Scientists show the first hardware for a 'probabilistic computer' to fill the gap between conventional computers and future quantum computers
A quantum computer is a type of computer that uses a processor that utilizes the principles of quantum mechanics. A ‘quantum processor’ is therefore uniquely able to perform the same calculations in one go over a very, very large amount of data. It can, therefore, be much faster than a conventional computer in performing specific tasks.
The concept of a quantum computer was already described in the early 1980s by scientists like Richard Feynman and Yuri Manin. However even for scientists today it is extremely difficult to produce a functional quantum computer. A lot of money is being invested in the development though, and we are slowly getting closer.
Nonetheless, It may yet be decades before quantum computers are really prepared to resolve problems that conventional computers aren't quick or effective enough to resolve. The evolving "probabilistic computer" could bridge the chasm between traditional and quantum computing.
Recently scientists at Purdue University and Tohoku University in Japan constructed the first hardware to reveal how the fundamental units (p-bits), of what would be a probabilistic computer, are capable of executing a calculation that could otherwise have been done with the help of quantum computers.
The researchers published their findings in the scientific journal Nature. Their hardware could form the basis for a probabilistic computer that might prove to be a massive help in a wide array of scientific fields (like data analysis and the search for new drugs).
The significant difference between quantum computing and conventional computing is that current computers store their information in binary form (using ones and zeroes, called bits). Quantum computers use a different type of ‘bits’ called quantum bits. The unique feature of quantum bits is that they can both be zero and one simultaneously. A few years ago, scientists from Purdue University proposed the idea of a probabilistic computer. A probabilistic computer would use p-bits that can either be zero or one at any given time (like conventional computers) and fluctuate rapidly between the two.
Professor Supriyo Datta, the scientist that led the Purdue research, stated that there is a useful subset of problems that you could solve with quantum bits, but that can also be solved with p-bits. He referred to the p-bit as a ‘poor man’s quantum bit.’
The benefit of p-bits is that they work at normal temperatures where quantum bits need extremely cold temperatures to function. According to scientists this means that current hardware can be adapted to construct a probabilistic computer. The research team constructed a device that is an altered variant of magnetoresistive random-access memory (also known as MRAM), which is used to store information in specific contemporary computers.
MRAM is a memory technology cantered on thin-film magnetism. The memory cells are constructed from a thin magnetic multi-layer. This multi-layer typically consists of a so-called "fixed" magnetic layer and a "free" magnetic layer. The magnetization of the solid layer is fixed in a fixed direction and cannot be easily influenced by external magnetic fields. The technology utilizes the positioning of magnets to produce states of resistance relating to zero or one. The researchers altered an MRAM device so that it became unstable. That way, it would be more suitable to enable the capability of p-bits to fluctuate. Purdue researchers combined this device with a transistor to build a three-terminal unit whose fluctuations could be controlled. The circuit successfully solved what is often considered a "quantum" problem: Breaking down, or factoring, numbers such as 35,161 and 945 into smaller numbers.
Ahmed Zeeshan Pervaiz, a Purdue Ph.D. student, stated that the circuit would take up the same area as a transistor when put on a chip. However it would be able to perform calculations that would otherwise need thousands of transistors to perform.
In short probabilistic computing has a lot of potentials and might be an ideal filler to help us bridge the gap between conventional computing and futuristic quantum computing. It could speed up data analysis for research and help in the search for new drugs. We will see how long it takes to implement this technology into usable hardware.
Sources and further reading: Integer factorization using stochastic magnetic tunnel junctions / Purdue University / Quantum computing
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