Temperature Dependence of a Sub-wavelength Compact Graphene Plasmon-Slot Modulator

We investigate a plasmonic electro-optic modulator with an extinction ratio exceeding 1 dB/um by engineering the optical mode to be in-plane with the graphene layer, and show how lowering the operating temperature enables steeper switching. We show how cooling Graphene enables steeping thus improving dynamic energy consumption. Further, we show that multi-layer Graphene integrated with a plasmonic slot waveguide allows for in-plane electric field components, and 3-dB device lengths as short as several hundred nanometers only. Compact modulators approaching electronic length-scales pave a way for ultra-dense photonic integrated circuits with smallest footprint

Differentiable Quantum Architecture Search for Quantum Reinforcement Learning

Differentiable quantum architecture search (DQAS) is a gradient-based framework to design quantum circuits automatically in the NISQ era. It was motivated by such as low fidelity of quantum hardware, low flexibility of circuit architecture, high circuit design cost, barren plateau (BP) problem, and periodicity of weights. People used it to address error mitigation, unitary decomposition, and quantum approximation optimization problems based on fixed datasets. Quantum reinforcement learning (QRL) is a part of quantum machine learning and often has various data. QRL usually uses a manually designed circuit. However, the pre-defined circuit needs more flexibility for different tasks, and the circuit design based on various datasets could become intractable in the case of a large circuit. The problem of whether DQAS can be applied to quantum deep Q-learning with various datasets is still open. The main target of this work is to discover the capability of DQAS to solve quantum deep Q-learning problems. We apply a gradient-based framework DQAS on reinforcement learning tasks and evaluate it in two different environments - cart pole and frozen lake. It contains input- and output weights, progressive search, and other new features. The experiments conclude that DQAS can design quantum circuits automatically and efficiently. The evaluation results show significant outperformance compared to the manually designed circuit. Furthermore, the performance of the automatically created circuit depends on whether the super-circuit learned well during the training process. This work is the first to show that gradient-based quantum architecture search is applicable to QRL tasks.Comment: 4+1 pages, 3 figures, QCE23 workshop QM