Electroactive ionic gel/metal nanocomposites are produced by implanting supersonically accelerated neutral gold nanoparticles into a novel chemically crosslinked ion conductive soft polymer. The ionic gel consists of chemically crosslinked poly(acrylic acid) and polyacrylonitrile networks, blended with halloysite nanoclays and imidazolium-based ionic liquid. The material exhibits mechanical properties similar to that of elastomers (Young's modulus ≈ 0.35 MPa) together with high ionic conductivity. The fabrication of thin (≈100 nm thick) nanostructured compliant electrodes by means of supersonic cluster beam implantation (SCBI) does not significantly alter the mechanical properties of the soft polymer and provides controlled electrical properties and large surface area for ions storage. SCBI is cost effective and suitable for the scaleup manufacturing of electroactive soft actuators. This study reports the high-strain electromechanical actuation performance of the novel ionic gel/metal nanocomposites in a low-voltage regime (from 0.1 to 5 V), with long-term stability up to 76 000 cycles with no electrode delamination or deterioration. The observed behavior is due to both the intrinsic features of the ionic gel (elasticity and ionic transport capability) and the electrical and morphological features of the electrodes, providing low specific resistance (<100 Ω cm ), high electrochemical capacitance (≈mF g ), and minimal mechanical stress at the polymer/metal composite interface upon deformation.

Some problems in physics can be handled only after a suitable ansatz solution has been guessed.Such method is therefore resilient to generalization, resulting of limited scope. The coherent transport by adiabatic passage of a quantum state through an array of semiconductor quantum dots provides a par excellence example of such approach, where it is necessary to introduce its so called counter-intuitive control gate ansatz pulse sequence. Instead, deep reinforcement learning technique has proven to be able to solve very complex sequential decision-making problems involving competition between short-term and long-term rewards, despite a lack of prior knowledge. We show that in the above problem deep reinforcement learning discovers control sequences outperforming the ansatz counter-intuitive sequence. Even more interesting, it discovers novel strategies when realistic disturbances affect the ideal system, with better speed and fidelity when energy detuning between the ground states of quantum dots or dephasing are added to the master equation, also mitigating the effects of losses. This method enables online update of realistic systems as the policy convergence is boosted by exploiting the prior knowledge when available. Deep reinforcement learning proves effective to control dynamics of quantum states, and more generally it applies whenever an ansatz solution is unknown or insufficient to effectively treat the problem.

Several tasks involving the determination of the time evolution of a system of solid state qubits require stochastic methods in order to identify the best sequence of gates and the time of interaction among the qubits. The major success of deep learning in several scientific disciplines has suggested its application to quantum information as well. Thanks to its capability to identify best strategy in those problems involving a competition between the short term and the long term rewards, reinforcement learning (RL) method has been successfully applied, for instance, to discover sequences of quantum gate operations minimizing the information loss. In order to extend the application of RL to the transfer of quantum information, we focus on Coherent Transport by Adiabatic Passage (CTAP) on a chain of three semiconductor quantum dots (QD). This task is usually performed by the so called counter-intuitive sequence of gate pulses. Such sequence is capable of coherently transfer an electronic population from the first to the last site of an odd chain of QDs, by leaving the central QD unpopulated. We apply a technique to find nearly optimal gate pulse sequence without explicitly give any prior knowledge of the underlying physical system to the RL agent. Using the advantage actor-critic algorithm, with a small neural net as function approximator, we trained a RL agent to choose the best action at every time step of the physical evolution to achieve the same results previously found only by ansatz solutions.