Nanocomputing is a term used for the representation and manipulation of data by computers smaller than a microcomputer. Current devices are already utilizing transistors with channels below 100 nanometers in length.
The current goal is to produce computers smaller than 10 nanometers. Future developments in nanocomputing will provide resolutions to the current difficulties of forming computing technology at the nanoscale. For example, current nanosized transistors have been found to produce a quantum tunneling effect where electrons ‘tunnel’ through barriers, making them unsuitable for use as a standard switch.
The increased computing power formed by nanocomputers will allow for the solution of exponentially difficult real world problems. Nanocomputing also has the advantage of being produced to fit into any environment, including the human body, whilst being undetectable to the naked eye. The small size of devices will allow for processing power to be shared by thousands of nanocomputers. Nanocomputing in the form of DNA nanocomputers and quantum computers will require different technology than current microcomputing techniques but supply their own benefits.
Nanocomputing can be produced by a number of nanoscale structures including biomolecules such as DNA and proteins. As DNA functions through a coding system of four nucleobases it is suited for application in data processing. DNA nanocomputers could produce faster problem solving through the ability to explore all potential solutions simultaneously. This is in contrast to conventional computers which solve problems by exploring solution paths one at a time in a series of steps.
Solutions to difficult problems would no longer be constrained by processing time. DNA has the ability to provide this level of computing ability at the nanoscale because of the endless possible rearrangements of DNA through gene-editing technology. The large number of random genetic code combination can be used for processing solutions simultaneously, necessary for solving exponentially difficult real world problems.
Practical applications of this theoretical technology will require the ability to control and program DNA flexibly. The earliest applications of DNA to computing will likely be in the form of transistor switches, overcoming current microcomputing problems such as transistor tunneling. Biomolecular switches will be able to control electron flow for computation through a change in composition of the DNA molecules or by adapting the amount of light scattered by the biomolecules. Alternative transistors have already been developed using DNA for biological nanocomputers. The DNA switch could be genetically programmed to produce or inhibit the production of a protein. This would allow for the development of biological functions that can compute disease diagnostics.
Quantum computing provides computational power at the nanoscale with abilities that reach beyond the limitations of conventional computers. This is because quantum computers store and manipulate data through the utilization of subatomic particles dynamics. Binary computers process single pieces of information as a binary state, either a 1 or a 0. Subatomic particles have two states, but can also exist in any superposition of states. This means they are governed by the laws of quantum mechanics rather than classical physics allowing them to compute solutions to problems with greater speed whilst requiring less space.
Future applications of quantum computing may include:
- The simulation of drug response that is more efficient than current medical trials. This will lead to the faster development of new drugs.
- Greater understanding of disease development through improved computational models.
- Improved transportation logistics across the world.
- Improved financial modeling to avoid economic downturns.
- The development of driverless cars with the ability to process real world driving problems faster than human drivers.
- The rapid processing of large amounts of astronomical data for discovering new planets.
- The production of quantum simulations for modeling the behavior of subatomic particles without the need for creating the extreme conditions necessary for observing these particles.
- Improved machine learning for artificial intelligence progression.
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