Binary refers to any system that uses two alternative states, components, conditions or conclusions. The binary, or base 2, numbering system uses combinations of just two unique numbers, i.e., zero and one, to represent all values, in contrast with the decimal system (base 10), which uses combinations of ten unique numbers, i.e., zero through nine.
Virtually all electronic computers are designed to operate internally with all information encoded in binary numbers. This is because it is relatively simple to construct electronic circuits that generate two distinct voltage levels (i.e., off and on or low and high) to represent zero and one. The reason is that transistors and capacitors, which are the fundamental components of processors (the logic units of computers) and memory, generally have only two distinct states: off and on.
The values of bits are stored in various ways, depending on the medium. For example, the value of each bit is stored as an electrical charge in a single capacitor within a RAM (random access memory) chip. It is stored as the magnetization of a microscopic area of magnetic material on a platter in a hard disk drive (HDD) or on a floppy disk. It is stored along the spiral track on an optical disk as a change from a pit to the surface or from the surface to a pit (representing a one) and as no change (representing a zero).
Computers are almost always designed to store data and execute instructions in larger and more meaningful units called bytes, although they usually also provide ways to test and manipulate single bits. Bytes are abbreviated with an upper case B, and bits are abbreviated with a lower case b. The number of bits in a byte varied according to the manufacturer and model of computer in the early days of computing, but today virtually all computers use bytes that consist of eight bits.
Whereas a bit can have only one of two values, an eight-bit byte can have any of 256 possible values, because there are 256 possible permutations (i.e., combinations of zero and one) for eight consecutive bits (i.e., 28). Thus, an eight-bit byte can represent any unsigned integer from zero through 255 or any signed integer from -128 to 127. It can also represent any character (i.e., letter, number, punctuation mark or symbol) in a seven-bit or eight-bit character encoding system (such as ASCII, the default character encoding used on most computers).
The number of bits is often used to classify generations of computers and their components, particularly CPUs (central processing units) and busses and to provide an indication of their capabilities. However, such terminology can be confusing or misleading when used in an imprecise manner, which it frequently is.
For example, classifying a computer as a 32-bit machine might mean that its data registers are 32 bits wide, that it uses 32 bits to identify each address in memory or that its address buses or data buses of that size. A register is a very small amount of very fast memory that is built into the CPU in order to speed up its operations by providing quick access to commonly used values. Whereas using more bits for registers makes computers faster, using more bits for addresses enables them to support larger programs.
A bus is a set of wires that connects components within a computer, such as the CPU and the memory. A 32-bit bus transmits 32 bits in parallel (i.e., simultaneously rather than sequentially).
Although CPUs that treat data in 32-bit chunks (i.e., processors with 32-bit registers and 32-bit memory addresses) still constitute the personal computer mainstream, 64-bit processors are common in high-performance servers and are now being used in an increasing number of personal computers as well.
The rate of data transfer in computer networks and telecommunications systems is referred to as the bit rate or bandwidth, and it is usually measured in terms of some multiple of bits per second, abbreviated bps, such as kilobits, megabits or gigabits (i.e., billions of bits) per second.
A bitmap is a method of storing graphics (i.e., images) in which each pixel (i.e., dot that is used to form an image on a display screen) is stored as one or several bits. Graphics are also often described in terms of bit depth, which is the number of bits used to represent each pixel. A single-bit pixel is monochrome (i.e., either black or white), a two-bit pixel can represent any of four colors (or black and white and two shades of gray), an eight bit pixel can represent 256 colors and 24-bit and 32-bit pixels support highly realistic color which is referred to as true color.
The word bit was invented in the latter half of the 1940s by John W. Tukey (1915-2000), an eminent statistician, while working at Bell Labs (the research arm of AT&T, the former U.S. telecommunications monopoly). He coined it as a contraction of the term binary digit and as a handier alternative to bigit or binit. Tukey also coined the word software.
The term bit was first used in an influential publication by Claude E. Shannon (1916-2001), also while at Bell Labs, in his seminal 1948 paper A Mathematical Theory of Communication. Shannon, widely regarded as the father of information theory, developed a theory that for the first time treated communication as a rigorously stated mathematical problem and provided communications engineers with a technique for determining the capacities of communications channels in terms of of bits.
Although the bit has been the smallest unit of storage used in computing so far, much research is being conducted on qubits, the basic unit of information in quantum computing (which is based on phenomena that occur at the atomic and subatomic levels). Qubits hold an exponentially greater amount of information than conventional bits.
Created March 4, 2005. Updated April 5, 2006.


Few modern materials have achieved the fame of silicon, a key element of computer chips. The next generation of computers, however, may not rely so much on silicon. University at Buffalo researchers are among scientists working to identify materials that could one day replace silicon to make computing faster. Their latest find: A vanadium oxide bronze whose unusual electrical properties could increase the speed at which information is transferred and stored.
This week, design company 4DSP has launched live industry demonstrations of licensed NASA fiber optic sensing and 3D shape rendering technology. Past fiber optic sensing solutions have been limited by both processing speed and high deployment costs, and 4DSP expects the new technology to offer a 20-fold improvement in performance.
According to data from a 2008 Business R&D and Innovation Survey by the National Science Foundation, businesses perform the lion's share of their R&D activity in just a small number of geographic areas, particularly the San Jose-San Francisco-Oakland area and the New York-Newark-Bridgeport area.
A professor from Tel Aviv University is reconfiguring existing complementary metal-oxide-semiconductor (CMOS) chips designed for computers and turning them into high-frequency circuits. The ultimate goal of this project is to produce chips with radiation capabilities that are able to see through packaging and clothing to produce an image of what may be hidden beneath.
A new, Massachusetts Institute of Technology-developed analytical method identifies the precise binding sites of transcription factors—proteins that regulate the production of other proteins—with 10 times the accuracy of its predecessors.
A European research team has recently been able to demonstrate that germanium, under certain conditions, can function as a laser material. Together with silicon, the researchers report, germanium lasers could form the basis for innovative computer chips in which information would be transferred partially in the form of light.
Researchers from North Carolina State University have developed a new software tool to prevent performance disruptions in cloud computing systems by automatically identifying and responding to potential anomalies before they can develop into problems.
Computers may be getting faster every year, but those advances in computer speed could be dwarfed if their 1s and 0s were represented by bursts of light, instead of electricity. Researchers at the University of Pennsylvania have made an important advance in this frontier of photonics, fashioning the first all-optical photonic switch out of cadmium sulfide nanowires.
Scientists from the University of Aberdeen's Marine Biodiscovery Center and the University of St Andrews presented their work on the components of a new type of computer chip created using molecules from a sea squirt sourced from the bottom of the Great Barrier Reef.
In Finland, researchers have experimentally determined the conditions for rebounding of water droplets moving on superhydrophobic surfaces. Like billiard balls, these droplets move by way of collisions, allowing the scientists to build “droplet logic”. When combined with chemical reactions these devices demonstrate elementary Boolean logic operations.
Particular sequences of the familiar double helix structure of DNA form genes, which tell cells how to make proteins. But the vast majority of DNA lies outside of genes and is poorly understood. A massive project by more than 500 scientists to gain a comprehensive look at how our DNA works has produced an encyclopedia of information that reveals extraordinarily complex networks that tell our genes what to do. It also reveals just how much of the human genome is active.
A refined method developed at NIST for measuring nanometer-sized objects may help computer manufacturers more effectively size up the myriad tiny switches packed onto chips' surfaces. The method, which makes use of multiple measuring instruments and statistical techniques, is already drawing attention from industry.
Only about 1% of the human genome contains gene regions that code for proteins, raising the question of what the rest of the DNA is doing. Scientists have now begun to discover the answer: About 80% of the genome is biochemically active, and likely involved in regulating the expression of nearby genes, according to a study from a large international team of researchers.
Over the past few decades, the hunt for extrasolar planets has yielded incredible discoveries. Now, planetary researchers have a new tool—simulated models of how planets are born. A team of researchers at The University of Texas at Austin are using supercomputers to model and simulate the protostellar disks that precede the formation of planet.
Disorders such as schizophrenia can originate in certain regions of the brain and then spread out to affect connected areas. Identifying these regions of the brain, and how they affect the other areas they communicate with, would allow drug companies to develop better treatments and could ultimately help doctors make a diagnosis. But interpreting the vast amount of data produced by brain scans to identify these connecting regions has so far proved impossible, until now.
An international research collaboration led by scientists in the U.K. has developed a new approach to quantum computing that could lead more widespread use of new quantum technologies. The breakthrough has been a move from glass-based circuitry that allowed circuits to manipulate photons to a silicon-based technology that accomplishes the same calculations using quantum mechanical effects.
Most major Websites maintain huge databases. Almost any transaction on a shopping site, travel site, or social networking site require multiple database queries, which can slow response time. Now, researchers at Massachusetts Institute of Technology have developed a system that automatically streamlines Websites' database access patterns, making the sites up to three times as fast.
Researchers from the Australian National University have taken a quantum leap towards developing the next generation of super-fast networks needed to drive future computers. The team has developed a technique that allows for quantum information to travel at higher bandwidth using a beam of light and the phenomenon called entanglement.
On Tuesday IBM introduced a new line of mainframe computers the company calls its most powerful and technologically advanced ever. The zEnterprise EC12 mainframe server is designed to help users securely and quickly sift through massive amounts of data. Running at 5.5 GHz, IBM said the microprocessor that powers the mainframe is the fastest chip in the world.
A critical element in any microchip is an inverter—an electronic component that spits out zeros when it is given ones, and vice versa. Complementary metal-oxide-semiconductor, or CMOS, is the industry standard for this type of component, but still requires billions of dollars to achieve production scale. Researchers have recently pioneered a room-temperature additive process that creates a nanoscale inverter quickly and at low cost.
Cancer metastasis, the escape and spread of primary tumor cells, is a common cause of cancer-related deaths. But metastasis remains poorly understood, and only recently have studies indicate that blood’s “stickiness” actually tears off tumor cells. Using a statistical technique employed by animators, scientists created a new computer simulation that reveals how cancer cells enter the bloodstream and the physical forces involved.
At outdoor athletic competitions?at the Olympic Games, for example?athletes pushed themselves to the limit. But it’s hard to depict this in pictures alone. Researchers at the Fraunhofer Institute in Germany have created an intelligent camera that instantly delivers more complete picture of the action, supplying additional metadata acceleration, temperature, or heart rate.
Researchers at the Stanford University School of Medicine and Intel Corp. have collaborated to synthesize and study a grid-like array of short pieces of a disease-associated protein on silicon chips normally used in computer microprocessors. Used recently to identify patients with a severe form of lupus, the new technology has the potential to improve diagnoses of a multitude of diseases.
A research team at the University of Santa Barbara has designed and fabricated a quantum processor capable of factoring a composite number—in this case the number 15—into its constituent prime factors, 3 and 5. Although modest compared to, say, a 600-digit number, the algorithm they developed was right about half the time, matching theoretical predictions and marking a milestone on the trail of building a stronger quantum computer.
Using next-generation sequencing technology and a new strategy to encode 1,000 times the largest data size previously achieved in DNA, Harvard University geneticist George Church has encoded his book in life's language. While the volume of data is comparatively modest, the density of 5.5 petabits, or 1 million gigabits per cubic meter, is off the charts.


An artist's rendering of a molecular defect predicted to be a good qubit for quantum computing.
Credit: courtesy of J. R. Weber et al., and rendered by Peter Allen
This Behind the Scenes article was provided to LiveScience in partnership with the National Science Foundation.
Quantum computers may represent the next major paradigm shift in technology. In theory, such computers could perform faster and more complex computations using a fraction of the energy. However, in practice, building a quantum computer is a very tricky engineering challenge.
At the atomic level, particles do not behave in a way one would expect from the laws of classical physics. According to the Heisenberg uncertainty principle, it is impossible to precisely determine the speed and location of a particle at any given moment. Instead, particles are characterized by a wave function that represents a probability that the particle will be in a given physical state.
In quantum computing, instead of 0s and 1s, information is encoded in that wave function and the infinite variations that are possible in the spectrum of the wave.
"You have a lot more flexibility in setting the values of the things that you compute," said Chris Van de Walle, who, as a professor at the University of California, Santa Barbara, studies potential quantum systems. "You could have any continuous value that is being encoded in the wave function of some entity that you are now using as your fundamental unit of computing."
If it sounds far-out, it is. Quantum bits are a basic unit of information representing either a 1 or 0, and in quantum computing, a qubit can represent 1 and 0 at the same time. Over the last decade, researchers have investigated various ways of designing a practical implementation of a quantum bit (or, qubit). None are near completion.
"If you can come up with such qubits and incorporate them in the computing architecture, it has been shown theoretically that you can solve problems computationally that are currently not feasible," Van de Walle said. "The big challenge is to come up with specific implementations of these qubits."
One of the most promising implementations involves a defect in diamonds that leads to a missing carbon in the material's matrix, with a rogue nitrogen atom located nearby. This altered structure creates a hole, or vacancy — called an NV (nitrogen vacancy) center — with a specific wave function that many believe can be effectively manipulated for quantum computing.
In industry, defects are a negative. But when it comes to materials for quantum computing, it is the defect that makes computation possible.
"The defect is actually a good actor," Van de Walle said. "It's the qubit that you want to use as your unit of computation."
The biggest advantage of NV centers in diamonds is their ability to operate at room temperature, rather than requiring near-absolute-zero temperatures, as other quantum computing systems do. Electrons in the NV center also can remain coherent for a long time and be manipulated by outside forces.
"You can control where the vacancy is formed in the crystal and you can probe it very accurately with laser beams with a specific wave length," Van de Walle said.
Van de Walle, an expert in defects and impurities, has been working closely with David Awschalom, an experimentalist at UC Santa Barbara and a quantum computing expert, to expose the atomic-level dynamics of the diamond center. Van de Walle's computational simulations on the National Science Foundation-supported Ranger supercomputer at the Texas Advanced Computing Center matched experimental results for the NV center.
The simulations also added a few crucial pieces of information about the NV center. In particular, they found that the defect's charge state plays a crucial role in achieving a useable wavelength. This means one must control material doping in order to control the number of electrons that can enter a vacancy.
"For NV centers in diamonds, the optimal charge state is a negative one charge state," Van de Walle said. "For defects in other materials, it may be a different charge state, and just by guessing the charge state, you wouldn't be able to know if it's a good choice. But that's what we can calculate."
Simulating the quantum mechanical interactions of hundreds of atoms requires thousands of computer processors working in tandem for days. "Without the ability to run on Texas Advanced Computing Center's supercomputers, we would simply not have been able to do this project," Van de Walle said.
The high-fidelity quantum simulations inspire confidence among the researchers' experimental collaborators and generate new ideas for lab experiments.
"The ability to take our expertise in the area of defects and to use it creatively to design defects with certain properties is really great," Van de Walle said. "It's exciting to be able to dig into what we know about defects and use all of that knowledge to construct a defect with a given property."
Editor's Note: The researchers depicted in Behind the Scenes articles have been supported by the National Science Foundation, the federal agency charged with funding basic research and education across all fields of science and engineering. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. See the Behind the Scenes Archive.


Computer networks topology - Types of networking topologies.
Illustration of Different Network Topologies - What is Topology?
The virtual shape or structure of a network is referred as topology. It is worth remembering that this virtual design does not correspond to the actual or the physical shape of the computer networks: you could arrange the home network in a circle but it does not replicate Ring Topology. The logical or/and physical connections between nodes could be mapped graphically for determining a network topology. Graph Theory is used for studying network topology: nodes’ distance, interconnectivity, the rate of transmission and signal’s types of two networks might vary but their topologies could be identical.
The Technical Connotation of Topology
The pattern or layout of interconnections of different elements or nodes of a computer network is a network topology that might be logical or physical. As opposed to physical design, the transfer of data in a network is referred in Logical Topology (the basic network) where the Physical Topology (the core network) accounts the physical structure of a network that carries devices, cable installations and locations. LAN (local area network) is an example of network that keeps both logical and physical topologies.
What are the Basic Types of Topology?
There are seven basic types of network topologies in the study of network topology: Point-to-point topology, bus (point-to-multipoint) topology, ring topology, star topology, hybrid topology, mesh topology and tree topology. The interconnections between computers whether logical or physical are the foundation of this classification.
Logical topology is the way a computer in a given network transmits information, not the way it looks or connected, along with the varying speeds of cables used from one network to another. On the other hand the physical topology is affected by a number of factors: troubleshooting technique, installation cost, office layout and cables’ types. The physical topology is figured out on the basis of a network’s capability to access media and devices, the fault tolerance desired and the cost of telecommunications circuits.
The classification of networks by the virtue of their physical span is as follows: Local Area Networks (LAN), Wide Area Internetworks (WAN) and Metropolitan Area Networks or campus or building internetworks.
How Is the Physical Topology Classified?
Point-to-Point Network Topology
It is the basic model of typical telephony. The simplest topology is a permanent connection between two points. The value of a demanding point-to-point network is proportionate to the number of subscribers’ potential pairs. It is possible to establish a permanent circuit within many switched telecommunication systems: the telephone present in a lobby would always connect to the same port, no matter what number is being dialed. A switch connection would save the cost between two points where the resources could be released when no longer required.
Bus Network Topology
LANs that make use of bus topology connects each node to a single cable. Some connector connects each computer or server to the bus cable. For avoiding the bouncing of signal a terminator is used at each end of the bus cable. The source transmits a signal that travels in both directions and passes all machines unless it finds the system with IP address, the intended recipient. The data is ignored in case the address is unmatched. The installation of one cable makes bus topology an inexpensive solution as compared to other topologies; however the maintenance cost is high. If the cable is broken all systems would collapse.
Linear Bus: If all network nodes are connected to a combine transmission medium that has two endpoints the Bus is Linear. The data transmitted between these nodes is transmitted over the combine medium and received by all nodes simultaneously.
Distributed Bus: If all network nodes are connected to a combine transmission medium that has more than two endpoints created by branching the main section of the transmitting medium.
Star Network Topology
The topology when each network host is connected to a central hub in LAN is called Star. Each node is connected to the hub with a point-to-point connection. All traffic passes through the hub that serves as a repeater or signal booster. The easiest Star topology to install is hailed for its simplicity to add more nodes but criticized for making hub the single point of failure. The network could be BMA (broadcast multi-access) or NBMA (non-broadcast multi-access) depending on whether the signal is automatically propagated at the hub to all spokes or individually spokes with those who are addressed.
- Extended Star: A network that keeps one or more than one repeaters between the central node or hub and the peripheral or the spoke node, supported by the transmitter power of the hub and beyond that supported by the standard of the physical layer of the network.
- Distributed Star: The topology is based on the linear connectivity that is Daisy Chained with no top or centre level connection points.
Ring Network Topology
Such physical setting sets up nodes in a circular manner where the data could travel in one direction where each device on the right serves as a repeater to strengthen the signal as it moves ahead.
Mesh Network Topology
The exponent of the number of subscribers is proportionate to the value of the fully meshed networks.
- Fully Connected: For practical networks such topology is too complex and costly but highly recommended for small number of interconnected nodes.
- Partially Connected: This set up involves the connection of some nodes to more than one nodes in the network via point-to-point link. In such connection it is possible to take advantage of the redundancy without any complexity or expense of establishing a connection between each node.
Tree Network Topology
the top level of the hierarchy, the central root node is connected to some nodes that are a level low in the hierarchy by a point-to-point link where the second level nodes that are already connected to central root would be connected to the nodes in the third level by a point-to-point link. The central root would be the only node having no higher node in the hierarchy. The tree hierarchy is symmetrical. The BRANCHING FACTOR is the fixed number of nodes connected to the next level in the hierarchy. Such network must have at least three levels. Physical Linear Tree Topology would be of a network whose Branching Factor is one.
Knowledge of networking topologies is of core importance of computer networking design. Computer networks can only be developed using the knowledge about these topoliges and decide to which topology design is best suited according to the requirement.
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Quantum technologies are the way of the future, but will that future ever arrive?
Maybe so. Physicists have cleared a bit more of the path to a plausible quantum future by constructing an elementary network for exchanging and storing quantum information. The network features two all-purpose nodes that can send, receive and store quantum information, linked by a fiber-optic cable that carries it from one node to another on a single photon.
The network is only a prototype, but if it can be refined and scaled up, it could form the basis of communication channels for relaying quantum information. A group from the Max Planck Institute of Quantum Optics (M.P.Q.) in Garching, Germany, described the advance in the April 12 issue of Nature. (Scientific American is part of Nature Publishing Group.)
Quantum bits, or qubits, are at the heart of quantum information technologies. An ordinary, classical bit in everyday electronics can store one of two values: a 0 or a 1. But thanks to the indeterminacy inherent to quantum mechanics, a qubit can be in a so-called superposition, hovering undecided between 0 and 1, which adds a layer of complexity to the information it carries. Quantum computers would boast capabilities beyond the reach of even the most powerful classical supercomputers, and cryptography protocols based on the exchange of qubits would be more secure than traditional encryption methods.
Physicists have used all manner of quantum objects to store qubits—electrons, atomic nuclei, photons and so on. In the new demonstration, the qubit at each node of the network is stored in the internal quantum state of a single rubidium atom trapped in a reflective optical cavity. The atom can then transmit its stored information via an optical fiber by emitting a single photon, whose polarization state carries the mark of its parent atom's quantum state; conversely, the atom can absorb a photon from the fiber and take on the quantum state imprinted on that photon's polarization.
Because each node can perform a variety of functions—sending, receiving or storing quantum information—a network based on atoms in optical cavities could be scaled up simply by connecting more all-purpose nodes. "We try to build a system where the network node is universal," says M.P.Q. physicist Stephan Ritter, one of the study's authors. "It's not only capable of sending or receiving—ideally, it would do all of the things you could imagine." The individual pieces of such a system had been demonstrated—atoms sending quantum information on single emitted photons, say—but now the technologies are sufficiently advanced that they can work as an ensemble. "This has now all come together and enabled us to realize this elementary version of a quantum network," Ritter says.
Physicists proposed using optical cavities for quantum networks 15 years ago, because they marry the best features of atomic qubits and photonic qubits—namely that atoms stay put, making them an ideal storage medium, whereas photons are speedy, making them an ideal message carrier between stationary nodes. But getting the photons and atoms to communicate with one another has been a challenge. "If you want to use single atoms and single photons, as we do, they hardly interact," Ritter adds.
That is where the optical cavity comes in. The mirrors of the cavity reflect a photon past the rubidium atom tens of thousands of times, boosting the chances of an interaction. "During this time, there's enough time to really do this information exchange in a reliable way," Ritter says. "The cavity enhances the coupling between the light field and the atom."
The M.P.Q. group put their prototype network through a series of tests—transferring a qubit from a single photon to a single atom and reversing the process to transfer information from an atom onto a photon. Combining those read/write operations, the physicists managed to transmit a qubit from one rubidium atom to another located in a separate laboratory 21 meters away, using a messenger photon as the carrier between nodes. (The actual length of optical fiber connecting the two nodes is 60 meters, because it snakes along an indirect route.)
A significant number of the photons get lost along the way, limiting the efficiency of the process. But in principle, optical fibers could connect nodes at greater distances. "We're absolutely not limited to these 21 meters," Ritter says. "This 21 meters is just the distance that we happened to have between the two labs."
The researchers also demonstrated that their photonic link can be used to entangle the two distant atoms. Quantum entanglement is a phenomenon by which two particles share correlated properties—in other words, the quantum state of one particle depends on the state of its entangled partner. Manipulating one of the particles, then, affects the other particle's state, even if it is located in another laboratory. Researchers hope that entanglement can be harnessed to circumvent the photon losses that come from passage through optical fibers. In a proposed application called a quantum repeater, a series of nodes, linked by entanglement, would extend the quantum connection down the line without depending on any one photon as the carrier.
Ritter acknowledges that the new work is simply a prototype, and one for which numerous improvements are possible. For instance, the transfer of a quantum state between labs succeeded only 0.2 percent of the time, owing to various inefficiencies and technical limitations. "Everything is at the edge of what can be done," he says. "All these characteristics are good enough to do what we've done, but there are clear strategies to pursue to make them even better."


Jan. 18, 2001 — Physicists say they can effectively catch a light pulse in a bottle, hold onto it and release it, in an operation described as slowing light to a dead stop. It’s actually the information about the light wave that’s being captured, the researchers say, and such techniques could be applied to a future generation of quantum computers and ultrasecure communication devices.
Light normally moves through a vacuum at about 186,000 miles per second. Nothing in the universe moves faster, and Albert Einstein theorized that nothing ever could. (Click here for some caveats.)
However, light waves can slow down as they pass through a medium. Last year, a research team at the Rowland Institute for Science and Harvard University, headed by Danish physicist Lene Hau, brought light waves down to a 1 mph crawl by putting them through a specially prepared haze of ultracold sodium atoms.
Now the same group and another team at the Harvard-Smithsonian Center for Astrophysics, led by Ronald Walsworth and Mikhail Lukin, say they have “stored” pulses of light in separate experiments.
The Harvard-Smithsonian results are being published in the Jan. 29 issue of Physical Review Letters. The Rowland-Harvard findings will appear in the Jan. 25 issue of Nature, which is not yet publicly available. However, the Nature research was released to the media on Thursday, due to the reports about the other study.
Both teams accomplished what sounds like an impossible task: slowing down a light pulse so much that it appears to fade and stop, then starting it up again on demand. However, Lukin and a colleague, David Phillips, told MSNBC.com that the process is less crazy and more complicated than it sounds.
The experiments don’t involve stopping the actual photons, or particles of light. Instead, information about the light wave is gradually transferred to specially prepared atoms trapped within a glass chamber, and then turned back into a replica of the original light wave. That’s the real trick.
The Harvard-Smithsonian team used warm atoms of rubidium gas, while the Rowland-Harvard researchers used the chilled haze of sodium atoms that worked so well in their previous experiments.
In each experiment, one laser beam excites the atoms in such a way that they can’t absorb light in a traditional sense, a process called electromagnetically induced transparency. Then another laser emits a pulse of light toward the chamber. When the pulse enters the chamber, the photons and the excited atoms are coupled into quantum systems called polaritons. During this coupling, the properties of the photons are transferred to the atoms, changing or “flipping” their magnetic spin. In a sense, the atoms weigh down the photons, and that drags down the speed of the pulse.
When the pulse is fully within the haze of excited atoms, the intensity of the first laser beam is reduced to zero.
“As we decelerate, the pulse has less and less photons, and at the same time there are more and more excited spins. So when we make the light go infinitely slow ... there are no photons remaining, all of the information is in the spins,” Lukin said.
He stressed that the photons are not absorbed, as they would be under normal conditions. “The photon disappears, but when one photon disappears, one spin flips,” he said.
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Hau’s team said the pulse was “frozen” — essentially stored as a quantum pattern imprinted upon the atoms.
In each experiment, the information about the light pulse can be stored for about a thousandth of a second before it starts to decay. When the control laser beam is turned back on, photons are once again introduced into the system. The light pulse starts speeding up again, reaching its original velocity by the time it leaves the chamber.
“Essentially what we get is an exact replica, in the ideal case,” Lukin said.
Both groups said their findings could be applied to a weird technological frontier known as quantum computing.
“What’s a big deal is that you really stored this information, and that might have implications for quantum computation and quantum communication,” Lukin said.
In fact, the real value of the technique could come from “turning it inside out,” Phillips said.
“Instead of starting with a light pulse, you might imagine starting with atoms in a quantum state, and extracting the light pulse to another set of atoms, perhaps only a few inches away or maybe a thousand miles away,” he said. “Hence we will write the quantum state from the original atoms to this new set of atoms, and transmit the quantum information.”
Scientists say quantum computing could solve mathematical problems beyond the capability of existing computers, particularly involving code-making and code-breaking. And since quantum information is extremely sensitive to eavesdropping, quantum-based communication systems could provide a new level of data security.
One of the pioneers in quantum computing, IBM researcher Charles Bennett, said the efforts to slow down light represented an exciting field of research. The key, he said, is to keep the information in a quantum system free from decay, known in quantum circles as decoherence.
“If you could stop (a light pulse) and also stop the loss or at least reduce the loss of coherence, then that would be good,” he said.
But Bennett said he could not yet judge how these particular light-stopping experiments would affect his field.
In a commentary written for Nature, University of Colorado physicist Eric Cornell compared the experiments to a grand trick in which the magician makes a speeding train suddenly disappear into a sheet of gossamer fabric — and then, seconds later, just as suddenly roar out the other side.
Cornell said it wasn’t yet clear whether the experiments would truly have technological relevance to the quest for quantum computing.
“But for now it hardly matters — trainspotting doesn’t get any more interesting than this,” he said.
© 2013 msnbc.com Reprints


When in 1935 physicist Erwin Schrödinger proposed his thought experiment involving a cat that could be both dead and alive, he could have been talking about D-Wave Systems. The Canadian start-up is the maker of what it claims is the world’s first commercial-scale quantum computer. But exactly what its computer does and how well it does it remain as frustratingly unknown as the health of Schrödinger’s poor puss. D-Wave has succeeded in attracting big-name customers such as Google and Lockheed Martin Corp. But many scientists still doubt the long-term viability of D-Wave’s technology, which has defied scientific understanding of quantum computing from the start.
D-Wave has spent the last year trying to solidify its claims and convince the doubters. “We have the world’s first programmable quantum computer, and we have third-party results to prove it computes,” says Vern Brownell, CEO of D-Wave.
But some leading experts remain skeptical about whether the D-Wave computer architecture really does quantum computation and whether its particular method gives faster solutions to difficult problems than classical computing can. Unlike ordinary computing bits that exist as either a 1 or a 0, the quantum physics rule known as superposition allows quantum bits (qubits) to exist as both 1 and 0 at the same time. That means quantum computing could effectively perform a huge number of calculations in parallel, allowing it to solve problems in machine learning or figure out financial trading strategies much faster than classical computing could. With that goal in mind, D-Wave has built specialized quantum-computing machines of up to 512 qubits, the latest being a D-Wave Two computer purchased by Google for installation at NASA’s Ames Research Center in Moffett Field, Calif.
D-Wave has gained some support from independent scientific studies that show its machines use both superposition and entanglement. The latter phenomenon allows several qubits to share the same quantum state, connecting them even across great distances.
But the company has remained mired in controversy by ignoring the problem of decoherence—the loss of a qubit’s quantum state, which causes errors in quantum computing. “They conjecture you don’t need much coherence to get good performance,” says John Martinis, a professor of physics at the University of California, Santa Barbara. “All the rest of the scientific community thinks you need to start with coherence in the qubits and then scale up.”
Most academic labs have painstakingly built quantum-computing systems—based on a traditional logic-gate model—with just a few qubits at a time in order to focus on improving coherence. But D-Wave ditched the logic-gate model in favor of a different method called quantum annealing, also known as adiabatic quantum computing. Quantum annealing aims to solve optimization problems that resemble landscapes of peaks and valleys, with the lowest valley representing the optimum, or lowest-energy, answer.
Classical computing algorithms tackle optimization problems by acting like a bouncing ball that randomly jumps over nearby peaks to reach the lower valleys—a process that can end up with the ball getting trapped when the peaks are too high.
Quantum annealing takes a different and much stranger approach. The quantum property of superposition essentially lets the ball be everywhere at once at the start of the operation. The ball then concentrates in the lower valleys, and finally it can aim for the lowest valleys by tunneling through barriers to reach them.
That means D-Wave’s machines should perform best when their quantum-annealing system has to tunnel only through hilly landscapes with thin barriers, rather than those with thick barriers, Martinis says.
Independent studies have found suggestive, though not conclusive, evidence that D-Wave machines do perform quantum annealing. One such study—with Martinis among the coauthors—appeared in the arXiv e-print service this past April. Another study by a University of Southern California team appeared in June in Nature Communications.
But the research also shows that D-Wave’s machines still have yet to outperform the best classical computing algorithms—even on problems ideally suited for quantum annealing.
“At this point we don’t yet have evidence of speedup compared to the best possible classical alternatives,” says Daniel Lidar, scientific director of the Lockheed Martin Quantum Computing Center at USC, in Los Angeles. (The USC center houses a D-Wave machine owned by Lockheed Martin.)
What’s more, D-Wave’s machines have not yet demonstrated that they can perform significantly better than classical computing algorithms as problems become bigger. Lidar says D-Wave’s machines might eventually reach that point—as long as D-Wave takes the problem of decoherence and error correction more seriously.
The growing number of independent researchers studying D-Wave’s machines marks a change from past years when most interactions consisted of verbal mudslinging between D-Wave and its critics. But there’s still some mud flying about, as seen in the debate over a May 2013 paper [PDF] that detailed the performance tests used by Google in deciding to buy the latest D-Wave computer.
Catherine McGeoch, a computer scientist at Amherst College, in Massachusetts, was hired as a consultant by D-Wave to help set up performance tests on the 512-qubit machine for an unknown client in September 2012. That client later turned out to be a consortium of Google, NASA, and the Universities Space Research Association.
Media reports focused on the fact that D-Wave’s machine had performed 3600 times as fast as commercial software by IBM. But such reporting overlooked McGeoch’s own warnings that the tests had shown only how D-Wave’s special-purpose machine could beat general-purpose software. The tests had not pitted D-Wave’s machines against the best specialized classical computing algorithms.
“I tried to point out the impermanency of that [3600x] number in the paper, and I tried to mention it to every reporter that contacted me, but apparently not forcefully enough,” McGeoch says.
Indeed, new classical computing algorithms later beat the D-Wave machine’s performance on the same benchmark tests, bolstering critics’ arguments.
“We’re talking about solving the one problem that the D-Wave machine is optimized for solving, and even for that problem, a laptop can do it faster if you run the right algorithm on it,” says Scott Aaronson, a theoretical computer scientist at MIT.
Aaronson worries that overblown expectations surrounding D-Wave’s machines could fatally damage the reputation of quantum computing if the company fails. Still, he and other researchers say D-Wave deserves praise for the engineering it has done.
The debate continues to evolve as more independent researchers study D-Wave’s machines. Lockheed Martin has been particularly generous in making its machine available to researchers, says Matthias Troyer, a computational physicist at ETH Zurich. (Troyer presented preliminary results at the 2013 Microsoft Research Faculty Summit suggesting that D-Wave’s 512-qubit machine still falls short of the best classical computing algorithms.)
Google’s coalition also plans to let academic researchers use its D-Wave machine.
“The change we have seen in the past years is that by having access to the machines that Lockheed Martin leased from D-Wave, we can engage with the scientists and engineers at D-Wave on a scientific level,” Troyer says.
About the Author
Brooklyn, N.Y.–based reporter Jeremy Hsu knew the time was right for a story about the Canadian quantum-computer company D-Wave Systems and its controversial claims. “There are finally independent studies that go at these big questions that have been hanging over this company from the start,” he says. “It was time to check in with the quantum-computing community to see if their attitude had changed.” The answer? It’s complicated.


If the experiment was meant to silence the critics, it didn’t. Four years ago, an upstart tech company created a stir when it claimed to have built a quantum computer—a thing that, in principle, could solve problems ordinary computers can’t. Physicists from D-Wave Systems in Burnaby, Canada, even put on a demonstration. But other researchers questioned whether there was anything quantum mechanical going on inside the device. Now, the D-Wave team has published data that they say prove quantum phenomena are at work within its chip. But even if that’s true, others still doubt that, as D-Wave researchers claim, the chip can do quantum-mechanical computations.
“I think they’re overstating this,” says John Martinis, a physicist at the University of California, Santa Barbara (UCSB). “It’s not obvious that they’ve implemented a quantum algorithm.”
Physicists have been trying to develop quantum computers for more than a decade. An ordinary computer deals with bits that encode a 0 or a 1. As first conceived, a quantum computer would use subatomic particles or other quantum objects as “qubits” that could encoded 0, 1, or, thanks to the weird rules of quantum mechanics, both 0 and 1 at the same time. What's more, a string of qubits in that strange state could encode every possible combination of 1 and 0 values at the same time. As a result, a quantum computer could process myriad inputs at once and crack problems that would overwhelm a conventional computer. However, that approach to quantum computing, called the “gate model,” presents many unresolved practical problems, as scientists must maintain and manipulate the delicate quantum state of many qubits.
D-Wave researchers have taken a different tack, known as “adiabatic quantum computing” or “quantum annealing.” They begin with a set of noninteracting qubits—in their rig, little rings of superconductor that can carry current either one way or the other or both ways at once—and put the rings in their lowest energy “ground state." To perform the computation, the researchers slowly turn on various interactions among the qubits. If they’ve done things right, then the ground state of the noninteracting system should naturally evolve into the ground state of the interacting system and reveal the answer to the problem encoded in the interactions.
In February 2007, D-Wave created a splash when its 16-qubit machine solved several puzzles—although none that a conventional computer couldn’t handle—such as figuring out how to seat guests around a table so that people who dislike each other do not end up side by side. However, “people had serious doubts that this was a true quantum computer,” says Wim van Dam, a theoretical computer scientist at UCSB.
Here’s why: The workings of the machine can be thought of as tracing the trajectory of a marble through a changing energy landscape of peaks and valleys as it finds its way to the lowest point—the solution to the problem. A process called quantum tunneling lets the marble burrow from one valley to another. At the same time, however, plain old “thermal fluctuations” also agitate the hypothetical marble and can push it over the ridges in the landscape so that it reaches the lowest valley. That process is not quantum mechanical, van Dam says, so if that’s how the D-Wave computer works, then it cannot be significantly more efficient than an ordinary computer.
However, new data show that in fact the qubits in the chip can find their lowest energy state quantum mechanically, D-Wave researchers report this week in Nature. Physicist Mark Johnson and colleagues begin experimenting with a single qubit within their latest 128-qubit chip. Current in the ring can circulate either clockwise or counterclockwise, and those two states represent two dips in a very simple energy landscape. By tuning the qubit and applying a magnetic field, the researchers can raise the height of the ridge between those two states and also tilt the entire landscape to make one dip lower than the other. They can also change the temperature—the source of the pesky thermal fluctuations.
The researchers found that the ability of the qubit to get from the higher energy state to the lower one at first decreases as the temperature falls. But below about 45 thousandths of a degree above absolute zero (45 millikelvin), the rate at which the qubit makes the switch levels off. That suggests that even as thermal fluctuations grow too weak to nudge the system over the energy barrier, quantum tunneling remains to allow the qubit through it. The researchers observe a similar phenomenon as a chain of eight qubits with very simple interactions finds its way to its predicted ground state. “The evolution [of the system] is consistent with quantum mechanics and not with classical mechanics,” Johnson says.
Martinis has some quibbles. Still, he says, “I think it’s pretty likely that they’ve got tunneling. I’m not 100% sure, but I’m 90% sure.”
The results won’t end the controversy over D-Wave's technology, however. Quantum tunneling alone is not enough to make the device significantly faster than a classical computer, van Dam says. To whack through really big computations that would take an infinite amount of time on a classical computer, he says, D-Wave’s chip also has to maintain a kind of delicate synchrony between the individual qubits called coherence. But it’s possible that D-Wave’s qubits lose coherence very quickly to act more or less independently but nonetheless tunnel to their collective ground state. And in that case, the computer can’t hope to be any more efficient than a regular one, van Dam says.
Johnson and the D-Wave team are not convinced that coherence is necessary in adiabatic quantum computing. “I think it’s not entirely understood what role coherence plays in quantum annealing,” Johnson says. Martinis says it’s unusual to see a company essentially wager its future on a point of scientific dispute. “In some ways, I kind of respect that it’s a clear corporate strategy,” he says. “On the other hand, I’m not going to invest in their technology because I think they’re wrong.”
Stay tuned. Johnson says the D-Wave team members will have more publications to back up their claim that they really have a quantum computer.


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Posted: Mar 27, 2013
Physicists' technique for cooling molecules may be a stepping stone to quantum computing
(Nanowerk News) The next generation of computers promises far greater power and faster processing speeds than today's silicon-based based machines. These "quantum computers" — so called because they would harness the unique quantum mechanical properties of atomic particles — could draw their computing power from a collection of super-cooled molecules.
But chilling molecules to a fraction of a degree above absolute zero, the temperature at which they can be manipulated to store and transmit data, has proven to be a difficult challenge for scientists.
Eric Hudson"Scientists have been trying to cool molecules for a decade and have succeeded with only a few special molecules," said Eric Hudson, a UCLA assistant professor of physics and the paper's senior author. "Our technique is a completely different approach to the problem — it is a lot easier to implement than the other techniques and should work with hundreds of different molecules."
Previous attempts to create ultracold molecules were only effective with one or two specific kinds. Creating a method that can be used with many different molecules would be a major step forward because it is difficult to say which materials might be used in quantum computers or other future applications, Hudson said.
By immersing charged barium chloride molecules in an ultracold cloud of calcium atoms, Hudson and his colleagues are able to prevent most of the molecules from vibrating and rotating. Halting the molecules is a necessary hurdle to overcome before they can be used to store information like a traditional computer does.
"The goal is to build a computer that doesn't work with zeros and ones, but with quantum mechanical objects," Hudson said. "A quantum computer could crack any code created by a classical computer and transmit information perfectly securely."
Hudson's experiment makes molecules extremely cold under highly controlled conditions to reveal the quantum mechanical properties that are hidden under normal circumstances. At room temperature, molecules rocket around, bouncing into each other and exchanging energy. Any information a scientist attempted to store in such a chaotic system would quickly become gibberish.
"We isolate these molecular systems in a vacuum, effectively levitating them in the middle of nothing," Hudson said. "This removes them from the rest of the world that wants to make them classical."
The quantum mechanical world of subatomic particles deviates from the classical world that we observe with the naked eye because according to quantum mechanics, electrons can only exist at specific energy levels. In a quantum computer made of a collection of single atoms, information might be stored by boosting some atomic electrons to higher energy levels while leaving others at lower energy states. However, these atomic energy states are not stable enough to reliably preserve data, Hudson said.
"One of the challenges with atoms is that their energy states are very easily influenced by the outside world," Hudson said. "You make this beautiful quantum state, but then the outside world tries to destroy that information."
Instead of saving data in easily disrupted atomic energy states, a more robust way to store information is in the rotational energy states of molecules, Hudson said. A spinning molecule in the lowest energy rotational state could represent a binary one, while a stationary molecule could represent a binary zero.
Despite applications for quantum computing and other industries, cooling molecules to extremely low temperatures has proved a challenge. Even the simplest molecule composed of only two atoms is a far more complex system than a single atom. Each molecule vibrates and rotates like a miniature whirling slinky, and all of that movement must be stilled so that the molecule can lose energy and cool down.
A new cooling technique
To solve the ultracold molecule conundrum, Hudson and his group first created a floating cloud of calcium atoms corralled by incoming laser beams from all directions. This magneto-optical trap keeps the atoms stationary as it cools them to nearly absolute zero. They then use specialized rods with high, oscillating voltages as part of an ion trap to confine a cloud of positively-charged barium chloride molecules within the ultracold ball of calcium atoms to complete the cooling process.
For the vibrating, energetic molecules to lose heat, they must spend a significant amount of time in contact with the surrounding ultracold atom cloud. Hudson and his colleagues used barium chloride ions, molecules missing one electron, because charged molecules are easier to trap and cool than their neutral counterparts. The use of molecular ions is an essential innovation because previous efforts have demonstrated that neutral molecules ricochet off ultracold atoms without sufficient heat transfer.
"When a molecular ion and a neutral atom get close together they get in tight and bang off each other a bunch before the ion goes away," Hudson said. "When they collide like that it is very easy for the energy in one to go to the other."
While magneto-optical and ion traps are not new to the world of molecular physics, Hudson and his colleagues became the first group to combine these methods to create a cloud of ultracold molecules. This paper is the result of over four years of work spent designing, building, and testing their experiment.
"These two different technologies earned Nobel prizes for the scientists who developed them, but there wasn't really a body of knowledge about how to put these two procedures together," Hudson said. "
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You may have a $10,000 Sub-Zero fridge in your kitchen, but this is cooler. Theoretical physicists have dreamed up a scheme to make a refrigerator out of a pair of quantum particles such as ions or atoms, or even a single particle. The fridges may be the smallest ones possible. “It’s very elegant and innovative,” says Nicolas Gisin, a theorist at the University of Geneva in Switzerland. Theo Nieuwenhuizen, a theorist at the University of Amsterdam, says “I don’t see any error, so probably this would work.”
The challenge is to make a few quantum particles act like a so-called thermal machine, the theory of which was set out by French engineer Sadi Carnot in 1824. Carnot imagined a piston filled with gas that could be compressed or expanded. The piston could make contact with either of two large bodies (say, massive steel blocks) at different temperatures, which could serve as the “hot bath” and the “cold bath.”
Carnot put the imaginary piston through a cycle of motions, including one in which the gas expands while in contact with the hot bath and another in which it is compressed while in contact with the cold bath. During the cycle, the piston does work while absorbing heat from the hot bath and releasing heat into the cold one, making it a “heat engine.” Reverse the cycle and, in response to work done on it, the piston acts as a refrigerator, absorbing heat from the cold bath and releasing it into the hot one.
Now, Noah Linden, Sandu Popescu, and Paul Skrzypczyk of the University of Bristol in the United Kingdom report that, at least in principle, they can make a refrigerator out of a few quantum particles called “qubits.” Each qubit has only two possible quantum states: a zero-energy ground state and a fixed-energy excited state. The theorists have found a way to siphon energy out of one qubit by making it interact with just two others.
The theorists arrange things so that each qubit has a different excited-state energy but the trio of qubits has two configurations with the same total energy. One is the configuration in which only the first and third qubits are in their excited states—denoted (101). The other is the configuration in which only the second qubit is in its excited state—denoted (010). If all three qubits were at the same temperature, then the system would flip with equal probability back and forth between these two configurations.
But the researchers skew that flipping, as they explain in a paper in press at Physical Review Letters. The trick is to put the first two qubits in contact with a cold bath and the third one in contact with a hot bath. The higher temperature makes it more likely that the third qubit will be in its excited state—and thus that the trio will be in the (101) state instead of the (010) state. But that means the system is more likely to flip out of (101) and into (010) than the other way around. So on average the flipping takes the first qubit from its excited state to its ground state and draws energy out of the first qubit. After a flip, the qubits essentially reset by interacting with the baths, allowing the cycle to start again.
The theorists measure the fridge’s size in terms of the number of its quantum states, and the three qubits have a total of eight possible states. That number can be clipped to six, if they replace the second and third qubits with a single “qutrit,” a particle with a ground state and two excited states—although those two states have to be in contact with different baths. “We believe that’s probably the smallest number of states you can get away with,” Linden says.
In theory, such a fridge can get arbitrarily close to absolute zero, and Popescu says that it might be possible to make one using trapped ions for the qubits and streams of laser light as the baths. Some researchers hope to use such qubits as the guts for a quantum computer, and Popescu says the refrigerator scheme might allow researchers to cool some set of qubits with a few others. David Wineland, an experimental physicist with the U.S. National Institute of Standards and Technology in Boulder, Colorado, says he believes such schemes can indeed be implemented in trapped ions.
Others suggest that such tiny quantum refrigerators might already be humming along in nature. It’s possible that one part of a biomolecule might work to cool another in such a fashion, says Hans Briegel, a theorist at the University of Innsbruck in Austria. “I don’t expect that you will have a mechanism exactly like this,” Briegel says, “but it gives you a framework valuable for telling what to search for.”
No word yet on when physicists might unveil the smallest possible beer.


Microwave photonics circuit elements will need to be similar to their RF analogs to provide the desired functionality.
One of these analogous circuit elements is a terahertz microwave cavity resonator, which can be integrated onto an IC with standard CMOS processes.
This is one of many circuit elements that can be placed on an IC and used to enable unique applications.
These fibers will soon be integrated into semiconductor wafers as microwave lines to communicate with unique circuit elements like terahertz microcavity resonators.
Microwave components have a lot more going on than what ends up in your microwave oven. Terahertz wave sources, detectors, and components have yet to be miniaturized, and the terahertz portion of the microwave spectrum is still largely unexplored. So far, the best we can do is get into the high GHz (low THz) region for oscillation, detection, and wave manipulation. This region is critical for many applications, including quantum computing, imaging, sensing, and ultra-fast communication.
One fundamental set of components is terahertz microcavity resonators. These components are part of a larger photonics platform and they play analogous roles to RF resonators on a PCB. The simple geometry of these resonators also allows them to be placed on a chip alongside other photonic structures. If you’re a budding photonics engineer, keep reading to learn more about these resonator structures and how they might play a role in current and upcoming photonics systems.
What Are Terahertz Microcavity Resonators?
Much like any other resonator, terahertz microcavity resonators have a fundamental frequency that lies in the terahertz region. In terms of wavelength, a 1 THz wave in air has a wavelength of only 300 microns, which is quite large compared to today’s transistors. These structures provide the same function as well; they allow a wave matching the fundamental frequency or one of its harmonics to excite a high-Q resonance, whereby a standing wave can form in the cavity.
Much like a wave on a string or in a waveguide, this standing wave at one of the eigenfrequencies will have very high intensity due to constructive interference inside the cavity. The very strong, very coherent electromagnetic wave in this structure can then be used for some other application. The challenges in working with these structures are wave generation and detection, both of which need to be solved for terahertz microcavity resonators to be useful at the chip level.
Geometry and Eigenfrequencies
The image below shows a simple rectangular terahertz microcavity resonator and its discrete eigenfrequency spectrum. The eigenfrequencies can be tuned to desired values by adjusting the geometry, just like any other resonator. The equation below applies to a closed rectangular cavity and provides a good first approximation for a slightly lossy cavity (i.e., with high dielectric constant contrast at the edge).
Rectangular terahertz microcavity resonator geometry and eigenfrequencies.
Although a rectangular geometry is shown above, more complex structures may be used for different applications. In a different structure (e.g., circular, hemispherical, or cylindrical) with an open edge, the eigenfrequencies may not obey such a simple equation. Instead, they may be determined from a dispersion relation that is a transcendental equation, which requires a numerical technique to extract specific frequencies. This is a well-known procedure for solving Sturm-Liouville problems in waveguides and resonators.
If you have a much more complex structure that can’t be approximated as a simple shape, the various eigenfrequencies and the spatial distribution of the electromagnetic field can be determined using a 3D field solver (FDFD technique). A field solver you would normally use for IC packages can also be used for modeling terahertz microcavity resonators.
Applications for terahertz microcavity resonators are still being researched, as are the device architectures required for different applications. Some proposed applications of terahertz microcavity resonators include:
Sensing and imaging: High-Q terahertz microcavity resonators can be used for highly coherent imaging and sensing, with applications in molecular detection and biological imaging.
Silicon photonics: While this application area is normally discussed in terms of SMF or MMF wavelengths, devices in this area can also operate at THz frequencies and will need terahertz microcavity resonators to act as filters and amplifiers.
Communication: Currently, the world record for the highest data rate transmission belongs to an experimental wireless system operating at THz frequencies. Miniaturizing these systems at the chip level will require microcavity structures, including terahertz microcavity resonators.
The important advancement provided by these structures is that they can occur on an integrated circuit. Today, these applications still involve large optical systems where an infrared mode comb in a femtosecond soliton laser is used to generate a terahertz wave through interference. Similarly, large systems are also used for the detection and manipulation of terahertz waves. Terahertz microcavity resonators are one class of components that can provide high-Q or low-Q reception of THz frequencies, which can then be passed to a detector element or other photonic circuit.
The range of useful materials for building terahertz microcavity resonators, or for building coupling structures, is also an open research question. Some material platforms used for terahertz microcavity resonators include:
Silicon: This material is the most promising for the fabrication of terahertz devices and their integration alongside other electronic circuits.
GaAs, other III-V’s, and II-VI’s: This promising set of photonic materials has already shown interesting results at ~3 THz frequencies, particularly for the generation of laser light. This material platform is promising for photonics in general.
Photonic crystals: Periodic nanostructures that are fabricated through chemical deposition methods provide a tunable platform for fabricating a range of terahertz devices, including terahertz microcavity resonators.
Dielectrics: This broad range of materials includes oxides, salts, polymers, and other materials that can support transmission or absorption in various THz frequency ranges. For integration, the best set of materials should bond to the industry’s current range of semiconductors.
Microcavity resonator materials should be chosen to integrate into existing semiconductor materials platforms and manufacturing processes.
As your technology and designs push into more advanced spaces with the years to come, more advanced software that can navigate the nuances and challenges of THz components will be necessary. Be sure to prepare adequately as you stay ahead of the frequency curve.
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First Teleportation Between Distant Atoms
For the first time, scientists have successfully teleported information between two separate atoms in unconnected enclosures a meter apart – a significant milestone in the global quest for practical quantum information processing.
Teleportation may be nature’s most mysterious form of transport: Quantum information, such as the spin of a particle or the polarization of a photon, is transferred from one place to another, but without traveling through any physical medium. It has previously been achieved between photons over very large distances, between photons and ensembles of atoms, and between two nearby atoms through the intermediary action of a third. None of those, however, provides a feasible means of holding and managing quantum information over long distances.
Now a team from the Joint Quantum Institute (JQI) at the University of Maryland (UMD) and the University of Michigan has succeeded in teleporting a quantum state directly from one atom to another over a substantial distance (see reference publication). That capability is necessary for workable quantum information systems because they will require memory storage at both the sending and receiving ends of the transmission. In the Jan. 23 issue of the journal Science, the scientists report that, by using their protocol, atom-to-atom teleported information can be recovered with perfect accuracy about 90% of the time – and that figure can be improved.
“Our system has the potential to form the basis for a large-scale ‘quantum repeater’ that can network quantum memories over vast distances,” says group leader Christopher Monroe of JQI and UMD. “Moreover, our methods can be used in conjunction with quantum bit operations to create a key component needed for quantum computation.” A quantum computer could perform certain tasks, such as encryption-related calculations and searches of giant databases, considerably faster than conventional machines. The effort to devise a working model is a matter of intense interest worldwide.
Teleportation works because of a remarkable quantum phenomenon called entanglement which only occurs on the atomic and subatomic scale. Once two objects are put in an entangled state, their properties are inextricably entwined. Although those properties are inherently unknowable until a measurement is made, measuring either one of the objects instantly determines the characteristics of the other, no matter how far apart they are.
The JQI team set out to entangle the quantum states of two individual ytterbium ions so that information embodied in the condition of one could be teleported to the other. Each ion was isolated in a separate high-vacuum trap, suspended in an invisible cage of electromagnetic fields and surrounded by metal electrodes. [See illustration above.] The researchers identified two readily discernible ground (lowest energy) states of the ions that would serve as the alternative “bit” values of an atomic quantum bit, or qubit.
Conventional electronic bits (short for binary digits), such as those in a personal computer, are always in one of two states: off or on, 0 or 1, high or low voltage, etc. Quantum bits, how-ever, can be in some combination, called a “superposition,” of both states at the same time, like a coin that is simultaneously heads and tails – until a measurement is made. It is this phenomenon that gives quantum computation its extraordinary power.
At the start of the experimental process, each ion (designated A and B) is initialized in a given ground state. Then ion A is irradiated with a specially tailored microwave burst from one of its cage electrodes, placing the ion in some desired superposition of the two qubit states – in effect “writing” into “memory” the information to be teleported.
Immediately thereafter, both ions are excited by a picosecond (one trillionth of a second) laser pulse. The pulse duration is so short that each ion emits only a single photon as it sheds the energy gained by the laser and falls back to one or the other of the two qubit ground states.
Depending on which one it falls into, the ion emits one of two kinds of photons of slightly different wavelengths (designated red and blue) that correspond to the two atomic qubit states. It is the relationship between those photons that will eventually provide the telltale signal that entanglement has occurred.
Each emitted photon is captured by a lens, routed to a separate strand of fiber-optic cable, and carried to a 50-50 beamsplitter where it is equally probable for the photon to pass straight through the splitter or to be reflected. On either side of the beamsplitter are detectors that can record the arrival of a single photon.
Before it reaches the beamsplitter, each photon is in an unknowable superposition of states. After encountering the beamsplitter, however, each takes on specific characteristics. As a result, for each pair of photons, four color combinations are possible – blue-blue, red-red, blue-red and red-blue – as well as one of two polarizations: horizontal or vertical. In nearly all of those variations, the photons either cancel each other out or both end up in the same detector. But there is one – and only one – combination in which both detectors will record a photon at exactly the same time.
In that case, however, it is physically impossible to tell which ion produced which photon because it cannot be known whether a photon arriving at a detector passed through the beamsplitter or was reflected by it.
Thanks to the peculiar laws of quantum mechanics, that inherent uncertainty projects the ions into an entangled state. That is, each ion is in a superposition of the two possible qubit states. The simultaneous detection of photons at the detectors does not occur often, so the laser stimulus and photon emission process has to be repeated many thousands of times per second. But when a photon appears in each detector, it is an unambiguous signature of entanglement between the ions.
When an entangled condition is identified, the scientists immediately take a measurement of ion A. The act of measurement forces it out of superposition and into a definite condition: one of the two qubit states. But because ion A’s state is irreversibly tied to ion B’s, the measurement also forces B into the complementary state. Depending on which state ion A is found in, the researchers now know precisely what kind of microwave pulse to apply to ion B in order to recover the exact information that had been written to ion A by the original microwave burst. Doing so results in the accurate teleportation of the information.
What distinguishes this outcome as teleportation is that no information pertaining to the original memory actually passes between ion A and ion B. The information disappears when ion A is measured and reappears when the microwave pulse is applied to Ion B.
“One particularly attractive aspect of our method is that it combines the unique advantages of both photons and atoms,” says Monroe. “Photons are ideal for transferring information fast over long distances, whereas atoms offer a valuable medium for long-lived quantum memory. The combination represents an attractive architecture for a ‘quantum repeater,’ that would allow quantum information to be communicated over much larger distances than can be done with just photons. Also, the teleportation of quantum information in this way could form the basis of a new type of quantum internet that could outperform any conventional type of classical network for certain tasks.”
The research was supported by the Intelligence Advanced Research Project Activity program under U.S. Army Research Office contract, the National Science Foundation (NSF) Physics at the Information Frontier Program, and the NSF Physics Frontier Center at JQI.


Quantum computers are revolutionizing computers and are paving the way for innovations — for example, in medicine and the Internet of Things. Shohini Ghose explains what sets quantum computers apart.
Shohini Ghose’s work begins where our understanding ends. As a physicist, she works in the field of quantum mechanics, which theorizes that there is a probability for a particle to be found in two different locations at a given time — something that seems actually unthinkable. “It is so exciting. We observe those microscopic particles indirectly and develop an explanation of this hidden quantum world,” says Ghose. While this might sound like science fiction, it has concrete real-world applications, especially given that quantum mechanics could revolutionize computers. Ghose explains what this means as follows: “Quantum computers are not just a faster version of our current computers. They operate on the laws of quantum physics. It’s just like a light bulb compared to a candle.”
“Quantum computers can do computing tasks that are outside of the reach of even the best computers today.”
One? Zero? All in between!
Whereas conventional computers use bits as the smallest electronic storage unit, quantum computers use quantum bits — qubits for short. These go beyond the usual binary code of zeros and ones because they can take on any number of overlap states. In other words, it can be described as having a probability of being zero or one. This, which is commonly referred to as a “superposition” state, cannot be compared to anything from our everyday world, but can be easily explained with the following image: imagine a qubit as a sphere with the one at its north pole and the zero at its south pole. While a bit in a conventional computer is in a state of either zero or one, a qubit can take on any in-between state on the surface of the sphere.
This superposition allows qubits to carry out parallel computing operations. “That means we can do computing tasks that are outside of the reach of even the best computers today. We can do calculations faster, and search faster through big data,” says Ghose. Artificial intelligence, which is designed to analyze huge amounts of data, could benefit from this, as could materials and pharmaceutical research. “Future large-scale quantum simulation could perhaps lead to treatments for diseases like Alzheimer’s,” suggests Ghose. In order for that to happen, atom structures need to be precisely analyzed, which is already difficult for mainframe computers that are currently employed by researchers.
“Quantum is one way to really secure the Internet and the communication in the Internet of Things.”
An end to all cyber-attacks?
Quantum computers could also render communication more secure in the way information is “teleported”. There’s another term associated with sci-fi films. However, the phenomenon of “entanglement” lies behind quantum mechanics: two qubits are linked together in such a way that a change to one causes a change to its corresponding qubit. This occurs without time lags, over any distance, and of course without any physical connection such as cables or radio waves.
Using this idea key codes for data transmission could be generated. The clever thing here is that the quantum state of the qubit changes with every unauthorized access — for example, an attack from a hacker. The communication partners would perceive this as a disturbance in their communication, would thus be warned and could use a new key. “Quantum is one way to really secure the Internet and the communication in the Internet of Things”, says Ghose, who works with her team on encryption protocols of this nature.
is the temperature to which quantum computers must be cooled in order for the qubits to operate reliably.
Why it is important to talk about quantum computers
The immense power of quantum computers also raises ethical questions. On the one hand, they currently consume a great deal of electricity because their chips have to be laboriously cooled down with liquid helium to -273.13° Celsius in what are known as dilution refrigerators. On the other hand, there is a risk that this technology could fall into the wrong hands — should criminals succeed in building a quantum computer, they could use it for the purpose of launching cyber-attacks. They would then be able to crack all data that is encrypted on the basis of conventional computers. Therefore, Ghose is advocating for a social discussion about quantum computers: “I hope that we can address this before the technology is rolled out rather than to catch up and to regulate and control later.” Ghose is convinced that this would allow the enormous potential of the quantum revolution to be put on the right track.
An interview with Dr. Shohini Ghose, professor of quantum physics
Dr. Shohini Ghose
Professor of quantum physics and computer science at Wilfrid Laurier University in Waterloo, Canada
Quantum offers a way to encrypt information that can never be hacked, no matter how good the hackers are.
Shohini Ghose grew up in India and later studied physics and mathematics at the University of Miami and the University of New Mexico, USA. In 2003 she was a postdoctoral student at the University of Calgary in Canada and one year later became a professor at Wilfrid Laurier University. Together with her colleague Paul Jessen’s team from the University of Arizona, Ghose was the first to show that there is a connection between chaos theory and quantum entanglement in cesium atoms. She is also the founder and director of the Laurier Centre for Women in Science and is the president of the Canadian Association of Physicists.
Quantum computing makes use of what are referred to as quantum bits, making them more powerful than conventional computers. Among other things, this enables very secure encryption techniques for data transmission on the Internet, says Shohini Ghose. Nevertheless, she warns that the large computing capacity of quantum computers also raises ethical questions that urgently need to be discussed.


Artificial general intelligence (AGI) also known as strong AI is the mimic of generalized human cognitive abilities. AGI has the thinking and acting capabilities of human beings. So it can think, perform as human beings. It is the application of emergent behavior that ensures reinforced learning. Strong Artificial intelligence describes a mindset of AI development within a game environment. Moreover, it is indistinguishable from the human brain. Here we will discuss details of AGL and the best possible artificial general intelligence Examples.
AGI Meaning: What is the best definition of strong AI?
Deep AI or Artificial general intelligence (AGI) refers to the hypothetical ability of an intelligent agent that can perform any intellectual task like humans. It is the representation of human cognitive performance through machines. It consists of comprehensive knowledge and cognitive computing capabilities of the human brain. Sometimes, this type of artificial intelligence is beyond human capacities to process vast amounts of data.
Artificial General Intelligence (AGI) also refers to general intelligent action, full AI, and deep AI. AGI is a continuous process that discusses science fiction and future studies. We can compare it with a child because it focuses on learning through experience. It constructs mental abilities through different functions and processes impersonated from the human brain. It needs continuous processing, developing, and upgrading to reach the optimum perfection level.
We can see science fiction movies regarding the application of artificial general intelligence. Sometimes we found it in games. AGI is independent and can adapt to new situations. It is a crucial part of the AI revolution.
The theory of Artificial General Intelligence (AGI ) rests upon complex machine systems that study neural networks. It is the system’s actual ability capable of solving complex situations by trial and error systems. Many AI experts think that AGI does not exist, but some believe in AGI.
Why Do We Call Strong AI as General AI?
Strong general intelligence is capable of doing many complex tasks. As a result, maximum intelligence functions can be solved by this AI. So we can generalize all the requirements if we can implement AGI. As a result, we can consider it as general AI.
Tests of AGI
So far, we have found two tests of deep AI. The first one is the Turing Test, developed by Alan Turing in 1950. He discussed it in the paper “Computing Machinery and Intelligence”.
The second one is the Chinese Room Argument (CRA). John Searle discussed it in 1980.
Strong AI vs. weak AI
Strong AI is the intimation of human intelligence. We can compare it to a childhood brain. A brain develops from childhood to adulthood. Similarly, this AI develops in the process of the learning experience. Weak AI or narrow AI is the opposite of it. Narrow AI has limited memory and does not have any experience.
AGI is dedicated to all sorts of tasks. On the other hand, weak AI is capable of solving particular problems. Artificial general intelligence performs a variety of functions and solves problems from the scenario. It does not rely on human interference. In the country, weak AI depends on human interference. Fuzzy logic is the example of AGI, and Self-driving cars and virtual assistants are the examples of weak AI.
Why is Deep AI so powerful?
AI bridges the gap between data science and its execution. This emerging technology has become an essential part of our daily life. Moreover, it is consists of big data, machine learning, neural network, and deep learning. Each moment it gathers experience from previous learning. Sometimes, it performs based on the situation. All of those elements make IA more powerful.
What is Artificial General Intelligence Examples?
The application of AGI is in the developing stage. It simplifies the task and gives results in an accurate and faster way. Moreover, we can use it for precise predictions, decisions making, and accurate analyses. However, here are some possible examples of strong artificial intelligence.
1. Manufacturing Robots
AI-based robot control solutions can automate manual workstations. We can implement manufacturing robots in plugging cables, assembling products, Picking parts, tracking contours, etc. A single-camera can assist this project.
2. Self-driving cars
Without human drivers, self-driving cars can drive up to their destination. It uses sensors, cameras, radar, and artificial intelligence (AI) to run smoothly. nuTonomy, AutoX, Drive.ai, Optimus Ride, Waymo, Zoox, and Tesla are examples of AI-enabled solid self-driven cars.
3. Smart Assistants
AI assistants are a combination of microphones, computer chips, and AI software. It takes input and process with intelligence. Alexa, Siri, Nina, Viv Google Assistant, etc. are the example of Smart assistants.
4. Proactive healthcare management
We can see countless applications in healthcare. AI can contribute EKGs, genomics, radiology images, blood tests, and managing patient medical history. It eliminates the chance of human error.
5. Disease Mapping
We can see the uses of STRONG AI in mapping diseases. For any infectious disease, it can be the best example. During the covid situation, we can get the best utilization of AGI.
6. Automated Financial Investing
The digital platform sets a pre-plan of the investment policy, customer information, and trading-based algorithm. Underwriters take decisions from the credit system. The application of Artificial general intelligence (AGI) can make confident decisions.
7. Virtual Travel Booking Agent
The travel industry is booming after the use of artificial general intelligence. It is highly suitable for flight booking and accommodation. Intelligent chatbots and AI assistants are the key factors motivating tourists in the tourism industry.
8. Social Media Monitoring
AI is also important for social media monitoring. It can deliver insight regarding social media profiles and brand insight. AI responds to customers based on their preferences.
Example: EThe Facebook chatbot is an example of AI Social media monitoring.
9. Conversational marketing bot
A set of AI technology is used for automated speech recognition and human-like interaction. It serves 24/7 and reduces the cost of stuffing. Almost all business organizations are using the Conversational marketing AI bot.
10. Natural Language Processing (NLP) Tools
Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that understands natural language and acts accordingly. Translation, spell check, or topic classification are some examples of Natural Language Processing (NLP) tools.
11. Fraud News Detection
Deep AI can understand the insight of massage. So, it can detect the fraud news quickly. It helps a lot for misleading people.
What Can Artificial General Intelligence Do?
Artificial general intelligence is known as deep AI. This concept mimics the human brain, understanding, thinking, and applying solutions in real life. Its theory of mind part explains the needs, beliefs, emotions, and thought of human psychology. It can perceive the environment and solve any real-life problem like humans.
Requirements of Artificial General Intelligence?
We already study narrow or weak AI. But, AGI is different from narrow AI. Similarly, the requirement for strong AI is also different. Here is the requirement of deep AI:
- Application of common sense
- Background knowledge
- Capacity to learn machine learning and deep learning algorithms
- Good knowledge of Statistics and modeling
- Transfer Learning and Abstraction
- Knowledge of the different programming languages.
- algorithms writing capabilities to find patterns and learning
How far are we from artificial general intelligence?
Everything is possible in theory. But, in real life, it isn’t effortless. Till now, we are at the age of narrow AI. The scientist is trying, but it is far away. Quantum computing may be the gateway of artificial general intelligence.
With the advancement of technology, we expect to get the blessing of deep AI within a few decades. Experts expected to offer the beta version of AGI by around 2030. However, based on the ongoing research, we can expect artificial general intelligence within 2060.
Does Deep AI happen? Is AGI possible?
We can assume that AGI is possible because of the cognitive stimulation of the brain. But there is a controversial issue of duplication of human intellectual abilities. However, doubt may arise because of the lack of substantial progress of deep AI. Moreover, there is no real definition of AGI relating to the human brain. Finally, the question is whether artificial general intelligence is possible or not.
Artificial General Intelligence or deep AI is a mimic similar to the human brain. We can compare it to the brain of a child. Every moment it is learning. We can see the super fiction movies of AGI. But, in the real world, it may not be possible. Scientists are trying to implement Strong AI into different solutions. We hope to see artificial intelligence within the next decade.