AI News, Artificial intelligence techniques reconstruct mysteries of quantum systems

Artificial intelligence techniques reconstruct mysteries of quantum systems

For the first time, physicists have demonstrated that machine learning can reconstruct a quantum system based on relatively few experimental measurements.

This method will allow scientists to thoroughly probe systems of particles exponentially faster than conventional, brute-force techniques.

'We have shown that machine intelligence can capture the essence of a quantum system in a compact way,' says study co-author Giuseppe Carleo, an associate research scientist at the Center for Computational Quantum Physics at the Flatiron Institute in New York City.

Each electron, for instance, can have either an upward or downward spin, similar to Schrödinger's cat being either dead or alive in the famous thought experiment.

Through quantum entanglement, independent particles become intertwined and can no longer be treated as purely separate entities even when physically separated.

In turn, this ability can help scientists validate that a quantum computer is correctly set up and that any quantum software would run as intended, the researchers suggest.

Center for Computational Quantum Physics co-director Andrew Millis notes that the ideas provide an important new approach to the center's ongoing development of novel methods for understanding the behavior of interacting quantum systems, and connect with work on other quantum physics-inspired machine learning approaches.

Besides applications to fundamental research, Carleo says that the lessons the team learned as they blended machine learning with ideas from quantum physics could improve general-purpose applications of artificial intelligence as well.

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