the NASA will identify 40 light years from Earth, a planet with atmosphere and oceans of liquid water, similar to the Earth. Two) 2023, Experiments with QCs, will demonstrate the existence of parallel universes. Three) 2028: QCs will play a critical role in creating robots, which will match everything a human does. On the other hand, Google, Intel and IBM, also have prototypes of QCs, not commercialized of 45 qbits, although they hope to escalate  the number of their qbits to 1000 or more, in order to be able to predict climatic changes, perform data encryption (100%, safe), develop new materials, etc. What is missing? 1) Avoid decoherence (qbits collapse), induced by noise, temperature changes, vibrations. Given this fragility, the qbits, require temperatures close to absolute zero (-273 ^oC Celsius), to remain stable. 2) New software and interconnect technologies are needed to take advantage of the gigantic processing power of QCs. As it is known while in classical computers (CC), a bit (transistor), supposes 2 separate states (0 and 1), in a  QC, a qbit is 0 and 1 at the same time (superposition), a phenomenon that together with entanglement (combinations of qbits  superpositions), allow a QC, to calculate in hours the  complex modeling  of a molecule, which possibly would take years to a CC. 300 combinations of exponential superposition  of qbits would produce more overlapping states than existing atoms in the visible universe. 3) There are 6 to 7 different types of qubits, although only 3 or 4 are used. To obtain a superposition, 2 qubits are needed and for an entanglement more than 3. Some qbits need superconductors (where electrons flow without resistance), others: oscillating charges of ions kept fixed by lasers, also silica qbits with a single electron, controlled by microwaves. 4) Increase the number of qbits to 1 million. Continue working with QC simulators. Increase the number of math softwares. Improve the accuracy of qbits controller microwaves. 5) Given the enormous amounts of money to be invested, it is important to have a coordinated work strategy between universities, governments and industry. 6) Promote the growth of artificial intelligence (AI), through the use of QCs. 7) Establish the supremacy of   QCs over the CCs, solving problems that the latter cannot do. Establish new ways (faster, more efficient), to perform certain calculations, in order to facilitate modeling of the brain, of complex molecules, of factoring large numbers solving encryption problems, taking as an example the Shor's algorithm (factorization of large numbers), of great practical contributions. According to Scott Aronson (Texas University), QCs must solve certain problems, better than CCs. It is important that QCs can solve the random circuit sampling problem (take random efficient quantum circuits and generate samples for external distribution), build quantum circuits of at least a certain minimum size, with which CCs, do not count. 8) Reduce the error, especially now that the number of gates and qbits must be increased. The most crucial mistake is that which occurs every time a gate is used. Try that error for gates of qbits, be around: 0.1%. 9) How much is missing to have a universal quantum computer? Google that favor the supremacy of QCs, hopes to present one of them, at the end of 2019. Without ruling on the date: IBM, IonQ, Rigetti and Harvard University, say it will be very soon. Harvard University that   uses rubidium atoms and Microsof atoms that uses topological qbits, need more time. Since silicon qbits are fixed and need to be individually calibrated,they  will have a difficult task at  time of increasing the number of their qbits. Trapped ions give more time to make sure of their collapse, due to environmental noise. On the other hand, its gates are very slow allowing ions to move, when they are not needed. Some think that   qbits should adopt 2 bit qualities of CCs: be easily scalable and impeccable. 10) Anything else? According to Adam Bouland (California, University, Berkeley), after QCs solve the problem of random circuit sampling, there will be others, especially everyday tasks: financial services, AI, chemistry, creation of  QCs capable of self-eliminating their errors in real time, so that at  the end, they will  offer calculations without errors, which will require massive amounts of error-correcting qbits, connected to each logic gate.

DEEP MIND

IT IS THE STRUCTURE and the TYPE OF COMPUTER

Taken from Science.    Shimon Ullman.

In an updated paper about the current possibilities of creating a functional artificial brain similar to the human brain, the weaknesses and strengths of such efforts are exposed in Science. The analysis begins with the question: ¿Can  machines think?, self-answered by Alan Turing, in 1950, who alluded  to: a) A mathematical negative formulated by Kurt Gödel and Church, Kleene, Boater and Turing, referred to digital computers, which despite having an infinite capacity, such machines in front of certain things or questions would not respond or would do it wrongly. Penrose suggested (1980,1994), that certain human brain molecular structures could adopt a state of quantum superposition and entanglement (more so, if electrons transiting through human neural circuits do so in ionic forms), paving the way for the future use of artificial quantum computers in order to equal human brain performance. b) Returning to Turing, he closed his point of view   postulating by that time (1950), that for machines to start thinking like a human brain the important thing was to imitate the complex biological neural computational systems, having as a guide the human neural circuits, imitation that to the present time  has made possible the creation of neural circuits systems imitating the cerebral cortex (deep network), constructed with successive layers of elements similar to neurons connected by artificial synapses, producing speech recognitions, complex games, translation of texts, computer vision, classification and segmentation of objects, capture of images, where someone  try to produce a short verbal description of an image, answer visual questions and of human communication about the content of an image, or non-visual tasks: analyze humor and sarcasm, comprehension and intuitive aspects of social things, serve as assistants persons, in medical diagnosis, automatic car handling. Despite this, there are problems to be solved: I) To improve the adjustment of learning through synapses to produce desired output patterns, conditioned by training at the inputs. II) Achieve a learning with deep artificial neural networks that go beyond simple memorization producing logical outputs, not necessarily programmed during the learning process. III) In this perspective, highlights the incorporation into AI (artificial intelligence), associated to deep neural artificial networks of the called: learning reinforcement: LR (mapping of situations or actions to maximize the signal of reward or reinforcement, not to take certain actions, but to discover through trial, error and reward the best option in order to modify the behavior). LR models combined with AI algorithms, are currently applied to video games, Go and Chess, reaching in this last level of world champions with only 4 hours of training. IV) However, the most notable differences between the biological circuit and artificial neural networks systems are those based on structure: biological neurons are complex and diverse in morphology, physiology and neurochemistry. The entrances to excitatory pyramidal neurons are distributed over very complex dendritic branches. Cortical inhibitory neurons exhibit different functions, none of these heterogeneities being included in artificial neural networks.  Biological cerebral cortical circuits are more complex than models of artificial neural networks, including lateral connectivity between neurons, as well as local connections, more extensive connections and connections up and down in the hierarchical cortical regions. V) It is expected that artificial neural networks will promote a real human understanding, in order to address broad aspects of cognition and general artificial intelligence (IGA). Meanwhile, these techniques continue to be perfected under the guidance of neuroscience. VI) There are other functional differences between biological and artificial systems: A) AI's current artificial models rest heavily on the empirical side using simple and uniform artificial neural network structures employing large sets of training data for learning. Biological systems carry out tasks with limited training, learning about pre-existing network structures already encoded in circuits before learning, with which insects, fish and pigeons, perform complex navigation tasks using part of an elaborate set of innate mechanisms with sophisticated computational capabilities. B) Therefore, the development of complex cognitive and perceptual activities in children with little training, in the first months of their lives is possible, recognizing they complex instruments such as their hands, following people with their gazes  and distinguish visually if the characteristics of certain animals are dangerous or not, while developing an incipient understanding of physical or social interactions, through unsupervised learning, given the presence of innate cognitive systems generated by evolution, which facilitated the acquisition of significant concepts and skills. Recent models of visual learning in childhood, show that significant and complex concepts are not innate or learned by the child, but are proto-concepts that provide signals of internal teaching guiding the learning system along pathways that lead to a progressive acquisition and organization of complex concepts with little or no explicit training. Sometimes, a particular pattern of moving images provides an internal signal for the recognition of their hands that helps them to manipulate objects guiding the learning system in the direction of their gaze. Innate structures implemented in cortical regions with specified connectivity warn initially of specific input errors. Perhaps in the future, these pre-existing structures could be coupled to artificial neural models to simulate human learning. Imagine computational learning methods starting from proto-concepts with structures inserted in humans or robots that learn to quickly become familiar with unknown environments in an efficient and flexible way, very different from the current learning procedures. Summing up: I) Following Shimon Ullman, we believe that each machine or robot of the future that must possess a human-like brain should it be  virginally inserted a basic code -not to obey commands- but to complete what it should be (or do), before each new situation or environment, using analogies, logic or emerging thinking solutions. II) Today's hyper-super-computers have no future for this purpose because: a') They use artificial neuron-artificial neuron transmissions, using electrons that circulate through metallic means. a'') Human neurons send messages to other neurons using ions that are more adapted to quantum models (entanglements and others), allowing almost simultaneous transmissions in all possible planes, including feedback-type ones. Although current computers transmit information in several planes, they lack artificial-totalizing organizer neurons, which to be functional should be spherical or pyramidal. b') Although the human brain allows the circulation of 20% of total human body blood at every moment, it does not get very hot, because the membranes of its neurons are covered by a fat content resistor that allows them to capture electrons from the environment and adapt them to ions that allow the transmission of the message to millions of other neurons . b'') You have to copy this resistor model and include it in a quantum computer. c') The problem of reduced space of the artificial brain is solved in quantum computers using artificial neurons with spherical or pyramidal shapes capable (or almost) of dealing with thousands or millions of other artificial neurons.

QUANTUM COMPUTER

THE CHALLENGE: QUANTUM COMPUTERS

Academic researchers that try to build quantum computers, face enormous challenges. Some believe completing the construction of one of them could take 40 years, starting today. Difficulties: a) While a regular computer stores information using bits: 0s and 1s (for discernible movements, like: up-down, etc.), quantum computers would store information of millions of electrons with 0s and 1s, in superposition states (qubits), mobilized simultaneously in all directions, performing million of computations at the same time. b) Given the increasing exponential rate of calculations, many of these can be canceled by their exponential complexity, in order to protect the system. Interactions (intertwinning) of several qubits, can collapse an entire network of entangled qubits. c) Although the high speed and computational complexity prevent inserting in quantum computers, adequate control mechanisms, researchers   are gradually correcting software errors, especially in areas of entangled qubits, while the calculations are completed. d) At this time, most calculations are only possible at the end of quantum computing processes. e) Another form of control, is selecting the most frequent final answers, discarding the remaining as noise. f) Finally, exposure  of quantum computers  to the environment or human bodies -not accustomed to obey quantum laws-  not only deteriorates (quantum decoherence), calculations  of quantum computers, but induces quantum computers  to use again   regular binary calculations. Therefore, it is necessary to isolate quantum computers to operate and maintain their quantum coherence. Given these difficulties, some scholars are developing analogs of quantum computers with 16 digits base (qudits), assuming any number of states-d, for certain quantum calculations.  It is expected soon, that an analog quantum simulator can compute in seconds, quantum equations of motion of about 100 atoms. Using 16 digits base, it is possible to store 1  qudit in a single atom. With these achievements, it is expected to calculate the age of the universe in hours, the possible folds of particular proteins, predicting global climate change, etc. An analog simulator (2002) placed in the Max Planck Institute performed quantum equations of motion of atoms in superfluid and insulating systems and in cold trapped atoms.

EL DESAFIO: COMPUTADORAS CUÁNTICAS

La mayoría de investigadores académicos que intentan  construir computadoras cuánticas, enfrentan enormes  desafíos. Algunos creen que finalizar la construcción de una de ellas, podría demorar  40 años, a partir de hoy. Dificultades :  a) Mientras una computadora  regular almacena información empleando bits: 0s y 1s (para movimientos discernibles: arriba-abajo, etc.),  las computadoras cuánticas  almacenarían  información de millones de electrones en estados de superposición  :  0s y 1s a la vez (qubits), movilizándose  al mismo tiempo en todas  direcciones, realizando simultáneamente millones de computaciones.  b) Ante la  creciente velocidad exponencial de los cálculos, muchos de estos pueden ser cancelados por su propia complejidad exponencial, a fin de  proteger al sistema. Las interacciones (intertwinning),  de varios  qubits,  pueden colapsar  una red entera de  entangled qubits. c) Aunque la elevada   velocidad y complejidad de los cálculos, impiden  insertar en las computadoras cuánticas, adecuados mecanismos de control, poco a poco se  introducen  softwares de corrección de errores, especialmente en áreas de  entangled qubits,  mientras se   completan los cálculos. d) De momento, ciertos cálculos solo son posibles  al término de los procesos  de computación  cuántica. e) Otra forma de control es la selección de las respuestas finales   más frecuentes, descartando las restantes como ruido. f) Finalmente, el contacto con el medio ambiente o  cuerpos humanos  -no habituados a  obedecer leyes cuánticas- no solo deteriora (quantum decoherence), el  funcionamiento  de las  computadoras cuánticas, sino que induce a que estas  regresionen a  métodos de cálculos binarios regulares. Por ello, es  necesario aislar a las  computadoras cuánticas para que funcionen y mantengan su coherencia cuántica. Ante estas dificultades, algunos académicos desarrollan análogos de computadoras cuánticas con bases de 16 dígitos (qudits),  asumiéndose  cualquier número de estados-d, para realizar ciertos cálculos cuánticos.   Se espera  pronto, que un  simulador cuántico analógico pueda  computar en pocos segundos, ecuaciones cuánticas del movimiento de unos  100 átomos. Empleando  bases de  16 dígitos es posible almacenar 1 qudit, en un solo átomo. Con estos logros, se espera calcular  en horas la  edad del universo, los posibles pliegues de proteínas particulares, predecir  cambios climáticos globales, etc. Un simulador análogo (2002) del Instituto Max Planck,  ya  realiza ecuaciones cuánticas del movimiento de átomos en  superfluidos y aislantes y de  átomos atrapados en frio.