The most popular and comprehensive Open Source ECM platform
Let us know more
True Randomness: Generating Random Numbers Based on Electron Motion
Random numbers are often used in simulations, data encryption, unbiased selection of things, like patients in a drug trial, and in many other applications. Because most of these applications are automated with the use of a computer, the ability for computers to generate random numbers is useful and important.
BY definition, computers are regular and predictable. Computers can easily generate sequences of random numbers, but they are repeatable sequences. Every time the computer generates the sequence it will be the same unless the initial ‘seed’ value used by the computer is changed. Thes computer-generated pseudorandom numbers are dependent on the randomness of the see used to start the generatino process. Randomness generated by a computer presents a sort of ‘chicken before the egg’ type of dilemma.
Steve Ward, Professor of Computer Science and Engineering at MIT’s Computer Science and Artificial Intelligence Laboratory, said that “one thing that traditional computer systems aren’t good at is coin flipping. They’re deterministic, which means that if you ask the same question you’ll get the same answer every time. In fact, such machines are specifically and carefully programmed to eliminate randomness in results. They do this by following rules and relying on algorithms when they compute.”
Rather than use software, hardware is often used to generate truly random numbers, for example, thermal or atmospheric noise.
Recent work by a group at the Indian Institute of Science developed a true random number generator by tracking the random motion of electrons. The current of electrons can be made to move randomly as electronics move into and out of thin-layered graphene electron ‘traps’. Kausik Majumdar, Associate Professor, said that “you cannot predict exactly at what time the electron is going to enter the trap. So, there is an inherent randomness that is embedded in this process. When the idea first struck me, I knew it would be a good random number generator, but I didn’t expect it to have a record-high min-entropy.”
