Role of intermetallic phases in initiation and propagation of intergranular corrosion of an Al-Li-Cu-Mg alloy

Intermetallic phases in a recently developed Al-Li-Cu-Mg alloy have been investigated to understand their roles in the initiation and propagation processes of intergranular corrosion. Corrosion initiation involves trenching formation in the Al matrix adjacent to the large particles of Al7Cu2(Fe, Mn) phases. These phases containing Li are electrochemically active and susceptible to self-dissolution via a de-alloying mechanism during corrosion process. The subsurface particles of Al7Cu2(Fe, Mn) and Al20Cu2Mn3 phases act as the internal cathodes for continuous corrosion propagation along the particle-matrix interface and the associated grain boundaries. Corrosion propagation along the particle-matrix interface was facilitated by the anodic dissolution of the surrounding Al matrix due to the micro-galvanic interaction with the cathodic intermetallic phases. In addition, T1 (Al2CuLi) precipitates and the isolated particles of Al7Cu2(Fe, Mn) and Al20Cu2Mn3 phases were dissolved along the path of corrosion propagation. The dissolved metal ions were redeposited through the network of crevice.

Experimental semi-autonomous eigensolver using reinforcement learning

The characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eigenvectors of an arbitrary Hermitian operator using the IBM quantum computer. To this end, we only use single-shot measurements and pseudo-random changes handled by a feedback loop, reducing the number of measures in the system. Due to the classical feedback loop, this algorithm can be cast into the reinforcement learning paradigm. Using this algorithm, for a single-qubit observable, we obtain both eigenvectors with fidelities over 0.97 with around 200 single-shot measurements. For two-qubits observables, we get fidelities over 0.91 with around 1500 single-shot measurements for the four eigenvectors, which is a comparatively low resource demand, suitable for current devices. This work is useful to the development of quantum devices able to decide with partial information, which helps to implement future technologies in quantum artificial intelligence.