Breaking adiabatic quantum control with deep learning

Robust optimization for quantum reinforcement learning control using partial observations

“…For example, Ref. [38] proposed a robust quantum control protocol against systematic errors by combining Short-cuts-To-Adiabatic (STA) and deep RL methods, which has been verified in the trappedion system [39]. Moreover, weak measurements can be implemented to reduce the measurement cost more significantly [40,41], which have been successfully applied to the double-well and dissipative qubit systems, respectively.…”

Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe

“…The efficiency of this approach has been shown for the control of spin chains [158]. RL has been combined with analytical control pulses for spin manipulation in order to account for robustness constraints [182]. Another promising example is given by a hybrid algorithm using a quantum computer as an active part of the optimization process, devising the control of a molecule by a laser field [118]: The time evolution of the wave packet is determined from a quantum computer, while the iterative procedure is realized by a machine learning algorithm.…”

Quantum Stream Learning

“…In the past few years, deep reinforcement learning (DRL) has solved pulse design for fast and robust quantum state preparation [33]- [35], gate operation [36], and quantum Szilard engine [37]. However, as we highlighted in our previous researches [19], [20], we are still far from exploiting the power of RL for quantum control because of quantum measurement. RL requires the observation of states for outputting an action, conflicting with quantum mechanics' fundamental feature that the state is destroyed after direct quantum measurement.…”