Classical Light Masters 'Quantum' Tricks ⚡ экспресс
Negative values of the Wigner quasiprobability distribution are considered one of the key signatures of nonclassical phenomena in quantum systems. This work presents a classical model of squeezed light that, combined with postselection of events based on crossing an amplitude threshold, can reproduce the behavior of single-photon-added coherent states. Using a classical model of balanced homodyne detection and standard tomographic methods, the density matrix is reconstructed in the Fock basis. The resulting Wigner functions exhibit negativity for photon-added states for both vacuum and weak coherent fields. The results show that Wigner negativity is not an exclusively quantum effect and can be simulated by classical systems with postselection, calling into question its use as an unambiguous criterion of nonclassicality.
The Wigner distribution is like a topographic map of probabilities: hills where the particle is most likely, and valleys where it shouldn't be. For a long time, negative 'valleys' were considered a foolproof sign that the system behaves quantumly. Yet classical light, like ripples on a lake, can reproduce these terrains after some clever filtering.
The secret lies in event selection: by passing light through a threshold, scientists kept only strong signals, ignoring the weak ones. It's exactly like a survey where all the 'no' answers are discarded and only 'yes' answers are kept, making the outcome seem one-sided. Thus, negative zones appear on the map, even though the original light was ordinary. The negative values themselves aren't real probabilities, but a mathematical trick, like a negative balance in accounting.
This means: Wigner negativity doesn't guarantee the presence of quantum effects. To avoid being fooled, more refined tests are needed. This insight dates back to Roy Glauber, who studied how light can disguise itself as quantum.
🎯 In 1932, Eugene Wigner devised a way to describe quantum particles using the language of probabilities, but with a catch: negative values in his formula are not real probabilities, but a calculational trick. That's why the distribution is called a 'quasi-distribution'.