Bit and Qubits
Your computer represents the culmination of years of technological advancements but it's fundamentally no different from its 30 ton ancestor, (17,468 vacuum tubes, 7,200 crystal diodes, 1,500 relays, 70,000 resistors, 10,000 capacitors,..). Although computers have become more compact and considerably faster in performing their task, the task remains the same: to manipulate and interpret an encoding of binary bits into a useful computational result. A bit is a fundamental unit of information, classically represented as a 0 or 1 in your digital computer. Each classical bit is physically realized through a macroscopic physical system, such as the magnetization on a hard disk or the charge on a capacitor. Herein lies a key difference between your classical computer and a quantum computer: whereas digital computers confine bits of information to either 1 or 0, quantum computers harness the strange laws of quantum physics to achieve "qubits" of information. Unlike bits, qubits can represent more than one number at a time, remaining in an indeterminate state until it is observed, like a tossed coin that is still spinning.
Quantum Computing
Quantum computers (QCs) use quantum mechanics (QM), the rules that underlie the behavior of all matter and energy, to accelerate computation. It has been known for some time that once some simple features of QM are harnessed, machines will be built capable of outperforming any conceivable conventional supercomputer. QCs are not just faster than conventional computers. They change what computer scientists call the computational scaling of many problems.
In 1936, mathematician Alan Turing published a famous paper that addressed the problem of computability. His thesis was that all computers were equivalent, and could all be simulated by each other. By extension, a problem was either computable or not, regardless of what computer it was run on. This paper led to the concept of the Universal Turing Machine, an idealized model of a computer to which all computers are equivalent.
We now know that Turing was only partially correct. Not all computers are equivalent. His work was based on an assumption  that computation and information were abstract entities, divorced from the rules of physics governing the behavior of the computer itself. One of the most important developments in modern science is the realization that information (and computation) can never exist in the abstract. Information is always tied to the physical stuff upon which it is written. What is possible to compute is completely determined by the rules of physics. Turing's work, and conventional computer science, are only valid if a computer obeys the rules of Newtonian physics  the set of rules that apply to large and hot things, like baseballs and humans. If elements of a computer behave according to different rules, such as the rules of QM, this assumption fails and many very interesting possibilities emerge.
As an example, consider the modeling of a nanosized structure, such as a drug molecule, using conventional (i.e., non-quantum) computers. Solving the Schrodinger Equation (SE), the fundamental description of matter at the QM level, more than doubles in difficulty for every electron in the molecule. This is called exponential scaling, and prohibits solution of the SE for systems greater than about 30 electrons. A single caffeine molecule has more than 100 electrons, making it roughly 100,000,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000 times harder to solve than a 30-electron system, which itself makes even high-end supercomputers choke.
This restriction makes first-principles modeling of molecular structures impossible, and has historically defined the boundary between physics (where the SE can be solved by brute force) and chemistry (where it cannot, and empirical modeling and human creativity must take over). Quantum computers are capable of solving the SE with linear scaling exponentially faster and with exponentially less hardware than conventional computers. For a Quantum Computer, the difficulty in solving the SE increases by a small, fixed amount for every electron in a system. Even very primitive Quantum Computers will be able to outperform supercomputers in simulating nature.
Even more significant, as QC technology matures, systems containing hundreds, thousands, even millions of electrons will be able to be modeled by the direct, brute force solution of the SE. This means that the fundamental equations of nature will be solvable for all nanoscale systems, with no approximations and no fudge factors. Results of these virtual reality simulations will be indistinguishable from what is seen in the real world, assuming that QM is an accurate picture of nature. This type of simulation, by direct solution of the fundamental laws of nature, will become the backbone of engineering design in the nanotech regime where quantum mechanics reigns.
Quantum Computer's Hardware
There are many potential ways to build QCs. Of these, four types have emerged as being most likely to succeed. These are based on (A) assemblies of individual atoms trapped by lasers; (B) optical circuits, for example using photonic crystals; (C) semiconductor-based designs, usually including atomic-scale control of dopant atom distribution or quantum dots; and (D) superconducting electronics.
Superconductors have an unique property: Very large structures can be built out of them that behave according to the rules of quantum mechanics. Thus, the design of superconducting QCs does not require new technology development. This contrasts with the other three types of QCs, in which information is stored using atoms or individual photons (particles of light). Controlling and manipulating this information requires technologies that do not yet exist.
The two superconductors used to build QCs are aluminum and niobium. At room temperature these materials are metals. When they are cooled down close to absolute zero, the electrons in the metals pair to form particles called Cooper pairs. These particles carry charge in the superconductor.
Cooper pairs are very different from electrons. One key difference is that Cooper pairs are what physicists call bosons, while electrons are fermions. Bosons are allowed to occupy the same quantum state, while fermions are not.
In a superconductor, all the Cooper pairs can (and do) exist in exactly the same state. This means that all of the charge carriers in the superconductor are fundamentally linked. They directly inherit their behavior from the scale of a single Cooper pair.
In essence, a chunk of superconductor amplifies the quantum effects that exist at the level of extremely tiny individual particles up to the scale of the whole chunk, even if the chunk is very large.
This amplification of quantum effects is responsible for the well-known properties of superconductors, such as zero resistance to current flow and exclusion of magnetic field.
It is also extremely useful for building QC components. Superconductors naturally shield themselves from external noise, creating a safe haven for quantum effects. This ability to build large things that behave like small things overcomes the many practical problems inherent to building real QCs.
Quantum Computing Applications
Quantum computers have many applications in business and science. Three of the areas that would benefit from their ability to solve computationally difficult problems are optimization, quantum simulation and bioinformatics.
Quantum computers can solve NP-hard optimization problems better than any digital supercomputer. Commercially, this kind of problem is particularly interesting because it contains nearly all cases where we allocate limited resources to minimize or maximize something.
For several decades, computer scientists have been trying to classify all of the problems that we currently hold knowledge of today. Whenever a new problem arises, it is placed in one of the existing categories of problems. These categories describe how intricate the problems are, subsequently explaining why.
One of the most interesting categories contains problems that are called NP-complete. This category shares a common feature: in order to solve the problem, all possible solutions must be tried, and the number of possible solutions grows exponentially with the problem size. A good example is the Traveling Salesman Problem, although there are literally thousands of them.
This category is a particularly interesting target from a commercial perspective because most real-life business problems are paralleled by it. NP-complete optimization problems underlie nearly all circumstances where limited resources are being allocated in order to minimize or maximize something. They are ubiquitous in engineering, finance, science, and logistics.
Because these problems cannot be solved by computers, the approach commonly taken is to devise methods for gaining answers which are 'good enough' for the given application. These approaches are called heuristics, and include genetic algorithms and simulated annealing.
However, quantum computers can be used to calculate approximate solutions to large NP-complete optimization problems more quickly than the best-known methods running on any supercomputer.
To design nanotechnology products, we must be able to model their behavior at the level of atoms and molecules. Only quantum computers are capable of this kind of quantum simulation.
Quantum bioinformatics could transform biology and related sciences such as biochemistry, biomedicine, biotechnology, cell biology, genetics, microbiology and molecular biology. This is possible because only quantum computers can model how molecules interact.
The scientific study of life and of living organisms drives dramatic breakthroughs in medicine and healthcare. It includes biology and related sciences such as biochemistry, biomedicine, biotechnology, cell biology, genetics, microbiology, and molecular biology.
Digital technology has enabled a multitude of scientific and medical advances that have improved the quality of life and extended human life expectancy. This technology, however, has limits. It cannot, for example, calculate the dimensional structure of molecules or compounds. As a result, the interaction of different molecules cannot be modeled. This means that traditional “wet laboratories,†for example, animal testing and human drug trials, must still be used to determine how molecules react under different circumstances.
Part of the promise of quantum computers is their ability to model how molecules react in contact with others, under various circumstances.
Everything from the car you drive, the plane you last flew in, the building in which you sit, to the computer chip in your PC, are made possible by simulation.
There is an implicit assumption that the tactics used in engineering today will apply to engineering at the nanoscale. The promise of nanotechnology is based on the premise that since everything is built of atoms, if we can manipulate matter on the level of atoms, we can build anything that is physically possible.
Building, however, is only a part of engineering. Merely having the capability to build any given assembly of atoms does not mean that we can predict how it behaves prior to construction.
Unfortunately, conventional (non-quantum) computers  no matter how powerful  are not very successful at predicting the behaviour of nature on the nanoscale. The quantum properties of matter and energy that make nanotechnology so interesting wreak havoc with conventional simulation methods.
Quantum computers are the only known solution to this problem. They are able to directly solve the fundamental equations of quantum mechanics for any physical system. Sufficiently robust quantum computers will be able to create the ultimate virtual reality environment, where products and processes at the level of atoms and molecules can be exactly and effortlessly probed.
Quantum Computing is not Science Fiction
Though the capabilities may seem astonishing, quantum computing is not science fiction. By the end of 2008, D-Wave hopes to unveil a powerful 1,000-qubit machine. D-Wave hasn’t yet released data on early test runs, but some experts are expressing guarded optimism about the machine. "I think that this current piece of work is potentially solid," says Seth Lloyd, a mechanical engineer at MIT.
Others are more dubious. Qubits must function in a single collective quantum state, so that any action performed on one simultaneously affects all the others. Achieving that kind of coherence isn't easy. "Understanding their basic fabrication process and talking with D-Wave people, I know that any quantum coherence in their system is very minimal," says John Martinis, a physicist at the University of California at Santa Barbara. But Geordie Rose, D-Wave's founder, seems unconcerned. "What we're going to do is build them and see whether they're behaving as quantum computers should."
