Despite advances in technology, computers still can’t come close to the power of the world’s most remarkable computer – the human brain.

Computing machines have seen three phases: the tabulating phase, the programmable phase, and now the new era of computing, the cognitive phase [1]. Tabulating machines performed a fixed task, whereas programmable machines could be reprogrammed to execute different tasks without any change in the hardware. Cognitive machines, however, promise learning and reasoning capabilities.

The possibility of such machines raises the question: is cognition the ultimate test for conscious existence? What do we know about cognition? How do we define intelligence? The questions go on and on. One thing, however, everyone seems to agree with is the fact that understanding the mechanism of human cognition is the key to developing advanced artificial intelligence, and cognitive machines.

Cognitive machines have the ability to learn, and they employ artificial intelligence to “reason.” Artificial intelligence is defined as, “The science of making machines do things that would require intelligence if done by men” [2]. The good news is that cognitive computing is no longer an esoteric pursuit of some futurists. It is, and has been, essential for the operation of many big-data driven processes. The 21st century has been flooded with data coming from almost every aspect of our lives. Technology has made it possible to generate data at an exponential rate. Temperature distribution throughout our buildings, the number of people diagnosed with cancer in the last six months, real-time changes in customer preferences, ethnic profiles of college applicants, and the top three words trending in online conversations at this very moment, are some examples of the kind of data available today.

As the amount of data generated increases, it also becomes harder and harder to process and make sense of the data collected. Our “greedy and ambitious” human nature does not want to waste, and it wants to use every bit of available data. This is where it becomes imperative to have a computing machine that goes beyond performing pre-programmed tasks and learns as it goes, without human interference. For this kind of computing, we need cognition.

The biggest challenge in imitating human cognition is to understand how cognition happens in the brain. It is obviously beyond our ability to monitor such activity that is constantly taking place in our brains, let alone recreating such marvels in the first place. Nevertheless, it will be a great achievement if we can manage to somewhat imitate human cognition, even partially. It would open a whole new era in terms of what can be achieved from a computing standpoint. For instance, the entire curriculum of a college degree can be processed by a cognitive machine in a fraction of a second; such machine can digest the whole of medical literature in a short period of time, provide human doctors with second opinions on their diagnoses [3].

Despite the fact that there have been substantial improvements in designing “intelligent” computing machines, mimicking the hardware of the human brain and simulating its decision-making processes, have posed three fundamental challenges: a hardware with comparable processing power and memory, a software algorithm to implement intelligent behavior, and the necessity of both being self-adapting and self-improving.

First of all, human intelligence has not been fully characterized – its capabilities and limitations are still unknown. This lack of knowledge makes it difficult, and perhaps even impossible, to reduce such intelligence to smaller, or simpler, modules. Therefore, we don’t have a good handle on how to mimic the human brain in a behavioral sense.

The second major problem is that we are still far away from having the hardware on which our “intelligence” software could run. Implementing intelligence in conventional computing machines, evidently, seems to be a futile undertaking. Programmable machines are no match for human brains; even the fastest supercomputers, taking advantage of thousands of processors, is able to mimic just one percent of one second worth of human brain activity-and even that takes 40 minutes [4]. Therefore, cognitive computing machines must incorporate different hardware architecture from conventional computers to achieve cognition comparable to humans. IBM’s SyNAPSE chip is one example of hardware inspired by the brain, and it has the potential to carry out the required, intense computations.

Lastly, the human brain and its cognitive power are constantly changing. Depending on various factors and experiences, our brains can improve or deteriorate; this is also true of our cognitive power. However, such improvement or deterioration could be in the form of a change in the physical structure or the amount of capacity utilized [5]. Such dynamic flexibility, also called Brain Plasticity [6], is essential to our intelligence. At this time, no self-evolving computing hardware has been worked out. However, promising developments have been reported with respect to cognitive computing machines that can learn – that is, they can make deductions and reach conclusions that are not preprogrammed.

Along the way, human supervision will be the ultimate guide in perfecting such imitation. Therefore, human cognition, taken for granted in our daily lives, remains to be the final frontier for our thousands-years long technological journey. Once again, the creation set the boundaries for human development.

Adem G. Aydin holds a Phd degree in Electrical and Computer Engineering. He works as an engineer scientist at IBM.

References

[1] Virginia Rometty, 2013, http://smarterplanet.tumblr.com/post/32816006311/i-b-m-chief-on-watson-cognitive-computing-and-her

[2] Marvin Minsky, 1968, http://www.akri.org/ai/defs.htm

[3] “WellPoint and IBM Announce Agreement to Put Watson to Work in Health Care”, http://www-03.ibm.com/press/us/en/pressrelease/35402.wss

[4] “Largest neuronal network simulation achieved using K computer” http://www.riken.jp/en/pr/press/2013/20130802_1/

[5] William James, The Principles of Psychology

[6] Bryan Kolb and Ian Q. Whishaw, Brain Plasticity and Behavior, Annual Review of Psychology, Vol. 49: 43-64

It was only a decade ago that a phenomenon called the “Internet” came along and changed the way we communicated, did business and conducted our lives. Now, computer technology has become an essential and significant part of our daily lives. But how did it all start and where is it heading?

I entered the world of computers at an early age. I had an Atari 800 XL, the third version of the Atari introduced in 1983, and I was doing some basic programming. The computer contained a full 64K of memory, 1.8 MHz processing power (CPU), and looked like a bulky keyboard. I enjoyed spending a lot of time with games and programming; however I felt very limited with its capabilities. After more than 20 years, from time to time I still feel the same about my 2.8 GHz eight-core desktop computer with 8 GB of memory. While the computers are getting faster and more powerful, our need for computing power is also increasing just to complete our daily tasks at home and work. I always wondered how it all started and where we are heading with computer technology.

Over forty years ago, Gordon Moore, Intel Co-founder, predicted the number of transistors incorporated in a chip to double every 24 months. This is popularly known as Moore’s law, and used countless times by futurists [1] and science fiction writers. For the last half century, computer’s functionality and performance increased in line with Moore’s law, while costs are decreasing. However, fundamental barriers in semiconductor technology are emerging including the power needs and limits of manufacturing in atomic dimensions. Computer industry is already working on technologies to keep Moore’s Law alive.

Intel is already experimenting with 3D transistors that are smaller, faster and more energy efficient using a 22nm (nanometer, 10-12 m) manufacturing process compared to today’s 32nm systems. This will be a significant step forward to build more transistors onto silicon chips. Another approach will be by replacing the silicone in transistors. In 2010, IBM showcased a graphene transistor running at 100 GHz, with a potential of up to 1000 GHz. A graphene layer is only one atom thick with a honeycomb-like structure of carbon atoms. The graphene has unique electrical, optical, mechanical and thermal properties with promising applications in many industries.

In 1971, a theoretical prediction was made for the missing link in electronics, memristor, a fourth element to supplement resistor, capacitor and inductor, which form the basis of today’s electronic devices. HP’s demonstration in 2010 shows a resistor with memory that remembers the electric voltage applied even when the power is turned off. Requiring very little energy to store information promises to run ten times more power efficient and ten times faster than current counterparts.

In 1994, Leonard Adelman [2] proposed DNA computing to solve the famous, “shortest path problem”. Since then, many approaches have been made to utilize the properties of DNA for computing. In 2010, researchers at California Institute of Technology demonstrated a DNA computer, most advanced to date, which can calculate square roots. This approach could be put in use inside living organisms, and perform vital tasks such as disease detection. The promise of DNA computers depends on the parallel processing capabilities of DNA molecules that can try many possibilities at once with low power requirements [3]. Further advancements in DNA based computers in the coming decades will bring faster and low-powered computers to our daily lives.

In 1981, the famous physicist Richard Feynman speculated the possibility of computers obeying quantum mechanical laws that might best simulate the real-world quantum systems. This is a big challenge even for today’s fastest supercomputers. Since then, researchers are pacing towards building quantum computers that rely on quantum mechanics to conduct operations. Quantum computers can use properties like entanglement [4] and qubits. In entanglement, particles behave identically independent of the distance between them, and qubits act as both memory and state of the entanglement. Shor’s Algorithm, formulated by Peter Shor in 1994 for prime factorization is a powerful example of quantum computing that allows breaking encryption algorithms like RSA encryption more effectively and quickly than today’s super computers. Quantum computers can perform at much higher speeds than traditional computers, and are able to solve more complex problems. Recent developments in quantum computing such as quantum photonic chips [5], and first commercial quantum computer by D-Wave shows that we might be closer than we think to have our very own quantum computer in the coming decades.

While greater shift in computing might stem from the change in underlying technology in processing the information, computer form factors (desktop, laptop, tablet, phone, etc.) have a more direct effect in our daily use of computers. Human computer interaction changed significantly with the introduction of smartphones (e.g. iPhone, Android phones), and tablets (e.g. iPad), moving from keyboards and mouse as the primary input methods towards touch screens.

Today’s touch screens, although providing infinite ways of input structures available on their screens, lack tactile feedback when compared to keyboards. New keyboard designs with small screens on each key opens infinite customization of the input, but are still limited to initial design of the key (e.g. usually cubic). Recent developments in touchscreen designs enable users to feel clicks, vibrations and other tactile input by using “haptic technology”. Haptic technology takes advantage of the user’s sense of touch and provides feedback by applying forces, vibrations, or motions to the user. As seen in early prototypes, flexible screens and electronics will provide a more realistic feel for human computer interaction by shifting their forms to a specific shape (e.g. game pad, key, and wheel) in the coming decades.

Another level in human computer interaction even eliminates user’s touch. Apple introduced Siri, a smart virtual assistant, in 2011 as a part of their iOS operating system for iPhones and iPads. Siri is capable of analyzing user’s complex audio inputs to carry out many tasks including scheduling a meeting, creating a reminder, typing and sending SMS messages, and many other functions available in smart phones. Microsoft introduced Kinect in 2010, a motion-sensing device that enables users to control and interact with the game console using gestures and spoken commands. The Kinect interprets specific gestures by using an infrared projector and camera to track the movement of objects and individuals in three dimensions.

Samsung introduced a 46’’ transparent display using LCD technology in 2010, and demonstrated flexible displays in CES 2011. Transparent and flexible displays will easily find use in wearable electronics such as contact lenses and glasses. An obvious application of transparent display is Augmented Reality (AR) where information is displayed on top of real world images. While today’s smartphones and tablets allows augmented reality by combining information with the real-time video feed from the camera of the device, transparent display eliminates the need of using camera. Current AR technology includes head-mounted displays and virtual retinal displays for visualizing the information. It is widely applied in various areas including entertainment, advertising, game industry, navigation, education, military applications, and information sharing.

While having larger, transparent, and flexible displays in different forms, one direction in display technologies is to reduce the size or even eliminate the display through projection and holograms. Current trends in projectors include 3D projection, synchronization of multiple projections, and pico projectors. The world’s smallest glass lens (1mm x 1mm) introduced in 2011 will help minimize some of the problems of projectors such as size, power and heat, and improve their integration in smartphones and tablets.

Recent prototypes of holographic displays progressed significantly demonstrating 3D and full color animated images, since its first introduction at the MIT Media Lab 1989 [6]. While developments in 3D displays are promising, holography provides the best 3-D experience since it is closest to how we see our environment. A hologram uses an optical effect called “diffraction” to produce the light that would have come from an object, and makes the image of the object appear in front of the viewer. It is possible to view objects from different angles in holographic display by walking around them. Unlike other 3D display systems, holographic displays do not require special glasses for viewing and allows multiple viewers to experience the view from different angles at the same time. Future applications of holography can be implemented in health, entertainment and communication sectors, from 3D movies to telepresence applications.

All of the above examples and trends in computers deal with the computing as a product. Cloud computing can be defined as the delivery of computing as a service where shared resources, software, and information are provided over a network. Cloud computing describes a new delivery and consumption model that allows dynamic scalability and virtualization of resources. Users can dynamically upgrade the storage and computing power from virtualized resources on demand without hardware changes on the base system. Organizations can save from investing on expensive hardware, and human capital.

Many technologies from coming centuries are featured in science fiction books and movies such as Minority Report, Star Trek, and Star Wars. While some of them are already available to consumers, others might require decades to come. Motivation for the advancement in computer technologies usually stems from our needs and desires. At the same time, new technologies significantly impact consumer behavior and increase our dependence on new technologies. Our economy is structured such that all citizens have to consume more and more, even if that means disposing of perfectly good technological devices. Do we really need a new computer or phone every year? Most of us don’t.

Computer technologies are a significant part of our daily lives, and the line between the products and services is becoming thinner with the dependency on computers increasing in every aspect of our life. While improving the quality of our lives by making our daily tasks easier, computers and Internet technologies can affect us in different ways. It has already started to change how we read, write and even communicate with others. Many concerns are raised about the negative effects of the use of technology including Internet addiction, privacy, attention span, concentration, time consumption, anxiety, isolation, depression, digital security, communication disorder and various health issues. The challenge for us is to understand the benefits of the technology, have a balance in dependence and its use, and protect ourselves from its adverse effects.

Acknowledgment: This article is produced at Mergeous [7], an online article and project development service for authors and publishers dedicated to the advancement of technologies in the merging realms of science and religion.

References

1. Kaku, Michio. 2011. Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100, Knopf Doubleday Publishing Group.

2. Demir, Halil I. 2011. Super Computers in a Cell, The Fountain, Issue 80, March – April.

3. Adleman, Leonard M. 1994. “Molecular Computation of Solutions to Combinatorial Problems,” Science, 266 (11), 1021–1024.

4. Demir, Halil I. 2011. Quantum Worlds from Entanglement to Telepathy, The Fountain, Issue 84, November – December.

5. Shadbolt, P. J. et al., Generating, manipulating and measuring entanglement and mixture with a reconfigurable photonic circuit, arXiv:1108.3309v1 [quant-ph].

6. Hilaire, P. St., S. A. Benton, M. Lucente, M. L. Jepsen, J. Kollin, H. Yoshikawa and J. Underkoffler. 1990. “Electronic display system for computational holography.”In Practical Holography IV, Proceedings of the SPIE, volume 1212-20, pp. 174-182, Bellingham, WA. 7. Mergeous, Online article and project development platform, http://www.mergeous.com

ENIAC had cost around $500.000 and was capable of 5000 simple operations per second. Today, basic personal computers (PC) cost around $500 with enough processing power to perform millions of operations per second. An average PC is enough in terms of computing power for everyday use like word processing, checking emails, and computer games. However, some areas in scientific research require computers at the frontline of current processing capacity, called Super Computers. Twice a year, the TOP500 project, which started in 1993, ranks and publishes details of the 500 most powerful super computers in the world. The IBM Roadrunner, located at Los Alamos National Laboratory, was announced as the fastest supercomputer in the world as of May 2008.

In computing, “flop” (Floating Point Operations Per Second) is a measure of a computer’s performance which is similar to calculations per second. The IBM Roadrunner had cost $133 million and had a peak performance of 1.7 petaflops, which is around 1.7×1015 operations per second. The Roadrunner was delivered on 21 tractor-trailer trucks to its current location. Supercomputers are an essential component of research in areas like computational biology, fluid dynamics, structural mechanics and cancer research, which requires high computing power.

While computers get faster every year, their computing power is way behind when compared to a human brain. They consume hundreds of times more energy than a brain. It is estimated that a computer will be able to simulate a human brain in seven years, yet we are decades away from expecting a computer that can think like a human and make decisions. The brain is one of the most miraculous parts of the human body, full of mysteries. It works more efficiently than any machine developed in the last 50 years of the computer history.

However, the human brain is not the only body part which has an incredible computing power. In 1994, Leonard M. Adleman, a professor at the University of Southern California, introduced the idea of using DNA (Deoxyribonucleic Acid) to solve computational problems [3]. This idea then led to a new field of science, called DNA Computing, which combines two disciplines, biology and computer science, to build the fastest and smallest computers ever. DNA is known as the blueprint of life, with unique properties such as self-assembly, molecular recognition, minute size and high information density.

Computationally challenging problems have known solutions, but enormous amounts of resources (time and/or cost) are required to find the optimum solution. Some problems, such as optimization, can be solved by generating many possible solutions, and then selecting the optimum one. Standard computing methods can generate or test a possible solution one at a time. On the other hand, parallel computing methods can carry out this process simultaneously for thousands of possible solutions.

Similarly, enzymes can work in parallel for replicating and repairing DNA strands. They can even work on the next strand before the first one is replicated. An enzyme can replicate a DNA strand 500 times in a second, which is equal to 0.001 MIPS (million instructions per second). The computations in DNA can reach to 1014 MIPS, while a modern computer runs at an average of 1000 MIPS. DNA computing is not only faster in processing, but also much more efficient in energy consumption. The energy consumption of a DNA operation (on one strand) is about 1010 times less than the energy consumption of an operation on modern computers.

The Traveling Salesman Problem (TSP) is one of the most studied problems in computational mathematics. Here is an example of the problem: a traveling salesman needs to visit 20 cities once, with predefined starting and ending locations, and certain rules. The complexity of the TSP problems increases exponentially with the number of cities, so problems with only hundreds of cities will take thousands of years to solve by modern computers. If there are 18 factorial possible paths in this problem, it will take 2 whole years for a computer with 100 MIPS of processing power to generate the possible paths and find the correct answer. However, all possible paths can be generated in a very short time by using DNA computing. A simplified version of the Traveling Salesman problem presented by Adleman involves the following scenario:

A salesman wants to visit the cities (Figure 3) Monroe, Gainesville, and Conyers, starting from Athens, and arriving at Atlanta last. Each city should be visited only once. The cities are not fully connected. While some cities are connected to another in one direction, others are connected in both directions. Our objective is to find the shortest route to visit all cities once. The solution for this problem is a travel from Athens -> Gainesville -> Monroe -> Conyers -> Atlanta.

When we convert the problem to a molecular language, each city is coded as a single-stranded DNA molecule with 8 nucleotides. We can think of nucleotides as bytes in computer programming, which will take the value 0 or 1. Nucleotides exist as four bases: adenine (A), thymine (T), guanine (G) and cytosine (C). All cities are coded with eight nucleotides as follows:

City Code

Athens ATGC CATG

Gainesville TCAG GTCA

Monroe GACT TGAC

Conyers CGTA ACGT

Atlanta AGCT TAGC

Connections between two cities are coded with the last 4 nucleotides of the departure city and the first 4 nucleotides of the arrival city. For example, the connection between Athens (ATGCCATG) and Monroe (GACTTGAC) is coded as CATGGACT. The complementary codes for connections (the Watson-Crick complements), where every C is replaced by a G, every G by a C, every A by a T, and every T by an A, and connection codes are given below:

Connection Code Complementary Code

Athens – Gainesville CATG TCAG GTAC AGTC

Athens – Monroe CATG GACT GTAC CTGA

Gainesville – Atlanta GTCA AGCT CAGT TCGA

Gainesville – Monroe GTCA GACT CAGT CTGA

Monroe – Conyers TGAC CGTA ACTG GCAT

Conyers – Atlanta ACGT AGCT TGCA TCGA

Conyers – Monroe ACGT GACT TGCA CTGA

The mixture for performing reactions will include DNA strands and their complements for 5 cities and 7 connections between cities in our example. If an Athens molecule (ATGC CATG) encounters the complement strand of an Athens-Monroe (GTAC CTGA) connection in the mixture, a hydrogen bond will be formed between strands (Figure 4). Other strands will continue forming bonds for valid connections between cities, building a complete travel path with the help of DNA ligase. There needs to be enough copies of each DNA strand to generate all possible travel paths.

ATGC – CATG

| | | |

GTAC – CTGA

Polymerase Chain Reaction (PCR) will be used to make multiple duplicates of DNA strands containing Athens (start) and Atlanta (end) cities. The result of PCR will be the amplification of correct travel from Athens to Atlanta, which makes it easy to separate. PCR is a method by which a few strands of DNA can be copied into millions in a very short amount of time. This also makes PCR a very important method to increase small amounts of DNA found in blood or hair samples, which could be enough to carry out analysis and reveal a person’s identity in forensic science.

Electrophoresis follows the PCR process to sort the resulting paths according to their sizes. Since every city is coded with 8 nucleotides, the correct path should include exactly 40 nucleotides representing a full path for 5 cities. The gel electrophoresis process uses an electric field to separate DNA strands by size as they travel through a gel matrix. The speed of DNA molecules differs by their size, which results in the sorting of the molecules by size. After this step, DNA strands starting with the code of Athens, ending with the code of Atlanta and with a size of 40 nucleotides are separated from the mixture.

The last step will be reading the code and removing the DNA strands that didn’t contain all the cities. Adleman used a common method known as affinity purification for the separation process. Finally, the mixture has the DNA strands with the correct travel path, starting from Athens and arriving to Atlanta, and traveling through every city once. All the laboratory work looks complex for this simple problem, but as a new concept for computing, it is revolutionary. In its ability to perform parallel computations, DNA computing shows great promise over traditional computing approaches.

Data density is another unique advantage of DNA. Billions of DNA strands can be stored in a regular laboratory tube. A DNA strand is composed of bases A, T, C and G spaced evenly, 0.35 nanometers apart from each other. The data density of DNA is around 106 GB (gigabytes) per square inch, which is 100,000 times larger than the data density of today’s storage technologies (7 GB per square inches). Moreover, DNA is a durable and strong molecule; the information stored within it can be kept for thousands of years in the right conditions. In 2008, 80% of the woolly mammoth genome, several thousand years old, has been identified from tufts of frozen woolly mammoth hair [4].

DNA is also created with remarkable mechanisms such as built-in error correction. The double stranded nature of DNA provides a double check on pairing. Error repairing enzymes are always ready to search for anomalies during the DNA replication process. It results ina ratio of one error per billion replications. DNA is located and protected at the center of each cell with a perfect balance. The miraculous architecture of DNA has waited for thousands of years to be understood by humans and be used for the benefit of the world. Further studies on DNA might open new opportunities to help researchers in solving technologically challenging problems.

Acknowledgment: This article was produced at MERGEOUS [5], an online article and project development service for authors and publishers dedicated to the advancement of technologies in the merging realms of science and religion.

Halil I. Demir is a postdoctoral scholar in the area of Informatics, and lives in Iowa.

References

[1] ENIAC, Image Credit: Wikimedia, http://upload.wikimedia.org/wikipedia/commons/4/4e/Eniac.jpg

[2] IBM Roadrunner, Image Credit: Wikimedia,

http://upload.wikimedia.org/wikipedia/commons/c/c7/Roadrunner_supercomputer_HiRes.jpg

[3] Leonard M. Adleman (1994-11-11). “Molecular Computation of Solutions to Combinatorial Problems.” Science, 266 (11): 1021–1024.

[4] Miller, W (et al). 2008. “Sequencing the nuclear genome of the extinct woolly mammoth”, November, Nature.

[5] Mergeous, http://www.mergeous.com

How about quantum computers? Quantum computers make use of a quantum mechanical phenomenon, so-called quantum superposition (being in different states simultaneously). Classically, voltage across a circuit element can be either positive or negative when measured by a voltmeter, but not simultaneously negative and positive. Could it somehow be possible to be in both states simultaneously?

Quantum superposition

For electrical circuits, the answer is obviously no. In microscopic scales of single atoms, or photons (i.e., single quantized packets that constitutes the light beam), however, the answer is yes. Consider an optical component, for instance, that splits an incoming light beam into two beams of equal intensity. In optics, such a device is called a 50/50-beam splitter. You can ask what happens when a single photon is sent to such a beam splitter. Since a single photon cannot be split in this simple experiment, you might expect that it would either be transmitted or reflected with equal probability . Experiments, however, show that this is not actually true in the single photon level. The single photon is indeed simultaneously reflected and transmitted.

Once microscopic quantum superposition is brought into our macroscopic world, we can imagine many interesting phenomena. Simultaneously occupying many different places and being dead and alive at the same time are only two of them. Of course such technology, especially applied to humans is highly science fiction, given current experimental and theoretical challenges. Nevertheless, quantum superposition has a strikingly interesting similarity with the spiritual states already achievable by saints, such that they can be available in more than one place at a given time or become dead and alive, in the sense that they live both in the future and in the past.

It is not known exactly why quantum superposition exists, but what we know is that it is a necessary ingredient for our complex universe to perform its vital functions in a finite amount of time. Quantum superposition principle reflects the great wisdom and power of the Omnipotent. Similar to the single photon example above, with this principle, God gives the underlying particles of the universe an immense power to achieve many tasks simultaneously. Otherwise, regarding the finite age of the universe (about 15 billion years), our physical universe and the events taking place all around us would not come into existence. The Quantum superposition principle has also inspired researchers to build unprecedentedly fast computers to solve the problems that are intractable with any classical computing method. In this article, we introduce this new strategy to computing.

Quantum computing with superposition

Having provided some background about the quantum superposition, we ask the question “How could we exploit quantum superposition for fast computing?” Below we will give a glimpse of that power. Consider a three-bit register. It can only store one out of eight numbers in the set, {0, 1, 2, 3, 4, 5, 6, 7}, in a given moment of time. For example, number 5 is stored in a three-bit register as “101.” Now suppose that these three bits are replaced by their quantum cousins, so-called qubits (short for quantum bit). You can imagine, for example, a quantum register consisting of three rubidium (Rb) atoms. These individual atoms can be prepared in the 0 and 1 logical states simultaneously by shining a laser beam for a certain amount of time. Then it is possible for three atoms combined to be prepared in a superposition of eight numbers, which is impossible classically. In other words all those eight guys physically exist in the same room, although it doesn’t allow more than one guy to fit classically. If we want to make operations on all of these numbers, we don’t need to perform serially; instead, we can achieve that in only one computational step on a single hardware. Thus, quantum superposition leads to a massive parallelism, which renders the computational complexity (i.e., a measure of how efficiently a given problem could be solved) highly reduced for various difficult problems in computer science.

For example, let’s consider RSA, a well-known algorithm (i.e., a set of instructions to solve a problem on a computer) for secure communication that was invented by Rivest, Shamir, and Adelman, hence the name, in 1977 at MIT. It is widely used in electronic commerce protocols. The details of RSA are out of scope in this article (See the FAQ section of the RSA Laboratories’ web site in Ref. [1] for a brief introduction to RSA). Here, we only want to mention its vulnerability to quantum computers if they were to exist. The security of the RSA cryptosystem relies on the difficulty of factoring large numbers, which is intractable with classical computers. Factorization for small numbers, say 15, is quite simple. When the number of digits increase up to a few 100s, for example, enormous computational resource is required. RSA Laboratories publish the RSA challenge numbers (see Ref. [2] for the list of challenge numbers and the prize) on their web site to test the security of their algorithm at various key lengths. The largest integer, RSA-640, which has 193 decimal digits (640 bits), was factorized recently by F. Bahr, et al. The next challenge number in turn is RSA-704, and the prize is $30,000. Imagine factorizing a 1000-digit number. You would probably be a considerably rich person in just a few minutes, if you had a moderate quantum computer and the RSA Laboratories kept feeding you with new challenge numbers, because the factorization of such a large number with current computational resources takes forever, perhaps even more than the estimated age of the universe. Of course, the RSA Laboratories will not let you be very rich, by simply quitting posting new challenge numbers. They would be interested in your quantum computer, though.

How does the quantum computer crack the world’s most secure cryptosystems with little effort? One can construct new algorithms for quantum computers based on above described principle of superposition. These algorithms can take the outcome of previous calculations and input them as a superposition to the next stage of the instructions, which results in a highly efficient form of computing (please consult Ref [3] to have for a simple explanation of quantum superposition for fast computation). In 1994, Peter Shor from AT&T’s Bell Labs in New Jersey just did that. He developed the world’s first quantum algorithm, which efficiently performs factorization. In 1996, Lov Grover also at Bell Labs invented the unstructured database (i.e., a disordered list such as a list of city names not in alphabetical order) search algorithm for quantum computers, so-called Grover’s algorithm.

Suppose that there is a basket with ten balls in it. You are now asked to find a specific one with your eyes closed, say red. It is known, however, beforehand that there is only one red ball in the basket. All you, or your smart digital friend, can do is just pick one randomly and see if it is red. If you are lucky enough, the first ball you pick might be red. In the worst case, however, you will be successful at your last choice. So, classically you have to repeat the process on average at half times the number of balls. If you made a quantum friend rather than classical, however, your life would be smoother. You would be able to find and manage your stuff easily, no matter how messy you are. Quantum computers speed up such unsorted database searches quadratically. You can find, say your favorite socks, in a number of trials that is about the square root of the total number of your stuff. You may think that you don’t have that much stuff. But consider identifying a specific element in a considerably large pool of unsorted data. As the number of elements in the set increases, it quickly becomes intractable to find what exactly you are looking for. In that case the significance of quadratic boost cannot be denied.

Motivated by the above mentioned factorization and unsorted database search algorithms, the power of quantum computing has inspired great attention, since their invention, among many disciplines including physicists, computer scientists, mathematicians, engineers, and material scientists.

Quantum computer today

Despite promising developments in theory, progress in the physical realization of quantum circuits, algorithms, and communication systems have been extremely challenging to date. There are many approaches for quantum information processing. Major model physical systems include nuclear spins, ions, neutral atoms, solid state nanostructures, superconductors, and optical circuits. In optics, for example, the qubit can be represented by the polarization (i.e., direction of oscillation of electric field) state of a single photon. So that the instructions described by the algorithm could be implemented by manipulating the polarization states of single photons. Unfortunately, all the models for quantum computing have their own drawbacks besides their advantages.

Given the trends, nobody knows whether or not a sufficiently scalable (i.e., large enough to harvest its potential power) quantum computer would be available in the decades to come. Nonetheless, D-Wave Systems, Inc., The Quantum Computing Company, was eager enough to unveil the “world’s first commercially viable quantum computer” (see Figure 1, and Ref [4] for the story.). D-Waves’ 16-qubit quantum computer makes use of superconducting element niobium, which operates at an extremely low temperature. It can search for molecular structures that match a target molecule, create a complicated seating plan, and fill in Sudoku puzzles. Although the device is very slow compared to an inexpensive PC, D-Wave intends to develop a 1000-qubit quantum computer.* The goal is to scale the quantum computer to about 10 thousand qubits to solve the most challenging problems outright, which are simply intractable with classical computers. The researchers, however, are not very optimistic. Prof. Lloyd of Massachusetts of Institute of Technology, a pioneering scientist in superconducting approach for quantum computing that underlies the D-Wave’s quantum computer, says “It’s too good to be true.”

Once quantum computers of reasonable power are built, the world will be unimaginably exciting and perhaps scary too. When the first commercial computer, Universal Atomic Computer I (UNIVAC I) (see Figure 2), was shipped to the United States Air Force in 1952, nobody was indeed aware of what this fat guy would lead to in our social, economical, political, and psychological life. Its descendants, however, are now inevitable parts of our lives. They are helping us in many aspects of daily life. Controlling machines, sending electronic mail, scheduling our plane tickets, communicating with our best friends, playing games, making our payments are only some of them.

In this article we only sketched the quantum superposition principle as an important ingredient for quantum computation. This is certainly not the whole story. “Entanglement” [6], for example, is another complementary resource for quantum computing and communications, as well as quantum mechanics to test its foundations.

Contrary to its classical counterparts, the power of quantum computers indeed comes directly from our granted capability of tailoring and mimicking the amazing design hidden in the microscopic world of atoms, photons or other quantum particles. Quantum computers sooner or later will bring the most science-fiction into reality. They will play a significant role especially in the development of ultra-intelligent machines and robots superior to classical ones, and communication systems whose ultimate security is guarantied by the nature’s architecture which was lay down by God. Quantum computers will reveal to us the deepest secrets of our Creator embedded in our universe, which cannot be explored using conventional computers. That day, the future will only be lacked by our limited imagination.

Acknowledgment

This article was produced in MERGEOUS [7], an online article and project development service for authors and publishers dedicated to the advancement of technologies in the merging realm of science and religion.

Omer D. Ikramoglu is a freelance writer in optics and quantum physics.

References

1. RSA Laboratories, http://www.rsa.com/rsalabs/

2. RSA Challenge Numbers, http://www.rsa.com/rsalabs/node.asp?id=2093

3. A short introduction to quantum computation by A. Barenco, A.Ekert, A. Sanpera and C.Machiavello from La Recherche, November 1996. http://cam.qubit.org/articles/intros/comp.php

4. J. R. Minkel, “First “Commercial” Quantum Computer Solves Sudoku Puzzles”, Scientific American, Feb 13 (2007).

5. UNIVAC I, http://en.wikipedia.org/wiki/UNIVAC_I

6. S. Candaroglu, “Quantum Entanglement: Illusion or Reality?” Fountain, Issue 61 (January-February 2008).

7. http://www.mergeous.com/

* At the time of writing D-Wave Systems had only 16-qubit quantum chip and they were intending to develop a 1000-qubit quantum computer by the end of 2008. Although they couldn’t meet their goal, they now have a design of a 128-qubit most powerful ever quantum chip which awaits the tests (see http://www.dwavesys.com for up to date information).