Neural networks can estimate the degree of entanglement in quantum systems far more efficiently than traditional techniques, a new study shows. By side-stepping the need to fully characterize quantum states, the new deep learning method could prove especially useful for large-scale quantum technologies, where quantifying entanglement will be crucial but resource limitations make full state characterization unrealistic.
Entanglement – a situation in which multiple particles share a common wavefunction, so that disturbing one particle affects all others – is at the heart of quantum mechanics. Measuring the degree of entanglement in a system is thus part of understanding how “quantum” it is, says study co-author Miroslav Ježek, a physicist at Palacký University in Czechia. “You can observe this behaviour starting from simple two-particle systems where the fundamentals of quantum physics are discussed,” he explains. “On the other hand, there is a direct link between, for example, changes of entanglement and phase transitions in macroscopic matter.”
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