Quantum AI And Quantum Brain — The Evolution Of Future Tech
“AI”, “Brain”, and “quantum” — these three words might look easy and understandable but it is very tough to sum up these huge fields in just one article. One can say that Quantum mechanics is among the preferred technical solutions to make computer science step into a brand new era of algorithmic execution, speed, and security.
What Is Quantum?
The term “quantum” is often overused, the same as AI. The word “quantum” is related to the atomic energy levels quantization. But, today “quantum” is defined as the place where the objects are both waves and particles with probabilistic measurement.
What Is Quantum AI?
Quantum AI belongs to the usage of quantum computing that is needed for the computation of machine learning algorithms. Both Quantum Computing and Quantum AI are innovative technologies that assure a revolutionary future. They have many computational benefits. This gave rise to quantum AI to accomplish better results which were impossible with classical computers.
In the year 1950, a paper was published by Alan Turing on the concepts of Intelligence and Computing Machinery. In the current modern era, the restrictions on computers have decreased gradually. Machine learning consists of immense capabilities to understand from its experiences. To get this kind of intelligence complex machine learning algorithms and various other computers were needed.
To know the basic of this concept, it is important to understand the details of neuromorphic computing. The “Neuromorphic Computing” tries to copy the methods a human brain functions. In technical words it can be said as-neuromorphic computing is closely related to computer engineering, in which the sections of the computer that include both software as well as hardware are equipped as per human cerebral system and nervous system.
The engineers study various subjects like Mathematics, Physics, Biology, Computer Science, and Electronic Engineering to create accurate neural structures. The neuromorphic computing core objective is to make such kind of devices, which can obtain information, learn, and make logical deductions similar to the human brain. Apace with, it also tries to show the functionality of the human brain by getting the latest information. In AI technology, neuromorphic computing gives the robots, which are embedded with less computing hardware so that they can decide by them in the future.
For the AI to function, the computer requires understanding the patterns in the surroundings and also learns new ones. The “Quantum Brain” is the main instance of neuromorphic computing, which is also the future of computing. These mainly take the help of cobalt atoms on a superconducting black phosphorus surface to copy the methods of signals in the human brain.
The cobalt atoms consist of quantum properties such as unique spin states, which store and processes information in the same manner to the brain. This supports the atoms to accomplish a self-adaptive behavior depending on the external stimuli.
Is it possible to work with a Quantum Brain?
AI is a rapidly-growing technology. But there are many constraints that it needs to tackle. With the help of Quantum computing, the hurdles to accomplish AGI (Artificial General Intelligence) can be abandoned easily. As it can quickly teach the machine learning models to get optimized algorithms, it can also boost the steady and optimized AI to finish the analysis in a limited span of time. Based on the inputs many researchers say that a realistic objective for Quantum AI is to replace the traditional algorithms with quantum algorithms. The quantum algorithms might have many use cases for better development.
- While the traditional decision-making issues are put together with decision trees, the further way of action to get the solution sets is by generating branches for a specific point. Despite this, the method gets more complicated while the issues are too complex. The quantum algorithms have the capability to resolve the issues quicker.
- Creating the quantum algorithms for traditional learning models would enable required boosting to the deep learning training process. The Quantum Computing supports machine learning by giving the ideal solution set of the weights of artificial neural networks, faster.
Advancements in the Quantum Brain
According to the researchers, creating a system, building a huge network of atoms, and diving into the latest “quantum” elements can be utilized for building self-learning computing devices that are a lot smaller and quite energy efficient.
Will the AI and neuroscience-inspired quantum computing mesh? Maybe yes, as there are many similarities between machine learning techniques and the brain like deep learning. As of now the AI industry is developing best type of applications where traditional computing can be replaced with quantum computing with better performance, and makes a vastly adopted open-source modelling and training frameworks.