Spintronic quantum gates

5. Spintronic quantum gates
The idea of using a single electron or nuclear spin to encode a qubit, and then utilizing this to realize a universal quantum gate, has taken hold. The motivation for this is the realization that spin coherence times in solids are much larger than charge coherence times. Charge coherence times in semiconductors tend to saturate to about 1 ns as the temperature is lowered. This is presumably due to coupling to zero point motion of phonons, which cannot be eliminated by lowering the temperature. On the other hand, electron spin coherence times of 100 ns in GaAs at 5 K have already been reported and much higher coherence times are expected for nuclear spins in
silicon. Therefore, spin is obviously the preferred vehicle to encode qubits in solids.
Using spin to carry out all optical quantum computing has also appeared as a viable and intriguing idea. The advantage of the all-optical scheme over the electronic scheme is that we do not have to read single electron spins electrically to read a qubit. Electrical read out is extremely difficult, although some schemes have been proposed for this purpose. Recently, some experimental progress has been made in this direction, but reading a single qubit in the solid state still remains elusive. The difficult part is that electrical read out requires making contacts to individual quantum dots, which is an engineering challenge. In contrast, optical read out does not require contacts. The qubit is read out using a quantum jump technique, which requires monitoring the fluorescence from a quantum dot. Recently, it has been verified experimentally that the spin state of an electron in a quantum dot can be read by circularly polarized light. Therefore, optical read out appears to be a more practical approach.

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SPINTRONICS

The visionary who first thought of using the spin polarization of a single electron to encode a binary bit of information has never been identified conclusively. Folklore has it that Feynman mentioned this notion in casual conversations (circa 1985), but to this author’s knowledge there did not exist concrete schemes for implementing spintronic logic gates till the mid 1990s. Encoding information in spin may have certain advantages.
First, there is the possibility of lower power dissipation in switching logic gates. In charge based devices, such as metal oxide semiconductor field effect transistors, switching between logic 0 and logic 1 is accomplished by moving charges into and out of the transistor channel. Motion of charges is induced by creating a potential gradient (or electric field). The associated potential energy is ultimately dissipated as heat and irretrievably lost. In the case of spin, we do not have to move charges. In order to switch a bit from 0 to 1, or vice versa, we merely have to toggle the spin. This may require much less energy. Second, spin does not couple easily to stray electric fields (unless there is strong spin-orbit interaction in the host

material). Therefore, spin is likely to be relatively immune to noise. Finally, it is possible that spin devices may be faster. If we do not have to move electrons around, we will not be limited by the transit time of charges. Instead, we will be limited by the spin flip time, which could be smaller.

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CONFIGURATION OF TACTILE SENSING SYSTEM

This system uses conductive plasterer as a sensor, which has a property that its conductivity changes as a function of the pressure applied. Fig 1 shows the configuration of the system. The conductive elastomer is mounted on 8*8 force sensing sites for the measurement of pressure distribution on the object. These force sensing sites are connected through PC add on data acquisition card. Stepper motor is used to apply specific amount of pressure for the proper identification of the objects. Hardware for scanning the matrix and related signal conditioning is designed along with the stepper motor interface circuitry. Once the image of the object is acquired through the Tactile Sensing System, then it is further processed using Image Processing concepts for the proper identification and inferring other properties of the objects. Tactile data for the object identification is acquired using Row-Scanning technique. After the removal of noise from this tactile data, it passes to the different modules of the system for further processing.
TACTILE DATA PROCESSING

The main modules of this system are pre-processing, data acquisition, matrix representation, graphical representation, edge detection and moments calculations for generating a feature vector. Tactile image acquisition involves conversion of the pressure image into an array of numbers that can be manipulated by the computer. In this system, a tactile sensor is used to obtain the pressure data of the object and this data is further acquired with the help of data acquisition and data input/output card, which are interfaced with the computer. The pre-processing module involves in the removal of the noise, which is essential for acquiring the image of the object under consideration. The image is analyzed by a set of numerical features to remove redundancy from the data and reduce its dimensions. Invariant moments are calculated in this module, which are required by the next module to the artificial neural for the identification of the object independent to scale, rotation and position.

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TACTILE SENSING SYSTEM USING ARTIFICIAL NEURAL NETWORK

Of the many sensing operations performed by human beings, the one that is probably the most likely to be taken for granted is that of touch. Touch is not only complimentary to vision, but it offers many powerful sensing capabilities. Tactile sensing is another name of touch sensing, which deals with the acquisition of information about the object simply by touching that object. Touch sensing gives the information about the object like shape, hardness, surface details and height of the object, etc.
Tactile sensing is required when an intelligent robot wants to perform delicate assembly operations. During this assembly operation an industrial robot must be capable of recognizing parts, determining their position, orientation and sensing any problem encountered during the assembly from the interface of the parts. Keeping all these things in mind, a Tactile Sensing System is developed which uses image processing techniques for pre-processing and analysis along with ANN’s for object identification. Classification of the object independent of translation, scale and rotation is a difficult task. The concept of Artificial Neural Network is used in this system for the proper identification of the object, irrespective of their size, position and orientation.

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Applications of Speech Recognition

The specific use of speech recognition technology will depend on the application. Some target applications that are good candidates for integrating speech recognition include:

Games and Edutainment

Speech recognition offers game and edutainment developers the potential to bring their applications to a new level of play. With games, for example, traditional computer-based characters could evolve into characters that the user can actually talk to.
While speech recognition enhances the realism and fun in many computer games, it also provides a useful alternative to keyboard-based control, and voice commands provide new freedom for the user in any sort of application, from entertainment to office productivity.

Data Entry

Applications that require users to keyboard paper-based data into the computer (such as database front-ends and spreadsheets) are good candidates for a speech recognition application. Reading data directly to the computer is much easier for most users and can significantly speed up data entry.
While speech recognition technology cannot effectively be used to enter names, it can enter numbers or items selected from a small (less than 100 items) list. Some recognizers can even handle spelling fairly well. If an application has fields with mutually exclusive data types (for example, one field allows "male" or "female", another is for age, and a third is for city), the speech recognition engine can process the command and automatically determine which field to fill in.

Document Editing

This is a scenario in which one or both modes of speech recognition could be used to dramatically improve productivity. Dictation would allow users to dictate entire documents without typing. Command and control would allow users to modify formatting or change views without using the mouse or keyboard. For example, a word processor might provide commands like "bold", "italic", "change to Times New Roman font", "use bullet list text style," and "use 18 point type." A paint package might have "select eraser" or "choose a wider brush."

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SPEECH RECOGNITION

One of the most important inventions of the nineteenth century was the telephone. Then at the midpoint of twentieth century, the invention of the digital computer amplified the power of our minds, enabled us to think and work more efficiently and made us more imaginative then we could ever have imagined .now several new technologies have empowered us to teach computers to talk to us in our native languages and to listen to us when we speak(recognition); haltingly computers have begun to understand what we say. Having given our computers both oral and aural abilities, we have been able to produce innumerable computer applications that further enhance our productivity. Such capabilities enable us to route phone calls automatically and to obtain and update computer based information by telephone, using a group of activities collectively referred to as Voice Processing.

SPEECH TECHNOLOGY

Three primary speech technologies are used in voice processing applications: stored speech, text-to – speech and speech recognition . Stored speech involves the production of computer speech from an actual human voice that is stored in a
computer’s memory and used in any of several ways. Speech can also be synthesized from plain text in a process known as text-to – speech which also enables voice processing applications to read from textual database. Speech recognition is the process of deriving either a textual transcription or some form of meaning from a spoken input.
Speech analysis can be thought of as that part of voice processing that converts human speech to digital forms suitable for transmission or storage by computers. Speech synthesis functions are essentially the inverse of speech analysis – they reconvert speech data from a digital form to one that’s similar to the original recording and suitable for playback.Speech analysis processes can also be referred to as a digital speech encoding ( or simply coding) and speech synthesis can be referred to as Speech decoding

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Wireless Application Testing

Wireless applications are booming in the modern PDA and cell phone market. Standalone applications and value-added extensions of existing applications are both excellent ways to attract customers and increase the value of the hardware. Many companies use these applications as a way to retain customers and improve customer experience and satisfaction.Testing these applications has continued to be a road block for many developers. Difficulty insecuring all platforms, and testing the many disparate configurations is cumbersome and time consuming.
2.TYPES OF TESTING:
a) Functional Testing
A set of functional tests will be developed based on client documentation. Functional tests verify that the application performs the tasks correctly and accurately as explained in the client documentation. Dedicated QA Engineers will write test cases for each component of functionality and execute those tests. Tests will be clearly defined prior to execution, with an emphasis on actions and expected corresponding behaviors.
b) Compatibility Testing

The wireless market has a myriad of devices available. The differences in each device can be extensive. Each device can have a different operating system, support different technologies, and have different hardware. Presenting a similar end-user experience on each device is challenging. Generally, each platform requires a separate version of the application, and in some cases, the same platform may have different versions for each different device. This often happens when developing applications for cell phones. Different screen sizes/resolutions and differences in other physical hardware often make it necessary to have different versions for each platform.

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