Accurate and efficient preparation of quantum state is a core issue in building a quantum computer. In this paper, we investigate how to prepare a certain single- or two-qubit target state from arbitrary initial states in semiconductor double quantum dots with only a few discrete control pulses by leveraging the deep reinforcement learning. Our method is based on the training of the network over numerous preparing tasks. The results show that once the network is well trained, it works for any initial states in the continuous Hilbert space. Thus repeated training for new preparation tasks is avoided. Our scheme outperforms the traditional optimization approaches based on gradient with both the higher efficiency and the preparation quality in discrete control space. Moreover, we find that the control trajectories designed by our scheme are robust against stochastic fluctuations within certain thresholds, such as the charge and nuclear noises.
Preparation of quantum state lies at the heart of quantum information processing. The greedy algorithm provides a potential method to effectively prepare quantum state. However, the standard greedy (SG) algorithm, in general, cannot take the global maxima and instead becomes stuck on a local maxima. Based on the SG algorithm, in this paper we propose a revised version to design dynamic pulses to realize universal quantum state preparation, i.e. preparing an arbitrary state from another arbitrary one. As applications, we implement this scheme to the universal preparation of single-and two-qubit state in the context of semiconductor quantum dots and superconducting circuits. Evaluation results show that our scheme outperforms the alternative numerical optimizations with higher preparation quality while possesses the comparable high efficiency. Compared with the emerging machine learning, it shows better accessibility and does not require any training. Moreover, the numerical results show that the pulse sequences generated by our scheme are robust against various errors and noises. Our scheme opens a new avenue of optimization in few-level system and limited action space quantum control problems.
Adiabatic evolution has important applications in quantum information processing. In that context, the system has to be maintained in one of its instantaneous eigenstates. Normally the adiabaticity of the system will be ruined by its surrounding environment. Quantum control has been used widely to speed up the adiabatic process and thus reduces the effect of the environment. In this letter, we investigate the adiabatic speedup and the associated quantum heat current with and without pulse control. The system is immersed in a non-Markovian and finite-temperature heat bath. Our calculation results show that the effective adiabatic speedup can be obtained in a weak system-bath coupling and low-temperature heat bath. Specifically, non-Markovianity from the environment can be beneficial to the enhancement of the adiabatic fidelity. Furthermore, we calculate the quantum heat current between the system and bath in the process of adiabatic speedup. We find that the adiabatic fidelity decreases with increasing heat current. Our investigation paves the way for the design of quantum heat engine and quantum device.
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