Precise and efficient control of quantum systems is essential to perform quantum information-processing tasks. In terms of adiabatic speedup via leakage elimination operator approach, for a closed system, the ideal pulse control conditions have been theoretically derived by P-Q partitioning technique. However, it is a challenge to design the corresponding control pulses for an open system, which requires noisy environments to be addressed. In this paper, we apply the stochastic search procedures to an open qutrit system and successfully obtain the optimal control pulses for significant adiabatic speedup. The calculation results show that these optimal pulses allow us to acquire higher fidelities than the ideal pulses. The improvement of fidelity is large for relatively strong system-bath coupling strength and high bath temperature. For certain coupling strength and bath temperature, the maximal improvement can be achieved for a critical characteristic frequency which represents the memory time of the environment. Our investigation indicates that the stochastic search procedures are powerful tools to design control pulses for combating the detrimental effects of the environment.
Precise and efficient control of quantum systems is essential to perform quantum information-processing tasks. In terms of adiabatic speedup via leakage elimination operator approach, for a closed system, the ideal pulse control conditions have been theoretically derived by P-Q partitioning technique. However, it is a challenge to design the corresponding control pulses for an open system, which requires noisy environments to be addressed. In this paper, we apply the stochastic search procedures to an open qutrit system and successfully obtain the optimal control pulses for significant adiabatic speedup. The calculation results show that these optimal pulses allow us to acquire higher fidelities than the ideal pulses. The improvement of fidelity is large for relatively strong system-bath coupling strength and high bath temperature. For certain coupling strength and bath temperature, the maximal improvement can be achieved for a critical characteristic frequency which represents the memory time of the environment. Our investigation indicates that the stochastic search procedures are powerful tools to design control pulses for combating the detrimental effects of the environment.
The realization of high-fidelity quantum control is crucial for quantum information processing, particularly in noisy environments where control strategies must simultaneously achieve precise manipulation and effective noise suppression. Conventional optimal control designs typically require numerical calculations of the system dynamics. Recent studies have demonstrated that long short-term memory neural networks (LSTM) can accurately predict the time evolution of open quantum systems. Based on LSTM predicted dynamics, we propose an optimal control framework for rapid and efficient optimal control design in open quantum systems. As illustrative examples, we apply the proposed framework to design optimal control for adiabatic speedup in a two-level system and for quantum state transfer in a spin chain, both under non-Markovian environments. For adiabatic speedup, our optimization procedure involves two steps: driving trajectory optimization and zero-area pulse optimization. Fidelity improvements for both steps have been obtained, demonstrating the effectiveness of the scheme. Furthermore, this effectiveness is validated for quantum state transfer in a spin chain, which is a high dimensional control problem. Our optimal control design scheme utilizes predicted dynamics to generate optimized controls, offering broad application potential in quantum computing, communication, and sensing.
Adiabatic quantum computation requires keeping a system in a particular eigenstate. However, unwanted transitions can occur due to the interaction of the system with a noisy environment. The adiabatic evolution of the system will then be ruined. To reduce the effect of the environment, quantum control methods can be used. This allows one to speed up the adiabatic process. Here we present an effective control scheme to allow for a speedup of the adiabatic process by using a sequence of pulses on the system. Using the hierarchical functional derivative (HFD) approach, we calculate the fidelity evolution, which is a measure of the adiabaticity in an open system. By carefully choosing the pulse intensity and duration, an accelerated adiabatic path can be obtained for both a weak-coupling and a strong non-Markovian environment.
Abstract 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 engines and quantum devices.
Understanding heat transfer between a quantum system and its environment is of grave importance if reliable quantum devices are to be constructed. Here, the heat transfer between the system and bath in non-Markovian open quantum systems in the process of adiabatic speedup is investigated. Using the quantum state diffusion equation method, the heat current, energy current and the power are calculated during the free evolution and under external control of the system. While the heat current increases with increasing system-bath coupling strength and bath temperature, the non-Markovian nature of the bath can restrict the heat current. Without pulse control, the heat current is nearly equal to energy current. However, with pulse control, the energy current is nearly equal to the power. We show that more non-Markovian baths can be used to better approximate an adiabatic evolution and have a smaller heat current. Our results reveal that the non-Markovian nature of the bath significantly contributes to both the heat transfer rate as well as the effective adiabatic speedup.
Adiabatic quantum computation requires that the system remains in its ground state. However, an adiabatic process often loses partly or entirely its quantumness, such as entanglement in its long runtime, due to the system-environment interactions. Here we put forward an effective quantum control technique to realize the adiabatic quantum search algorithm in a nonadiabatic regime and in the presence of environment. Using the non-Markovian quantum state diffusion equation approach, we numerically study the system dynamics characterized by the success probability. The results show that the probability increases with decreasing coupling strength and temperature. In particular, non-Markovianity from the environment can help to enhance the success probability. By choosing a suitable pulse intensity and period, a high success probability can be obtained in a short runtime for weak system-bath coupling, low temperature, and strong non-Markovian heat baths.
Accurate and precise quantum control is crucial for quantum information processing. Zero-area pulse control, originally developed for closed systems, has played a significant role in driving quantum systems toward desired states. However, its effectiveness is often compromised by environmental noise. Traditional methods can find optimal pulses for fixed environmental parameters, and they need to be recalculated again once the environmental parameters change. Here, we propose a supervised learning (SL)-based protocol to design optimal zero-area pulses in noisy environments. By including environmental parameters as input during training, the neural network (NN) learns to generate effective controls for various environmental parameters. We demonstrate the protocol using quantum state transmission through a spin chain governed by a non-Markovian master equation. The SL-optimized pulses significantly enhance transmission fidelity compared to ideal pulses, with greater improvements observed at higher system-bath coupling and temperature, where more optimization space exists. Interestingly, fidelity first increases and then decreases with stronger Markovianity, suggesting that highly Markovian environments limit control performance. We also compare the SL method with a genetic algorithm, and the similar results confirm the accuracy and generalization ability of the NN-based approach.