Quantum neural network may be able to cheat the uncertainty principle
Calculations show that injecting randomness into a quantum neural network could help it determine properties of quantum objects that are otherwise fundamentally hard to access
Technology
Calculations show that injecting randomness into a quantum neural network could help it determine properties of quantum objects that are otherwise fundamentally hard to access
9 January 2026
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Quantum computers could benefit from a path around the Heisenberg uncertainty principle
Marijan Murat/dpa/Alamy
The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum version of a neural network.
Given, for example, a chemically useful molecule, how can you predict what properties it might have in an hour or tomorrow? To make such predictions, researchers start by measuring its current properties. But for quantum objects, including some molecules, this can be unexpectedly difficult because each measurement can interfere with or change the outcome of the next measurement. Notably, the Heisenberg uncertainty principle states that some quantum properties of objects simply cannot be precisely measured simultaneously. For example, if you measure a quantum particle’s momentum extremely well, measuring its position will return only an approximate number.