arXiv subjects

Hai-Jing Song

Publications and source records attributed to Hai-Jing Song.

4 recordsLinked to original sources

arXiv2026

Generating photons from vacuum with counter rotating wave interaction

We propose a bang-bang control scheme to enhance photon generation from the vacuum via the counter-rotating wave (CRW) interaction, and develop a pruning greedy algorithm (PGA) to identify the optimal control sequence. Our numerical results demonstrate that the maximum number of photons generated within a given evolution time is increased by several orders of magnitude compared with that achieved by continuous activation of the CRW interaction.

quant-ph↗

arXiv2023

Disassociation of a one-dimensional cold molecule via quantum scattering

Motivated by the recent experimental developments on ultracold molecules and atoms, we propose a simplest theoretical model to address the disassociation, reflection and transmission probability of a 1-dimensional cold molecule via quantum scattering. First, we give the Born approximation results in the weak interaction regime. Then, employing the Lippmann-Schwinger equation, we give the numerical solution and investigate the disassociation's dependence on the injection momentum and the interaction strengths. We find that the maximum disassociation rate has a limit as increasing the interaction strengths and injection momentum. We expect that our model can be realized in experiments in the near future.

cond-mat.quant-gas↗

arXiv2020

Quantum optimal control of multi-level dissipative quantum systems with Reinforcement Learning

Manipulate and control of the complex quantum system with high precision are essential for achieving universal fault tolerant quantum computing. For a physical system with restricted control resources, it is a challenge to control the dynamics of the target system efficiently and precisely under disturbances. Here we propose a multi-level dissipative quantum control framework and show that deep reinforcement learning provides an efficient way to identify the optimal strategies with restricted control parameters of the complex quantum system. This framework can be generalized to be applied to other quantum control models. Compared with the traditional optimal control method, this deep reinforcement learning algorithm can realize efficient and precise control for multi-level quantum systems with different types of disturbances.

quant-ph↗

arXiv2018

Geometry and symmetry in quantum Boltzmann machine

Quantum Boltzmann machine extends the classical Boltzmann machine learning to the quantum regime, which makes its power to simulate the quantum states beyond the classical probability distributions. We develop the BFGS algorithm to study the corresponding optimization problem in quantum Boltzmann machine, especially focus on the target states being a family of states with parameters. As an typical example, we study the target states being the real symmetric two-qubit pure states, and we find two obvious features shown in the numerical results on the minimal quantum relative entropy: First, the minimal quantum relative entropy in the first and the third quadrants is zero; Second, the minimal quantum relative entropy is symmetric with the axes $y=x$ and $y=-x$ even with one qubit hidden layer. Then we theoretically prove these two features from the geometric viewpoint and the symmetry analysis. Our studies show that the traditional physical tools can be used to help us to understand some interesting results from quantum Boltzmann machine learning.

quant-ph↗