Quantum computing is still cutting-edge, but that doesn't mean it can't be improved. Is it the same as quantum cryptography, a central tenet of so-called quantum security? And where does artificial intelligence (AI) fit in? Often when you hear about quantum computing in terms of security, it's related to quantum cryptography. Quantum computing has a major speed advantage over computational computing.

Quantum computers are quite thoroughgoing in how they approach the problem solving dynamic. Quantum computers shy away from the traditional transistor methodology and opt for spins. The hype around quantum computers is unmatched, they have been dubbed as'The Cancer, aging and death solvers', some go as fare as calling them the'ultimate hackers' and will break RSA encryption. Well I am currently playing with these babies, specifically the IBM quantum computers and will share what I have learned so far. Traditional computers process tasks using binary 101100, lets imagine this as a heads and tales system with a coin.

When photons are used as the carriers of quantum information to transmit data, that information is often distorted due to environment fluctuations destroying the fragile quantum states necessary to preserve it. Researchers from Louisiana State University exploited a type of machine learning to correct for information distortion in quantum systems composed of photons. Published in Advanced Quantum Technologies, the team demonstrated that machine learning techniques using the self-learning and self-evolving features of artificial neural networks can help correct distorted information. This results outperformed traditional protocols that rely on conventional adaptive optics. "We are still in the fairly early stages of understanding the potential for machine learning techniques to play a role in quantum information science," said Dr. Sara Gamble, program manager at the Army Research Office, an element of U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory.

When photons are used as the carriers of quantum information to transmit data, that information is often distorted due to environment fluctuations destroying the fragile quantum states necessary to preserve it. Researchers from Louisiana State University exploited a type of machine learning to correct for information distortion in quantum systems composed of photons. Published in Advanced Quantum Technologies, the team demonstrated that machine learning techniques using the self-learning and self-evolving features of artificial neural networks can help correct distorted information. This results outperformed traditional protocols that rely on conventional adaptive optics. "We are still in the fairly early stages of understanding the potential for machine learning techniques to play a role in quantum information science," said Dr. Sara Gamble, program manager at the Army Research Office, an element of U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory.

A new Artificial Intelligence (AI) has allowed AI researchers, for the first time, to demonstrate a useful and reliable way of sifting through masses of correlating data to spot when correlation means causation. By fusing old, overlapping and incomplete datasets this new method, inspired by quantum cryptography, paves the way for researchers to glean the results of medical trials that would otherwise be too expensive, difficult or unethical to run. The research is being published at the prestigious and peer-reviewed Association for Advancement of Artificial Intelligence (AAAI) conference in New York. Dr Saurabh Johri, Chief Science Officer at Babylon, said: "Until now, we have been limited to piecing together answers from studies that needed to capture all the data really neatly. But when we've seen a correlation between obesity and low vitamin D in one study, and obesity and heart failure in another, we have not been able to say whether vitamin D has a causal role in heart failure without doing another, hugely expensive clinical trial. Now we can put the pieces of the jigsaw together."

IMAGE: This is an illustrative diagram giving an example of how artificial intelligence tackles establishing causation from correlation. A new Artificial Intelligence (AI) has allowed AI researchers, for the first time, to demonstrate a useful and reliable way of sifting through masses of correlating data to spot when correlation means causation. By fusing old, overlapping and incomplete datasets this new method, inspired by quantum cryptography, paves the way for researchers to glean the results of medical trials that would otherwise be too expensive, difficult or unethical to run. The research is being published at the prestigious and peer-reviewed Association for Advancement of Artificial Intelligence (AAAI) conference in New York. Dr Saurabh Johri, Chief Science Officer at Babylon, said: "Until now, we have been limited to piecing together answers from studies that needed to capture all the data really neatly. But when we've seen a correlation between obesity and low vitamin D in one study, and obesity and heart failure in another, we have not been able to say whether vitamin D has a causal role in heart failure without doing another, hugely expensive clinical trial. Now we can put the pieces of the jigsaw together."