Quantum Computation and Quantum Information
The earliest antecedent of Q computation and Q information may be found in the long-standing desire of physicists to better understand QM. The best known critic of QM, Albert Einstein, went to his grave unreconciled with the theory he helped invent. Generations of physicists since have wrestled with QM. In an effort to make its predictions more palatable. One of the goals of Q computation and Q information is to develop tools which sharpen our intuition about QM and make its predictions more transparent to human minds. {VR could help us here}
A related historical strand contributing to the development of Q computation and Q information is the interest dating back to the 1970s of obtaining complete control over single quantum systems. Applications of QM prior to 1970s typically involved a gross level of control over a bulk sample containing an enormous number of a QM systems, none of them directly accessible. For example: superconductivity has a superb Q mechanical explanation. However, because a superconductor involves a huge [compared to the atomic scales] sample of conducting metal, we can only probe a few aspects of its QM nature with the individual Quantum systems constituting the superconductor remaining inaccessible. Systems such as particle accelerators do allow us limited access to individual Systems, but again provide little control over the constituent systems. Since the ‘70s, many techniques for controlling single Q Systems have been developed. For example: methods have been developed for trapping a single atom in an ‘atom trap’, isolating it from the rest of the world and allowing us to probe many different aspects of its behavior with incredible precision. The Scanning Tunneling Microscope has been used to move single atoms around, creating designer arrays of atoms at will. {What are the implications of this? Need input from specialists/authors}
R. Feynman had suggested in 1982 that there seemed to be essential difficulties in simulating Mechanical systems on classical computers, and suggested that building computers based on the principles of QM would allow us to avoid those difficulties. In the 1990’s, several teams of researchers began fleshing out this idea out, showing that it is indeed possible to use Quantum computers to efficiently simulate systems that have no known efficient simulation on a classical computer. It is likely that one of the major applications of Quantum computers in the future will be performing simulations of QM systems too difficult to simulate on a classical computer; a problem with profound scientific and technological implications. {VR is demonstrating how powerful changing perspectives can be for the human mind, imagine how this will change with quantum certainty or rather, continuity…}
What other problems can Quantum computers solve more quickly than classical computers? The short answer is that we don’t know. Coming up with good Algorithms seems to be hard. Algorithm design for Quantum computers is hard because designers face two difficult problems not faced with classical computers. First our human intuition is rooted in the classical world. If we use that intuition as an aid to the construction of algorithms, then the algorithmic ideas we come up with will be classical ideas. To design good Algorithms, one must “turn-off”one’s classical intuition, for at least part of the design process, using truly q effects to achieve the desired algorithmic end. Second, to be truly interesting, it is not enough to design an algorithm that is merely quantum mechanical. The algorithm must be better than any existing classical algorithm. Thus, it is possible that one may find an algorithm which makes use of truly Q aspects of QM, that is nevertheless not of widespread interest because classical algorithms with comparable performance characteristics exist. The combination of these two problems makes the construction of new quantum algorithms a challenging problem for the future.