Optimize cooling-by-measurement by reinforcement learning

Abstract

Cooling by the conditional measurement demonstrates a transparent advantage over that by the unconditional counterpart on the average-population-reduction rate. This advantage, however, is blemished by few percentage of the successful probability of finding the detector system in the measured state. In this work, we propose an optimized architecture to cool down a target resonator, which is initialized as a thermal state, using an interpolation of the conditional and unconditional measurement strategies. Analogous to the conditional measurement, an optimal measurementinterval τ u opt for the unconditional (nonselective) measurement is analytically found for the first time, which is inversely proportional to the collective dominant Rabi frequency Ω d as a function of the resonator's population at the end of the last round. A cooling algorithm under the global optimization by the reinforcement learning results in the maximum value for the cooperative cooling performance, an indicator function to quantify the comprehensive cooling efficiency for arbitrary cooling-by-measurement architecture. In particular, the average population of the target resonator under only 16 rounds of measurements can be reduced by over four orders in magnitude with a successful probability about 30%.

Breaking the limits of purification: postselection enhances heat-bath algorithmic cooling

“…When those dynamics can be arbitrarily controlled, the system can always trivially be cooled; this is because adding an auxiliary system, performing an arbitrary joint interaction, then ignoring or measuring the arbitrary system allows one to enact an arbitrary quantum channel on a given quantum system to generate any desired state [63] and strongly violates the assumptions of HBAC. The HBAC can also be replaced by any other procedure that helps to serially cool a quantum system toward its ground state, including other measurement-based protocols [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Third, HBAC protocols have recently been shown to potentially benefit from the presence of environmental noise [12].…”

“…However, this naive picture is too simplistic, as it requires n ideal binary-outcome measurements to purify n qubits. Alternative measurement-based purification schemes exist that require many measurements or only converge toward the purest state [16][17][18][19][20][21][22][23][24][25][26][27][28], such as a recent idea that proposes to perform ?n measurements on a single auxiliary qubit to purify n qubits (with some finite success probability that decays exponentially with n) [29] and one that seeks to reduce the number of measurements by using techniques from reinforcement learning [30]. In comparison, our method can completely purify n qubits with a single binary measurement (and arbitrarily high success probability).…”