Scientists develop clairvoyant machine capable of PREDICTING FUTURE

The prototype device - which was created as part of a joint venture involving researchers from Australia’s Griffith University and Singapore’s Nanyang Technological University - consists of a photonic quantum information processor capable of mapping out all potential future outcomes of a decision process. Mile Gu of Nanyang, who spearheaded the development of the algorithm which underpins the device, said: “When we think about the future, we are confronted by a vast array of possibilities. “These possibilities grow exponentially as we go deeper into the future. 

“For instance, even if we have only two possibilities to choose from each minute, in less than half an hour there are 14 million possible . 

“In less than a day, the number exceeds the number of atoms in the universe.”

The device constructed by the team works on a much smaller scale, holding just 16 possible futures in simultaneous superposition, weighted by their probability of occurrence. 

However, in theory the algorithm which governs them can “scale up” without upper limit.

Lead author Farzad Ghafari of Griffith University said: “Our approach is to synthesise a quantum superposition of all possible futures for each bias.

“By interfering these superpositions with each other, we can completely avoid looking at each possible future individually. 

“In fact, many current algorithms learn by seeing how small changes in their behaviour can lead to different future outcomes, so our techniques may enable quantum enhanced AIs to learn the effect of their actions much more efficiently.”

Co-author Jayne Thompson said the team had taken inspiration from the late Nobel laureate physicist Richard Feynman.

She explained: “When Feynman started studying quantum physics, he realised that when a particle travels from point A to point B, it does not necessarily follow a single path.”

“Instead, it simultaneously transverses all possible paths connecting the points.

“Our work extends this phenomenon and harnesses it for modelling statistical futures.”

Co-author Professor Geoff Pryde said the achievements were comparable with those of researchers half a century ago.

He added: “This is what makes the field so exciting. It is very much reminiscent of classical computers in the 1960s. 

“Just as few could imagine the many uses of classical computers in the 1960s, we are still very much in the dark about what can do.

“Each discovery of a new application provides further impetus for their technological development.”

The research is published in the Nature Communications journal.

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