Three heteroditopic cryptands with different cavity
dimensions have been synthesized in high yields at
278 K without employing any templating metal ion. The three
secondary amino nitrogens in each cryptand could
be derivatized with anthryl groups to have a
fluorophore−spacer−receptor configuration. The fluorophores in
these
systems do not show any fluorescence due to an efficient photoinduced
intramolecular electron transfer (PET) from
nitrogen lone pairs. However, the fluorescence can be recovered to
different extents in the presence of different
metal ions and protons as well. On complexation by a transition
metal ion or on protonation in a solvent like dry
THF, each exhibits large fluorescence enhancement as the nitrogen lone
pairs responsible for PET are engaged in
bonding. Inner-transition-metal ions like Eu(III) or
Tb(III) show remarkable discrimination and give high
fluorescence
enhancement only in one case where the cavity size is smaller than that
of other two. Each system exhibits large
fluorescence enhancement with Pb(II) among the heavy metal ions
studied. The present study shows that transition
metal ions and Pb(II), which are known for quenching, can indeed
cause fluorescence enhancement in cryptand-based systems. It is also reported for the first time that
inner-transition-metal ions can also cause fluorescence.
The
enhancement in each case is interpreted in terms of a communication gap
between the metal ion and fluorophore.
Such cryptand-based fluorophores can be useful as potential
molecular photonic devices and metal ion sensors as
well.
Nanyang Technological University 637371, SingaporeMachine learning software applications are nowadays ubiquitous in many fields of science and society for their outstanding capability of solving computationally vast problems like the recognition of patterns and regularities in big datasets. One of the main goals of research is the realization of a physical neural network able to perform data processing in a much faster and energyefficient way than the state-of-the-art technology. Here we show that lattices of exciton-polariton condensates accomplish neuromorphic computing using fast optical nonlinearities and with lower error rate than any previous hardware implementation. We demonstrate that our neural network significantly increases the recognition efficiency compared to the linear classification algorithms on one of the most widely used benchmarks, the MNIST problem, showing a concrete advantage from the integration of optical systems in reservoir computing architectures.
The concurrent rise of artificial intelligence and quantum information poses opportunity for creating interdisciplinary technologies like quantum neural networks. Quantum reservoir processing, introduced here, is a platform for quantum information processing developed on the principle of reservoir computing that is a form of artificial neural network. A quantum reservoir processor can perform qualitative tasks like recognizing quantum states that are entangled as well as quantitative tasks like estimating a non-linear function of an input quantum state (e.g. entropy, purity or logarithmic negativity). In this way experimental schemes that require measurements of multiple observables can be simplified to measurement of one observable on a trained quantum reservoir processor.
We develop a scheme of quantum reservoir state preparation, based on a quantum neural network framework, which takes classical optical excitation as input and provides desired quantum states as output. We theoretically demonstrate the broad potential of our scheme by explicitly preparing a range of intriguing quantum states, including single-photon states, Schrödinger's cat states, and two-mode entangled states. This scheme can be used as a compact quantum state preparation device for emerging quantum technologies.
Strong light-matter interaction enriches topological photonics by dressing light with matter, which provides the possibility to realize active nonlinear topological devices with immunity to defects. Topological exciton polaritons—half-light, half-matter quasiparticles with giant optical nonlinearity—represent a unique platform for active topological photonics. Previous demonstrations of exciton polariton topological insulators demand cryogenic temperatures, and their topological properties are usually fixed. Here, we experimentally demonstrate a room temperature exciton polariton topological insulator in a perovskite zigzag lattice. Polarization serves as a degree of freedom to switch between distinct topological phases, and the topologically nontrivial polariton edge states persist in the presence of onsite energy perturbations, showing strong immunity to disorder. We further demonstrate exciton polariton condensation into the topological edge states under optical pumping. These results provide an ideal platform for realizing active topological polaritonic devices working at ambient conditions, which can find important applications in topological lasers, optical modulation, and switching.
The availability of large amounts of data and the necessity to process it efficiently have led to rapid development of machine learning techniques. To name a few examples, artificial neural network architectures are commonly used for financial forecasting, speech and image recognition, robotics, medicine, and even research. Direct hardware for neural networks is highly sought for overcoming the von Neumann bottleneck of software implementations. Reservoir computing (RC) is a recent and increasingly popular bio-inspired computing scheme which holds promise for an efficient temporal information processing. We demonstrate the applicability and performance of reservoir computing in a general complex Ginzburg-Landau lattice model, which adequately describes dynamics of a wide class of systems, including coherent photonic devices. In particular, we propose that the concept can be readily applied in exciton-polariton lattices, which are characterized by unprecedented photonic nonlinearity, opening the way to signal processing at rates of the order of 1 Tbit s −1 .
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