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venerdì 19 giugno 2009

Sunspots Revealed In Striking Detail By Supercomputers

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ScienceDaily (June 18, 2009) — In a breakthrough that will help scientists unlock mysteries of the Sun and its impacts on Earth, an international team of scientists led by the National Center for Atmospheric Research (NCAR) has created the first-ever comprehensive computer model of sunspots. The resulting visuals capture both scientific detail and remarkable beauty.
The high-resolution simulations of sunspot pairs open the way for researchers to learn more about the vast mysterious dark patches on the Sun's surface. Sunspots are associated with massive ejections of charged plasma that can cause geomagnetic storms and disrupt communications and navigational systems. They also contribute to variations in overall solar output, which can affect weather on Earth and exert a subtle influence on climate patterns.
The research, by scientists at NCAR and the Max Planck Institute for Solar System Research (MPS) in Germany, is being published June 18 in Science Express.
"This is the first time we have a model of an entire sunspot," says lead author Matthias Rempel, a scientist at NCAR's High Altitude Observatory. "If you want to understand all the drivers of Earth's atmospheric system, you have to understand how sunspots emerge and evolve. Our simulations will advance research into the inner workings of the Sun as well as connections between solar output and Earth's atmosphere."
Ever since outward flows from the center of sunspots were discovered 100 years ago, scientists have worked toward explaining the complex structure of sunspots, whose number peaks and wanes during the 11-year solar cycle. Sunspots encompass intense magnetic activity that is associated with solar flares and massive ejections of plasma that can buffet Earth's atmosphere. The resulting damage to power grids, satellites, and other sensitive technological systems takes an economic toll on a rising number of industries.
Creating such detailed simulations would not have been possible even as recently as a few years ago, before the latest generation of supercomputers and a growing array of instruments to observe the Sun. The model enables scientists to capture the convective flow and movement of energy that underlie the sunspots, which is not directly detectable by instruments.
The work was supported by the National Science Foundation, NCAR's sponsor. The research team improved a computer model, developed at MPS, that built upon numerical codes for magnetized fluids that had been created at the University of Chicago.
Computer model provides a unified physical explanation
The new simulations capture pairs of sunspots with opposite polarity. In striking detail, they reveal the dark central region, or umbra, with brighter umbral dots, as well as webs of elongated narrow filaments with flows of mass streaming away from the spots in the outer penumbral regions.
The model suggests that the magnetic fields within sunspots need to be inclined in certain directions in order to create such complex structures. The authors conclude that there is a unified physical explanation for the structure of sunspots in umbra and penumbra that is the consequence of convection in a magnetic field with varying properties.
The simulations can help scientists decipher the mysterious, subsurface forces in the Sun that cause sunspots. Such work may lead to an improved understanding of variations in solar output and their impacts on Earth.
Supercomputing at 76 trillion calculations per second
To create the model, the research team designed a virtual, three-dimensional domain that simulates an area on the Sun measuring about 31,000 miles by 62,000 miles and about 3,700 miles in depth - an expanse as long as eight times Earth's diameter and as deep as Earth's radius. The scientists then used a series of equations involving fundamental physical laws of energy transfer, fluid dynamics, magnetic induction and feedback, and other phenomena to simulate sunspot dynamics at 1.8 billion points within the virtual expanse, each spaced about 10 to 20 miles apart. For weeks, they solved the equations on NCAR's new bluefire supercomputer, an IBM machine that can perform 76 trillion calculations per second.
The work drew on increasingly detailed observations from a network of ground- and space-based instruments to verify that the model captured sunspots realistically.
The new model is far more detailed and realistic than previous simulations that failed to capture the complexities of the outer penumbral region. The researchers noted, however, that even their new model does not accurately capture the lengths of the filaments in parts of the penumbra. They can refine the model by placing the grid points even closer together, but that would require more computing power than is currently available.
"Advances in supercomputing power are enabling us to close in on some of the most fundamental processes of the Sun," says Michael Knoelker, director of NCAR's High Altitude Observatory and a co-author of the paper. "With this breakthrough simulation, an overall comprehensive physical picture is emerging for everything that observers have associated with the appearance, formation, dynamics, and the decay of sunspots on the Sun's surface."
The University Corporation for Atmospheric Research manages the National Center for Atmospheric Research under sponsorship by the National Science Foundation.
Adapted from materials provided by National Center for Atmospheric Research/University Corporation for Atmospheric Research.

Human Eye Inspires Advance In Computer Vision From Boston College Researchers


ScienceDaily (June 18, 2009) — Inspired by the behavior of the human eye, Boston College computer scientists have developed a technique that lets computers see objects as fleeting as a butterfly or tropical fish with nearly double the accuracy and 10 times the speed of earlier methods.
The linear solution to one of the most vexing challenges to advancing computer vision has direct applications in the fields of action and object recognition, surveillance, wide-base stereo microscopy and three-dimensional shape reconstruction, according to the researchers, who will report on their advance at the upcoming annual IEEE meeting on computer vision.
BC computer scientists Hao Jiang and Stella X. Yu developed a novel solution of linear algorithms to streamline the computer's work. Previously, computer visualization relied on software that captured the live image then hunted through millions of possible object configurations to find a match. Further compounding the challenge, even more images needed to be searched as objects moved, altering scale and orientation.
Rather than combing through the image bank – a time- and memory-consuming computing task – Jiang and Yu turned to the mechanics of the human eye to give computers better vision.
"When the human eye searches for an object it looks globally for the rough location, size and orientation of the object. Then it zeros in on the details," said Jiang, an assistant professor of computer science. "Our method behaves in a similar fashion, using a linear approximation to explore the search space globally and quickly; then it works to identify the moving object by frequently updating trust search regions."
Trust search regions act as visual touchstones the computer returns to again and again. Jiang and Yu's solution focuses on the mathematically-generated template of an image, which looks like a constellation when lines are drawn to connect the stars. Using the researchers' new algorithms, computer software identifies an object using the template of a trust search region. The program then adjusts the trust search regions as the object moves and finds its mathematical matches, relaying that shifting image to a memory bank or a computer screen to record or display the object.
Jiang says using linear approximation in a sequence of trust regions enables the new program to maintain spatial consistency as an object moves and reduces the number of variables that need to be optimized from several million to just a few hundred. That increased the speed of image matching 10 times over compared with previous methods, he said.
The researchers tested the software on a variety of images and videos – from a butterfly to a stuffed Teddy Bear – and report achieving a 95 percent detection rate at a fraction of the complexity. Previous so-called "greedy" methods of search and match achieved a detection rate of approximately 50 percent, Jiang said.
Jiang will present the team's findings at the IEEE Conference on Computer Vision and Pattern Recognition 2009, which takes place June 20-25 in Miami.
Adapted from materials provided by Boston College, via EurekAlert!, a service of AAAS.

Hybrid System Of Human-Machine Interaction Created


ScienceDaily (June 17, 2009) — Scientists at FAU have created a "hybrid" system to examine real-time interactions between humans and machines (virtual partners). By pitting human against machine, they open up the possibility of exploring and understanding a wide variety of interactions between minds and machines, and establishing the first step toward a much friendlier union of man and machine, and perhaps even creating a different kind of machine altogether.
For more than 25 years, scientists in the Center for Complex Systems and Brain Sciences (CCSBS) in Florida Atlantic University’s Charles E. Schmidt College of Science, and others around the world, have been trying to decipher the laws of coordinated behavior called “coordination dynamics”.
Unlike the laws of motion of physical bodies, the equations of coordination dynamics describe how the coordination states of a system evolve over time, as observed through special quantities called collective variables. These collective variables typically span the interaction of organism and environment. Imagine a machine whose behavior is based on the very equations that are supposed to govern human coordination. Then imagine a human interacting with such a machine whereby the human can modify the behavior of the machine and the machine can modify the behavior of the human.
In a groundbreaking study published in the June 3 issue of PLoS One and titled “Virtual Partner Interaction (VPI): exploring novel behaviors via coordination dynamics,” an interdisciplinary group of scientists in the CCSBS created VPI, a hybrid system of a human interacting with a machine. These scientists placed the equations of human coordination dynamics into the machine and studied real-time interactions between the human and virtual partners. Their findings open up the possibility of exploring and understanding a wide variety of interactions between minds and machines. VPI may be the first step toward establishing a much friendlier union of man and machine, and perhaps even creating a different kind of machine altogether.
“With VPI, a human and a ‘virtual partner’ are reciprocally coupled in real-time,” said Dr. J. A. Scott Kelso, the Glenwood and Martha Creech Eminent Scholar in Science at FAU and the lead author of the study. “The human acquires information about his partner’s behavior through perception, and the virtual partner continuously detects the human’s behavior through the input of sensors. Our approach is analogous to the dynamic clamp used to study the dynamics of interactions between neurons, but now scaled up to the level of behaving humans.”
In this first ever study of VPI, machine and human behaviors were chosen to be quite simple. Both partners were tasked to coordinate finger movements with one another. The human executed the task with the intention of performing in-phase coordination with the machine, thereby trying to synchronize his/her flexion and extension movements with those of the virtual partner’s.
The machine, on the other hand, executed the task with the competing goal of performing anti-phase coordination with the human, thereby trying to extend its finger when the human flexed and vice versa. Pitting machine against human through opposing task demands was a way the scientists chose to enhance the formation of emergent behavior, and also allowed them to examine each partner’s individual contribution to the coupled behavior. An intriguing outcome of the experiments was that human subjects ascribed intentions to the machine, reporting that it was “messing” with them.
“The symmetry between the human and the machine, and the fact that they carry the same laws of coordination dynamics, is a key to this novel scientific framework,” said co-author Dr. Gonzalo de Guzman, a physicist and research associate professor at the FAU center. “The design of the virtual partner mirrors the equations of motion of the human neurobehavioral system. The laws obtained from accumulated studies describe how the parts of the human body and brain self-organize, and address the issue of self-reference, a condition leading to complexity.”
One ready application of VPI is the study of the dynamics of complex brain processes such as those involved in social behavior. The extended parameter range opens up the possibility of systematically driving functional process of the brain (neuromarkers) to better understand their roles. The scientists in this study anticipate that just as many human skills are acquired by observing other human beings; human and machine will learn novel patterns of behavior by interacting with each other.
“Interactions with ever proliferating technological devices often place high skill demands on users who have little time to develop these skills,” said Kelso. “The opportunity presented through VPI is that equally useful and informative new behaviors may be uncovered despite the built-in asymmetry of the human-machine interaction.”
While stable and intermittent coordination behaviors emerged that had previously been observed in ordinary human social interactions, the scientists also discovered novel behaviors or strategies that have never previously been observed in human social behavior. The emergence of such novel behaviors demonstrates the scientific potential of the VPI human-machine framework.
Modifying the dynamics of the virtual partner with the purpose of inducing a desired human behavior, such as learning a new skill or as a tool for therapy and rehabilitation, are among several applications of VPI.
“The integration of complexity in to the behavioral and neural sciences has just begun,” said Dr. Emmanuelle Tognoli, research assistant professor in FAU’s CCSBS and co-author of the study. “VPI is a move away from simple protocols in which systems are ‘poked’ by virtue of ‘stimuli’ to understanding more complex, reciprocally connected systems where meaningful interactions occur.”
Research for this study was supported by the National Science Foundation program “Human and Social Dynamics,” the National Institute of Mental Health’s “Innovations Award,” “Basic and Translational Research Opportunities in the Social Neuroscience of Mental Health,” and the Office of Naval Research Code 30. Kelso’s research is also supported by the Pierre de Fermat Chaire d’Excellence and Tognoli’s research is supported by the Davimos Family Endowment for Excellence in Science.
Adapted from materials provided by Florida Atlantic University, via Newswise.

venerdì 5 giugno 2009

Endless Original Music: Computer Program Creates Music Based On Emotions


ScienceDaily (June 2, 2009) — A group of researchers from the University of Granada (UGR) has developed Inmamusys, a software program that can create music in response to emotions that arise in the listener. By using artificial intelligence (AI) techniques, the program enables original, copyright-free and emotion-inspiring music to be played continuously.
UGR researchers Miguel Delgado, Waldo Fajardo and Miguel Molina decided to design a software program that would enable a person who knew nothing about composition to create music. The system they devised, using AI, is called Inmamusys, an acronym for Intelligent Multiagent Music System, and is able to compose and play music in real time.
If successful, this prototype, which has been described recently in the journal Expert Systems with Applications, looks likely to bring about great changes in terms of the intrusive and repetitive canned music played in public places.
Miguel Molina, lead author of the study, says that while the repertoire of such canned music is very limited, the new invention can be used to create a pleasant, non-repetitive musical environment for anyone who has to be within earshot throughout the day.
Everyone's ears have suffered the effects of repetitively-played canned music, be it in workplaces, hospital environments or during phone calls made to directory inquiries numbers. On this basis, the research team decided that it would be "very interesting to design and build an intelligent system able to generate music automatically, ensuring the correct degree of emotiveness (in order to manage the environment created) and originality (guaranteeing that the tunes composed are not repeated, and are original and endless)."
Inmamusys has the necessary knowledge to compose emotive music through the use of AI techniques. In designing and developing the system, the researchers worked on the abstract representation of the concepts necessary to deal with emotions and feelings. To achieve this, Molina says, "we designed a modular system that includes, among other things, a two-level multiagent architecture."
A survey was used to evaluate the system, with the results showing that users are able to identify the type of music composed by the computer. A person with no musical knowledge whatsoever can use this artificial musical composer, because the user need do nothing more than decide on the type of music."
Beneath the system's ease of use, Miguel Molina reveals that a complex framework is at work to allow the computer to imitate a feature as human as creativity. Aside from creativity, music also requires specific knowledge.
According to Molina, this "is usually something done by human beings, although they do not understand how they do it. In reality, there are numerous processes involved in the creation of music and, unfortunately, we still do not understand many of them. Others are so complex that we cannot analyse them, despite the enormous power of current computing tools. Nowadays, thanks to the advances made in computer sciences, there are areas of research -- such as artificial intelligence -- that seek to reproduce human behaviour. One of the most difficult facets of all to reproduce is creativity."
Farewell to copyright payments
Commercial development of this prototype will not only change the way in which research is carried out into the relationship between computers and emotions, the means of interacting with music and structures by which music is composed in the future. It will also serve, say the study's authors, to reduce costs.
According to the researchers, "music is highly present in our leisure and working environments, and a large number of the places we visit have canned music systems. Playing these pieces of music involves copyright payments. Our system will make these music copyright payments a thing of the past."
Journal reference:
Miguel Delgado; Waldo Fajardo; Miguel Molina-Solana. Inmamusys: Intelligent multiagent music system. Expert Systems with Applications, 2009; 36 (3): 4574 DOI: 10.1016/j.eswa.2008.05.028
Adapted from materials provided by Plataforma SINC, via AlphaGalileo.

Computer Graphics Researchers Simulate The Sounds Of Water And Other Liquids

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ScienceDaily (June 4, 2009) — Splash, splatter, babble, sploosh, drip, drop, bloop and ploop!
Those are some of the sounds that have been missing from computer graphic simulations of water and other fluids, according to researchers in Cornell's Department of Computer Science, who have come up with new algorithms to simulate such sounds to go with the images.
The work by Doug James, associate professor of computer science, and graduate student Changxi Zheng will be reported at the 2009 ACM SIGGRAPH conference Aug. 3-7 in New Orleans. It is the first step in a broader research program on sound synthesis supported by a $1.2 million grant from the Human Centered Computing Program of the National Science Foundation (NSF) to James, assistant professor Kavita Bala and associate professor Steve Marschner.
In computer-animated movies, sound can be added after the fact from recordings or by Foley artists. But as virtual worlds grow increasingly interactive and immersive, the researchers point out, sounds will need to be generated automatically to fit events that can't be predicted in advance. Recordings can be cued in, but can be repetitive and not always well matched to what's happening.
"We have no way to efficiently compute the sounds of water splashing, paper crumpling, hands clapping, wind in trees or a wine glass dropped onto the floor," the researchers said in their research proposal.
Along with fluid sounds, the research also will simulate sounds made by objects in contact, like a bin of Legos; the noisy vibrations of thin shells, like trash cans or cymbals; and the sounds of brittle fracture, like breaking glass and the clattering of the resulting debris.
All the simulations will be based on the physics of the objects being simulated in computer graphics, calculating how those objects would vibrate if they actually existed, and how those vibrations would produce acoustic waves in the air. Physics-based simulations also can be used in design, just as visual simulation is now, James said. "You can tell what it's going to sound like before you build it," he explained, noting that a lot of effort often goes into making things quieter.
In their SIGGRAPH paper, Zheng and James report that most of the sounds of water are created by tiny air bubbles that form as water pours and splashes. Moving water traps air bubbles on the scale of a millimeter or so. Surface tension contracts the bubbles, compressing the air inside until it pushes back and expands the bubble. The repeated expansion and contraction over milliseconds generates vibrations in the water that eventually make its surface vibrate, acting like a loudspeaker to create sound waves in the air.
The simulation method developed by the Cornell researchers starts with the geometry of the scene, figures out where the bubbles would be and how they're moving, computes the expected vibrations and finally the sounds they would produce. The simulation is done on a highly parallel computer, with each processor computing the effects of multiple bubbles. The researchers have fine-tuned the results by comparing their simulations with real water sounds.
Demonstration videos of simulations of falling, pouring, splashing and babbling water are available at http://www.cs.cornell.edu/projects/HarmonicFluids.
The current methods still require hours of offline computing time, and work best on compact sound sources, the researchers noted, but they said further development should make possible the real-time performance needed for interactive virtual environments and deal with larger sound sources such as swimming pools or perhaps even Niagara Falls. They also plan to approach the more complex collections of bubbles in foam or plumes.
The research reported in the SIGGRAPH paper was supported in part by an NSF Faculty Early Career Award to James, and by the Alfred P. Sloan Foundation, Pixar, Intel and Autodesk.
Adapted from materials provided by Cornell University. Original article written by Bill Steele.

venerdì 15 maggio 2009

Is a room-temperature, solid-state quantum computer mere fantasy?

Marshall StonehamLondon Centre for Nanotechnology and Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK
Published April 27, 2009
Creating a practical solid-state quantum computer is seriously hard. Getting such a computer to operate at room temperature is even more challenging. Is such a quantum computer possible at all? If so, which schemes might have a chance of success?

In his 2008 Newton Medal talk, Anton Zeilinger of the University of Vienna said: “We have to find ways to build quantum computers in the solid state at room temperature—that’s the challenge.” [1] This challenge spawns further challenges: Why do we need a quantum computer anyway? What would constitute a quantum computer? Why does the solid state seem essential? And would a cooled system, perhaps with thermoelectric cooling, be good enough?
Some will say the answer is obvious. But these answers vary from “It’s been done already” to “It can’t be done at all.” Some of the “not at all” group believe high temperatures just don’t agree with quantum mechanics. Others recognize that their favored systems cannot work at room temperature. Some scientists doubt that serious quantum computing is possible anyway. Are there methods that might just be able to meet Zeilinger’s challenge?
The questions that challenge
What is a computer? Standard classical computers use bits for encoding numbers, and the bits are manipulated by the classical gates that can execute AND and OR operations, for example. A classical bit has a value of 0 or 1, according to whether some small subunit is electrically charged or uncharged. Other forms are possible: the bits for a classical spintronic computer might be spins along or opposite to a magnetic field. Even the most modest computers on sale today incorporate complex networks of a few types of gates to control huge numbers of bits. If there are so few bits that you can count them on your fingers, it can’t seriously be considered a computer.
What do we mean by quantum? Being sure a phenomenon is “quantum” isn’t simple. Quantum ideas aren’t intuitive yet. Could you convince your banker that quantum physics could improve her bank’s security? Perhaps three questions identify the issues. First, how do you describe the state of a system? The usual descriptors, wave functions and density matrices, underlie wavelike interference and entanglement. Entanglement describes the correlations between local measurements on two particles, which I call their “quantum dance.” Entanglement is the resource that could make quantum computing worthwhile. The enemy of entanglement is decoherence, just as friction is the enemy of mechanical computers. Second, how does this quantum state change if it is not observed? It evolves deterministically, described by the Schrödinger equation. The probabilistic results of measurements emerge when one asks the third question: how to describe observations and their effects. Measurement modifies entanglement, often destroying it, as it singles out a specific state. This is one way that you can tell if an eavesdropper intercepted your message in a quantum communications system.
Proposed quantum computers have qubits manipulated by a few types of quantum gates, in a complex network. But the parallels are not complete [2]. Each classical bit has a definite value, it can only be 0 or 1, it can be copied without changing its value, it can be read without changing its value and, when left alone, its value will not change significantly. Reading one classical bit does not affect other (unread) bits. You must run the computer to compute the result of a computation. Every one of those statements is false for qubits, even that last statement. There is a further difference. For a classical computer, the process is Load → Run → Read, whereas for a quantum computer, the steps are Prepare → Evolve → Measure, or, as in one case discussed later, merely Prepare → Measure.
Why do we need a quantum computer? The major reasons stem from challenges to mainstream silicon technology. Markets demand enhanced power efficiency, miniaturization, and speed. These enhancements have their limits. Future technology scenarios developed for the semiconductor industry’s own roadmap [3] imply that the number of electrons needed to switch a transistor should fall to just 1 (one single electron) before 2020. Should we follow this innovative yet incremental roadmap, and trust to new tricks, or should we seek a radical technology, with wholly novel quantum components operating alongside existing silicon and photonic technologies? Any device with nanoscale features inevitably displays some types of quantum behavior, so why not make a virtue of necessity and exploit quantum ideas? Quantum-based ideas may offer a major opportunity, just as the atom gave the chemical industry in the 19th century, and the electron gave microelectronics in the 20th century. Quantum sciences could transform 21st century technologies.
Why choose the solid state for quantum computing? Quantum devices nearly always mean nanoscale devices, ultimately because useful electronic wave functions are fairly compact [4]. Complex devices with controlled features at this scale need the incredible know-how we have acquired with silicon technology. Moreover, quantum computers will be operated by familiar silicon technology. Operation will be easier if classical controls can be integrated with the quantum device, and easiest if the quantum device is silicon compatible. And scaling up, the linking of many basic and extremely small units is a routine demand for silicon devices. With silicon technologies, there are also good ways to link electronics and photonics. So an ideal quantum device would not just meet quantum performance criteria, but would be based on silicon; it would use off-the-shelf techniques (even sophisticated ones) suitable for a near-future generation fabrication plant. A cloud on the horizon concerns decoherence: can entanglement be sustained long enough in a large enough system for a useful quantum calculation?
All the objections
It has been done already? Some beautiful work demonstrating critical steps, including initializing a spin system and transfer of quantum information, has been done at room temperature with nitrogen-vacancy (NV-) centers in diamond [5]. Very few qubits were involved, and scaling up to a useful computer seems unlikely without new ideas. But the combination of photons—intrinsically insensitive to temperature—with defects or dopants with long decoherence times leaves hope.
It can’t be done: serious quantum computing simply isn’t possible anyway. Could any quantum computer work at all? Is it credible that we can build a system big enough to be useful, yet one that isn’t defeated by loss of entanglement or degraded quantum coherence? Certainly there are doubters, who note how friction defeated 19th century mechanical computers. Others have given believable arguments that computing based on entanglement is possible [6]. Of course, it may prove that some hybrid, a sort of quantum-assisted classical computing, will prove the crucial step.
It can’t be done: quantum behavior disappears at higher temperatures. Confusion can arise because quantum phenomena show up in two ways. In quantum statistics, the quantal ħ appears as ħω/kT. When statistics matter most, near equilibrium, high temperatures T oppose the quantum effects of ħ. However, in quantum dynamics, ħ can appear unassociated with T, opening new channels of behavior. Quantum information processing relies on staying away from equilibrium, so the rates of many individual processes compete in complex ways: dynamics dominate. Whatever the practical problems, there is no intrinsic problem with quantum computing at high temperatures.
It can’t be done: the right qubits don’t exist. True, some qubits are not available at room temperature. These include superconducting qubits and those based on Bose-Einstein condensates. In Kane’s seminal approach [7], the high polarizability needed for phosphorus-doped silicon (Si:P) corresponds to a low ionization donor energy, so the qubits disappear (or decohere) at room temperature. In what follows, I shall look at methods without such problems.
What needs to be done: Implementing quantum computing
David DiVincenzo at IBM Research Labs devised a checklist [8] that conveniently defines minimal (but seriously challenging) needs for a credible quantum computer. There must be a well-defined set of quantum states, such as electron spin states, to use as qubits. One needs scalability, so that enough qubits (let’s say 20, though 200 would be better) linked by entanglement are available to make a serious quantum computer. Operation demands a means to initialize and prepare suitable pure quantum states, a means to manipulate qubits to carry out a desired quantum evolution, and means to read out the results. Decoherence must be slow enough to allow these operations.
What does this checklist imply for solid-state quantum computing? Are there solid-state systems with decoherence mechanisms, key energies, and qubit control systems that might work at useful temperatures, ideally room temperature? Solid-state technologies have good prospects for scalability. There is a good chance that there are ingenious ways to link the many qubits and quantum gates needed for almost any serious application. However, decoherence might be fast. This may be less of a problem than imagined, for fast operating speeds go hand in hand with fast decoherence. Fast processing needs strong interactions, and such strong interactions will usually cause decoherence [9].
For spin-based solid-state quantum computing, most routes to initialization group into four categories. First, there are optical methods (including microwaves), based on selection rules, such as those used for NV- experiments. Then there are spintronic approaches, using a source (perhaps a ferromagnet) of spin-polarized electrons or excitons. (Note that spins have been transferred over distances of nearly a micron at room temperature [10].) Then there are brute force methods aiming for thermal equilibrium in a very large magnetic field, where the ratio of Zeeman splitting to thermal energy kBT is large. And finally there are tricks involving extra qubits that are not used in calculations. Of these methods, the optical and spintronic concepts seem most promising for room-temperature operation.
For readout, there are two broad strategies. Most ideas for spin-based quantum information processing aim at the sequential readout of individual spins. However, there are other less-developed ideas in which the ensemble of relevant spins is looked at together, as in some neutron scattering studies of antiferromagnetic crystals. What methods are there for probing single spins, if the sequential strategy is chosen? First, there is direct frequency discrimination, including the use of Zeeman splitting, of hyperfine structure, and so on. Ideas from atom trap experiments suggest that one can continue to interrogate a spin with a sequence of photons that do not change the qubit [11]. Such methods might work at room temperature, at least if the relevant spectral lines remain sharp enough. Second, there are many ways to exploit spin-dependent rates of carrier scatter or trapping. One might examine how mobile polarized spins are scattered by a fixed spin that is to be measured. Or the spin of a mobile carrier might be measured by its propensity for capture or scatter by fixed spin, or by some combination of polarized mobile spins and interferometry. At room temperature, the problem is practice rather than principle, and acceptable methods seem possible. A third way is to use relative tunnel rates, where one spin state can be blocked. Tunneling-based methods can become very hard at higher temperatures. There are then various ideas, all of which seem to be both tricky and relatively slow, but I may be being pessimistic. These include the use of circularly polarized light and magneto-optics, the direct detection of spin resonance with a scanning tunneling microscope, the exploitation of large spin-orbit coupling, or the direct measurement of a force with a scanning probe having a magnetic tip.
For the manipulations during operation, probably the most important ways use electromagnetic radiation, whether optical, microwave or radio frequency. Other controls, such as ultrasonics or surface acoustic waves, are less flexible. Electromagnetic methods might well operate at room temperature. Other suggestions invoke nanoscale electrodes. I do not know of any that look both credible and scalable.
Hopes for higher temperature operation
In what follows, I shall concentrate on two proposals as examples, with apologies to those whose suggestions I am omitting. Both of the proposals use optical methods to control spins, but do so in wholly different ways. The first is a scheme for optically controlled spintronics that I, Andrew Fisher, and Thornton Greenland proposed [11, 12]. The second route exploits entanglement of states of distant atoms by interference [13] in the context of measurement-based quantum computing [14]. A broader discussion of the materials needed is given in Ref. [15].
Optically controlled spintronics [11, 12]. Think of a thin film of silicon, perhaps 10 nm thick, isotopically pure to avoid nuclear spins, on top of an oxide substrate (Fig. 1). The simple architecture described is essentially two dimensional. Now imagine the film randomly doped with two species of deep donor—one species as qubits, the other to control the qubits. In their ground states, these species should have negligible interactions. When a control donor is excited, the electron’s wave function spreads out more, and its overlap with two of the qubit donors will create an entangling interaction between those two qubits (Fig. 2). Shaped pulses of optical excitation of chosen control donors guide the quantum dance (entanglement) of chosen qubit donors [16].
For controlling entanglement in this way, typical donor spacings in silicon must be of the order of tens of nanometers. Optically, one can only address regions of the order of a wavelength across, say 1000 nm. The limit of optical spatial resolution is a factor 100 larger than donor spacings needed for entanglement. How can one address chosen pairs of qubits? The smallest area on which we can focus light contains many spins. The answer is to exploit the randomness inevitable in standard fabrication and doping. Within a given patch of the film a wavelength across, the optical absorptions will be inhomogeneously broadened from dopant randomness. Even the steps at the silicon interfaces are helpful because the film thickness variations shift transition energies from one dopant site to another. Light of different wavelengths will excite different control donors in this patch, and so manipulate the entanglements of different qubits. Reasonable assumptions suggest one might make use of perhaps 20 gates or so per patch. Controlled links among 20 qubits would be very good by present standards, though further scale up—the linking of patches—would be needed for a serious computer (Fig. 3). The optically controlled spintronics strategy [11, 12] separates the two roles: qubit spins store quantum information, and controls manipulate quantum information. These roles require different figures of merit.
To operate at room temperature, qubits must stay in their ground states, and their decoherence—loss of quantum information—must be slow enough. Shallow donors like Si:P or Si:Bi thermally ionize too readily for room-temperature operations, though one could demonstrate principles at low temperatures with these materials. Double donors like Si:Mg+ or Si:Se+ have ionization energies of about half the silicon band gap and might be deep enough. Most defects in diamond are stable at room temperature, including substitutional N in diamond and the NV- center on which so many experiments have been done.
What about decoherence? First, whatever enables entanglement also causes decoherence. This is why fast switching means fast decoherence, and slow decoherence implies slow switching. Optical control involves manipulation of the qubits by stimulated absorption and emission in controlled optical excitation sequences, so spontaneous emission will cause decoherence. For shallow donors, like Si:P, the excitation energy is less than the maximum silicon phonon energy; even at low temperatures, one-phonon emission causes rapid decoherence. Second, spin-lattice relaxation in qubit ground states destroys quantum information. Large spin-orbit coupling is bad news, so avoiding high atomic number species helps. Spin lattice relaxation data at room temperature are not yet available for those Si donors (like Si:Se+) where one-phonon processes are eliminated because their first excited state lies more than the maximum phonon energy above the ground state. In diamond at room temperature, the spin-lattice relaxation time for substitutional nitrogen is very good (~1 ms) and a number of other centers have times ~0.1 ms. Third, excited state processes can be problems, and two-photon ionization puts constraints on wavelengths and optical intensities. Fourth, the qubits could lose quantum information to the control atoms. This can be sorted out by choosing the right form of excitation pulses [16]. Fifth, interactions with other spins, including nuclear spins, set limits, but there are helpful strategies, like using isotopically pure silicon [17].
The control dopants require different criteria. The wave functions of electronically excited controls overlap and interact with two or more qubits to manipulate entanglements between these qubits. The transiently excited state wave function of the control must have the right spatial extent and lifetime. While centers like Si:As could be used to show the ideas, for room-temperature operation one would choose perhaps a double donor in silicon, or substitutional phosphorus in diamond. The control dopant must have sharp optical absorption lines, since what determines the number of independent gates available in a patch is the ratio of the spread of excitation energies, inhomogeneously broadened, to the (homogeneous) linewidth. The spread of excitation energies—inhomogeneous broadening is beneficial in this optical spintronics approach [11, 12]—has several causes, some controllable. Randomness of relative control-qubit positions and orientations is important, and it seems possible to improve the distribution by using self-organization to eliminate unusable close encounters. Steps on the silicon interfaces are also helpful, provided there are no unpaired spins. Overall, various experimental data and theoretical analyses indicate likely inhomogeneous widths are a few percent of the excitation energy.
A checklist of interesting systems as qubits or controls shows some significant gaps in knowledge of defects in solids. Surprisingly little is known about electronic excited states in diamond or silicon, apart from energies and (sometimes) symmetries. Little is known about spin lattice relaxation and excited state kinetics at temperatures above liquid nitrogen, except for the shallow donors that are unlikely to be good choices for a serious quantum computer. There are few studies of stabilities of several species present at one time. Can we be sure to have isolated P in diamond? Would it lose an electron to substitutional N to yield the useless species P+ and N- ? Will most P be found as the irrelevant (spin S=0) PV- center?
What limits the number of gates in a patch is the number of control atoms that can be resolved spectroscopically one from another. As the temperature rises, the lines get broader, so this number falls and scaling becomes harder. Note the zero phonon linewidth need not be simply related to the fraction of the intensity in the sidebands. Above liquid nitrogen temperatures, these homogeneous optical widths increase fast. Thus we have two clear limits to room-temperature operation. The first is qubit decoherence, especially from spin lattice relaxation. The second is control linewidths becoming too large, reducing scalability, which may prove a more powerful limit.
Entangled states of distant atoms or solid-state defects created by interference. A wholly different approach generates quantum entanglement between remote systems by performing measurements on them in a certain way [13]. The systems might be two diamonds, each containing a single NV- center prepared in specific electron spin states, the two centers tuned to have exactly the same optical energies (Fig. 4). The measurement involves “single shot” optical excitation. Both systems are exposed to a weak laser pulse that, on average, will achieve one excitation. The single system excited will emit a photon that, after passing though beam splitters and an interferometer, is detected without giving information as to which system was excited (Fig. 5). “Remote entanglement” is achieved, subject to some strong conditions. The electronic quantum information can be swapped to more robust nuclear states (a so-called brokering process). This brokered information can then be recovered when needed to implement a strategy of measurement-based quantum information processing [14].
The materials and equipment needs, while different from those of optically controlled spintronics, have features in common. For remote entanglement, a random distribution of centers is used, with one from each zone chosen because of their match to each other. The excitation energies of the two distant centers must stay equal very accurately, and this equality must be stable over time, but can be monitored. There are some challenges here, since there will be energy shifts when other defect species in any one of the systems change charge or spin state (the difficulty is present but less severe for the optical control approach). As for optically controlled spintronics [11, 12], scale-up requires narrow lines, and becomes harder at higher temperatures, though there are ways to reduce the problem. Remote entanglement needs interferometric stability, avoiding problems when there are different temperature fluctuations for the paths from the separate systems. Again, there are credible strategies to reduce the effects.
So is room-temperature quantum computing feasible?
Spectroscopy is a generic need for both optically controlled spintronics and remote entanglement approaches. Both need qubits (the electron qubit for the measurement-based approach) with slow decoherence, a significant multiple of switching times. Both need sharp optical transitions with weak phonon sidebands to avoid loss of quantum information. A few zero phonon lines do indeed remain sharp at room temperature. The sharp lines should have frequencies stable over extended times. This mix of properties is hard to meet, but by no means impossible.
Perhaps the hardest conditions have yet to be mentioned. A quantum gate is no more a quantum computer than a transistor is a classical computer. Putting all the components of a quantum computer together could prove really hard. System integration may be the ultimate challenge. Quantum information processing (QIP) will need to exploit standard silicon technology to run the quantum system; and QIP must work alongside a feasible laser optics system. The optical systems are seriously complicated, though each feature seems manageable. It may be necessary to go to architectures even more complicated than those I have described. It might even prove useful to combine elements of remote entanglement and optical spin control, whether this is regarded as using remote entanglement to link spin patches, or as having spin patches instead of NV- centers as nodes for remote entanglements. A short article like this has to miss out many features of importance, not least questions of error correction, but a major message is that, even in the most rudimentary approaches, we have to think through all of the system when talking of a possible computer.
And what would you do with a quantum computer if you had one? Proposals that do not demand room temperature range from probable, like decryption or directory searching, to the possible, like modeling quantum systems, and even to the difficult yet perhaps conceivable, like modeling turbulence. More frivolous applications, like the computer games that drive many of today’s developments, make much more sense if they work at ambient temperatures. And available quantum processing at room temperature would surely stimulate inventive new ideas, just as solid-state lasers led to compact disc technology.
Summing up, where do we stand? At liquid nitrogen temperatures, say 77 K, quantum computing is surely possible, if quantum computing is possible at all. At dry ice temperatures, say 195 K, quantum computing seems reasonably possible. At temperatures that can be reached by thermoelectric or thermomagnetic cooling, say 260 K, things are harder, but there is hope. Yet we know that small (say 2–3 qubit) quantum devices operate at room temperature. It seems likely, to me at least, that a quantum computer of say 20 qubits will operate at room temperature. I do not say it will be easy. Will such a QIP device be as portable as a laptop? I won’t rule that out, but the answer is not obvious on present designs.
Acknowledgments
This work was supported in part by EPSRC through its Basic Technologies program. I am especially grateful for input from Gabriel Aeppli, Polina Bayvell, Simon Benjamin, Ian Boyd, Andrea Del Duce, Andrew Fisher, Tony Harker, Andy Kerridge, Brendon Lovett, Stephen Lynch, Gavin Morley, Seb Savory, and Jason Smith. I am particularly grateful to Simon Benjamin and Stephen Lynch for preparing the initial versions of the figures.
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About the Author
Marshall Stoneham
Marshall Stoneham is Emeritus Massey Professor of Physics at University College London. He is a Fellow of the Royal Society, and also of the American Physical Society and of the Institute of Physics. Before joining UCL in 1995, he was the Chief Scientist of the UK Atomic Energy Authority, which involved him in many areas of science and technology, from quantum diffusion to nuclear safety. He was awarded the Guthrie gold medal of the Institute of Physics in 2006, and the Royal Society’s Zeneca Prize in 1995. He is the author of over 500 papers, and of a number of books, including Theory of Defects in Solids, now an Oxford Classic, and The Wind Ensemble Sourcebook that won the 1997 Oldman Prize. Marshall Stoneham is based in the London Centre for Nanotechnology, where he finds the scope for new ideas especially stimulating. His scientific interests range from new routes to solid-state quantum computing through materials modeling to biological physics, where his work on the interaction of small scent molecules with receptors has attracted much attention. He is the co-founder of two physics-based firms.

The Origin of Artificial Species: Creating Artificial Personalities


(Left) Rity was developed to test the world’s first robot “chromosomes,” which allow it to have an artificial genome-based personality. (Right) A representation of Rity’s artificial genome. Darker shades represent higher gene values, and red represents negative values. Image credit: Jong-Hwan Kim, et al. ©2009 IEEE.
(PhysOrg.com) -- Does your robot seem to be acting a bit neurotic? Maybe it's just their personality. Recently, a team of researchers has designed computer-coded genomes for artificial creatures in which a specific personality is encoded. The ability to give artificial life forms their own individual personalities could not only improve the natural interactions between humans and artificial creatures, but also initiate the study of “The Origin of Artificial Species,” the researchers suggest.
The first artificial creature to receive the genomic personality is Rity, a dog-like software character that lives in a virtual 3D world in a PC. Rity’s genome is composed of 14 chromosomes, which together are composed of a total of 1,764 genes, each with its own value. Rather than manually assign the gene values, which would be difficult and time-consuming, the researchers proposed an evolutionary process that generates a genome with a specific personality desired by a user. The process is described in a recent study by authors Jong-Hwan Kim of KAIST in Daejeon, Korea; Chi-Ho Lee of the Samsung Economic Research Institute in Seoul, Korea; and Kang-Hee Lee of Samsung Electronics Company, Ltd., in Suwon-si, Korea.
“This is the first time that an artificial creature like a or software agent has been given a genome with a personality,” Kim told PhysOrg.com. “I proposed a new concept of an artificial chromosome as the essence to define the personality of an artificial creature and to pass on its traits to the next generation, like a genetic inheritance. It is critical to provide an impression that the robot is a living creature. With this respect, having emotions enhances natural for human-robot symbiosis in the coming years.”
As the researchers explain, an autonomous artificial creature - whether a physical robot or agent - can behave, interact, and react to environmental stimuli. Rity, for example, can interact with humans in the physical world using information through a mouse, a camera, or a microphone, with 47 perceptions. For instance, a single click and double click on Rity are perceived as “patted” and “hit,” respectively. Dragging Rity slowly and softly is perceived as “soothed,” and dragging it quickly and wildly as “shocked.”
To react to these stimuli in real time, Rity relies on its internal states which are composed of three units - motivation, homeostasis, and emotion - and controlled by its internal control architecture. The three units have a total of 14 states, which are the basis of the 14 chromosomes: the motivation unit includes six states (curiosity, intimacy, monotony, avoidance, greed, and the desire to control); the homeostasis unit includes three states (fatigue, hunger, and drowsiness); and the emotion unit has five states (happiness, sadness, anger, fear, and neutral).
“In Rity, internal states such as motivation, homeostasis and emotion change according to the incoming perception,” Kim said. “If Rity sees its master, its emotion becomes happy and its motivation may be ‘greeting and approaching’ him or her. It means the change of internal states and the activated behavior accordingly is internal and external responses to the incoming stimulus.”
The internal control architecture processes incoming sensor information, calculates each value of internal states as its response, and sends the calculated values to the behavior selection module to generate a proper behavior. Finally, the behavior selection module probabilistically selects a behavior through a voting mechanism, where each reasonable behavior has its own voting value. Unreasonable behaviors are prevented with matrix masks, while a reflexive behavior module, which imitates an animal’s instinct, deals with urgent situations such as running into a wall and enables a more immediate response.
“Rity was developed to test the world's first robotic ‘chromosomes,’ which are a set of computerized DNA (Deoxyribonucleic acid) code for creating robots that can think, feel, reason, express desire or intention, and could ultimately reproduce their kind, and evolve as a distinct species in a virtual world,” Kim said. “Rity can express its feeling through facial expression and behavior just like a living creature.”
As the researchers explain, each of the 14 chromosomes in Rity’s genome is composed of three gene vectors: the fundamental gene vector, the internal-state-related gene vector, and the behavior-related gene vector. As each chromosome is represented by 2 F-genes, 47 I-genes, and 77 B-genes, Rity has 1,764 genes in total. Each gene can have a range of values represented by real numbers. While genes are inherited, mutations may also occur. The nature of the genetic coding is such that a single gene can influence multiple behaviors, and also a single behavior can be influenced by multiple genes.
Depending on the values of the genes, the researchers specified five personalities (“the Big Five personality dimensions”) and their opposites to classify an artificial creature’s personality traits: extroverted/introverted, agreeable/antagonistic, conscientious/negligent, openness/closeness, and neurotic/emotionally stable.
To demonstrate an artificial genome, the researchers used their evolutionary algorithm to generate two contrasting personalities for Rity - agreeable and antagonistic - and compare Rity’s behavior in the different cases. Running the algorithm through 3,000 generations took about 12 hours to generate a genome encoding a desired personality by a Pentium 4, 2 GHz processor. For comparison, the researchers also used manual and random processes to generate genomes with agreeable and antagonistic personalities, though neither outperformed the evolutionary algorithm in terms of personality consistency and similarity to desired personality. Finally, the researchers also verified the accuracy of the evolutionary genome encoding by observing how the artificial creature reacted to a series of stimuli.
“The genome is an essential one encoding a mechanism for growth, reproduction and evolution, which necessarily defines ‘The Origin of Artificial Species,’” Kim said. “It means the origin stems from a computerized genetic code, which defines the mechanism for growing, multiplying and evolving along with its propensity to ‘feel’ happy, sad, angry, sleepy, hungry, afraid, etc.”
As the researchers showed, a 2D representation of the genome can enable users to view the chromosomes of the three gene types and easily insert or delete certain chromosomes or genes related to an artificial creature’s personality.
In the future, the researchers plan to combine the genome-based personality with the artificial creature’s own experiences in order to influence the creature’s behavioral responses. They also plan to classify and standardize the different behaviors in order to generalize the artificial genome structure.
More information:
Robot Intelligence Technology Lab: http://rit.kaist.ac.kr/home/ArtificialCreatures
Jong-Hwan Kim, Chi-Ho Lee, and Kang-Hee Lee. “Evolutionary Generative Process for an Artificial Creature’s Personality.” IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, Vol. 39, No. 3, May 2009.
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