A team of physicists at the University of Sydney have successfully demonstrated the possibility of using big data and machine learning to accurately predict the future of a quantum system. These predictions enabled the researchers to perform actions that prevented the quantum system from breaking down in a big step forward for practical applications of quantum systems.
By Daniel Oberhaus | MOTHERBOARD
In a classical (that is, non-quantum) system, predicting the future of a system is a relatively straightforward affair because a particle occupies a single, specific position at any given time. Yet in a quantum system, a particle can occupy two different positions at the same time, a property known as superposition. This property of quantum systems is leveraged in quantum computers to allow the machines to calculate many different problems simultaneously, but also presents a host of technical difficulties that make it hard to apply in any practical sense.
The primary difficulty is that superposition is incredibly difficult to maintain for any given length of time. Simply measuring a quantum system will cause it to collapse into a discrete state (so that the particle either occupies one position or another, instead of both at the same time). It is impossible to completely isolate a quantum system from its environment, however, so even if it isn’t measured, the system will inevitably decay and lose its quantum properties as a result of interference from its environment.