Unconventional computing

Historically, mechanical computers were used in industry before the advent of the transistor.

Mechanical computers retain some interest today both in research and as analogue computers. Some mechanical computers have a theoretical or didactic relevance, such as billiard-ball computers, while hydraulic ones like the MONIAC or the Water integrator were used effectively.[3]

While some are actually simulated, others are not. No attempt is made to build a functioning computer through the mechanical collisions of billiard balls. The domino computer is another theoretically interesting mechanical computing scheme.

Most modern computers are electronic computers with the Von Neumann architecture based on digital electronics, with extensive integration made possible following the invention of the transistor and the scaling of Moore's law.

Unconventional computing is, according to a conference description,[4] "an interdisciplinary research area with the main goal to enrich or go beyond the standard models, such as the Von Neumann computer architecture and the Turing machine, which have dominated computer science for more than half a century". These methods model their computational operations based on non-standard paradigms, and are currently mostly in the research and development stage.

This computing behavior can be "simulated" using the classical silicon-based micro-transistors or solid state computing technologies, but aim to achieve a new kind of computing.

Reservoir computing is a computational framework in the context of machine learning. The main advantage of this unconventional computing framework is that it facilitates a simple and fast learning algorithm in addition to a hardware implementation, known as a physical reservoir computer. After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output.[5]

Optical computing uses light to compute.

Fluid Computing is a method of computing that uses a fluid, such as liquids or inert gases, in order to create logic elements. This can be applied in both analog and digital methods.

Quantum computing, perhaps the most well-known and developed unconventional computing method, is characterized by the use of quantum superposition in order to perform functions similar to that of a standard computer.

Some biological approaches are heavily inspired by the behavior of neurons. This method of calculation has achieved limited success, most notably in 1999, where a simple neurocomputer was created by William Ditto of the Georgia Institute of Technology.

Analog computing, widely regarded as the simplest method of computing, relies on the mechanical motion of objects in order to create logical outputs. A simple example would be a mechanical calculator, that relies on a series of wheels and gears in order to add numbers of different values, compared to a digital computer that uses a series of transistors and electronic signals utilizing binary logic.

Ternary computing is a type of computing that uses ternary logic (instead of binary logic).