Thermal balance and the amplifier entropy photon-number inequality
Harjas Sandhu
Abstract
What does an amplifier produce when there is nothing to amplify? Give a quantum amplifier two empty inputs and the output you keep is no longer empty. The energy comes from the pump. The extra contribution has a quantum origin: an amplifier needs a second input to preserve the rules that its output must obey, and that input matters even when it starts in the vacuum. For thermal inputs, the answer can be calculated exactly. At gain 2, an input with one photon per mode and a second input in the vacuum produce a thermal output with three photons per mode. But this calculation leaves a more interesting question: if we keep the input entropies fixed and allow more complicated quantum states, can we make the output entropy smaller? The amplifier entropy photon-number inequality, conjectured by De Palma, Mari and Giovannetti, says that thermal inputs already give the lower bound. Entropy photon number expresses entropy on a thermal scale: it is the occupation a thermal state would need to have the same entropy per mode. It need not equal the actual photon number of the state being studied. This manuscript presents a proof candidate for the inequality with two independent, finite-energy input groups. The modes within either group may be correlated or entangled. The proposed bound is sharp, with equality attained by thermal inputs. The argument brings together two descriptions of the same amplifier. One comes from the geometry of entropy, the other from relaxation towards thermal equilibrium. The amplifier’s second input changes the signs in both. At a regularised entropy minimum, the proposed argument pairs those changes so that the energy corrections cancel; a trace-norm thermal Stein estimate then helps close the finite-energy argument. The paper also presents a Gaussian spectral refinement and equality analysis, and a bound on correlations between the two outputs conditional on the general inequality. The Gaussian-input inequality itself is established prior work. The variational framework and four-weight interpolation are adapted from OpenAI’s beam-splitter argument.