The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. However, the existing quantum computing platforms are hard to simulate classical deep learning models or problems because of the intractability of deep quantum circuits. Thus, it is necessary to design feasible quantum algorithms for quantum machine learning for noisy intermediate scale quantum (NISQ) devices. This work explores variational quantum circuits for deep reinforcement learning. Specifically, we reshape classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits. Moreover, we use a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and target network. Besides, our variational quantum circuits can be deployed in many near-term NISQ machines.

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We report on the fabrication and electro-optic measurements of face-centered-cubic (fcc) lattices in holographic polymer dispersed liquid-crystal materials. Four linearly polarized coherent plane waves were interfered to generate a fcc optical lattice that was subsequently and indefinitely recorded as an arrayed pattern of nanometer-sized liquid-crystal droplets (approximately 50 nm) at lattice nodes within a polymer matrix. Observed transmission spectra and Kossel diffraction curves are consistent with fcc crystal structure. A completely reversible 2% wavelength shift of the (+/- 111) stop band was observed on application of an electric field.

Water-soluble DNA cross-linking phenol and biphenol derivatives 3 and 6 have been synthesized by a Mannich reaction. Both of them can cross-link DNA by photoactivation using visible light (wavelength > 400 nm). Compound 6 can cross-link DNA at pH 5.0 and 7.7, whereas no cross-link was observed at pH 10.0. Density functional theory (DFT) calculation indicated that 6 displays a twist structure. Therefore, it could bind to DNA naturally and has potent property to cross-link DNA after photoactivation.