Source – https://www.analyticsinsight.net/
Quantum AI and quantum computing are transformational technologies enabling a revolutionary future.
Quantum AI refers to the use of quantum computing for the computation of machine learning algorithms. With the computational advantages of quantum computing, quantum AI can now achieve results that were not possible with classical computers.
Alan Turing published a paper on Computing Machinery and Intelligence in 1950, and since then computers have come a long way. In the current modern age, computer limitations are gradually fading away, and machine learning has the ability to learn from its experiences. Traditionally, this type of intelligence was only achievable by using multiple computers and complicated machine learning algorithms. However, Nature Nanotechnology journal had a paper published recently where scientists proposed a new method – designing a computer with embedded intelligence and using the atom’s quantum spins to revolutionize computing as we know.
To understand this concept, let cover the basics of neuromorphic computing. In layman’s language, neuromorphic computing attempts to imitate the way a human brain works. From a technical perspective, neuromorphic computing is concerned with computer engineering where the elements of a computer, both hardware, and software, are wired according to the human nervous system and cerebral system.
Engineers study several disciplines like computer science, biology, mathematics, electronic engineering, and physics to create accurate neural structures. Neuromorphic computing aims to create devices that can learn, retain information, and make logical deductions the way a human brain does, a cognition machine. Alongside, it also attempts to prove how the human brain works by scavenging new information.
As a step forward in artificial intelligence technology, neuromorphic computing allows robots embedded with small computing hardware to make their own decisions in the future.
The Quantum Brain
The Quantum brain is a prime example of neuromorphic computing, the future of computing. Our human brains use signals sent by our neurons to make all kinds of computations. Similarly, the quantum brain uses cobalt atoms on a superconducting black phosphorus surface to imitate the process of human brain signals.
Cobalt atoms have quantum properties like unique spin states which carry information to stimulate ‘neuron firing’ with applied voltages. This helped the atoms to achieve a self-adaptive behavior based on the external stimuli.
Can AI Work With A Quantum Brain?
Artificial intelligence is an evolving technology, but it still has not overcome technological limitations. But with quantum computing, obstacles to achieving artificial general intelligence, AGI, can be discarded. Quantum computing can rapidly train machine learning models to generate optimized algorithms. Quantum computing can power an optimized and steady AI to complete analysis in a short time, as opposed to years of work that would delay any and all technological advancements.
According to researchers, a realistic aim for quantum AI is to replace traditional algorithms with quantum algorithms. These quantum algorithms can have several use cases to further advancements.
• Developing quantum algorithms for traditional learning models can provide possible boosts to the deep learning training process. Quantum computing can help machine learning by presenting the optimal solution set of the weights of artificial neural networks, quickly.
• When traditional decision-making problems are formulated with decision trees, the next course of action to reach the solution sets is by creating branches for a particular point. However, this method becomes complicated when the problem is too complex. Quantum algorithms can solve the problem faster.
Can neuroscience-inspired quantum computing and AI mesh? Yes, says several similarities between the brain and machine learning techniques like deep learning. Is that future near? Yes and no. Right now, the quantum AI industry needs to work to eliminate immaturities in the technology and achieve crucial milestones such as less error-prone and more powerful computing, developing the right AI applications where quantum computing can outperform traditional computing, and creating a widely adopted open-source modeling and training frameworks. These milestones will push quantum AI towards future developments.