Novel machine learning computational tools open new perspectives for quantum information systems. Here we adopt the open-source programming library TensorFlowTM to design multi-level quantum gates including a computing reservoir represented by a random unitary matrix. In optics, the reservoir is a disordered medium or a multimodal fiber. We show that trainable operators at the input and the readout enable to realize multi-level gates. We study single and qudit gates, including the scaling properties of the algorithms with the size of the reservoir.
Disorder is emerging as a strategy for fabricating random laser sources with very promising materials, like perovskites, for which standard laser cavities are not effective, or too expensive. We need however different fabrication protocols and technologies for reducing the laser threshold and controlling its emission. Here we demonstrate an effectively solvent-engineered method for high-quality perovskite thin films on the flexible polyimide substrate. The fractal perovskite thin films exhibit excellent optical properties at room temperature and easily achieve lasing action without any laser cavity above room temperature with a low pumping threshold. The lasing action is also observed in curved perovskite thin films on the flexible substrates. The lasing threshold can be further reduced by increasing the local curvature, which modifies the scattering strengths of the bent thin film. We also show that the curved perovskite lasers are extremely robust with respect to repeated deformations. Because of the low spatial coherence, these curved random laser devices are efficient and durable speckle-free light sources for applications in spectroscopy, bio-imaging, and illumination.
In a recent paper, we demonstrated an optical deep neural network with a real living piece of brain tumor (a 3D “tumour model”). We think this is the first example of a hybrid living/photonic hardware: a sort of artificially intelligent device performing optical functions and detecting tumour morphodynamics (including the effect of chemotherapy)
Abstract: The new era of artificial intelligence demands large-scale ultrafast hardware for machine learning. Optical artificial neural networks process classical and quantum information at the speed of light, and are compatible with silicon technology, but lack scalability and need expensive manufacturing of many computational layers. New paradigms, as reservoir computing and the extreme learning machine, suggest that disordered and biological materials may realize artificial neural networks with thousands of computational nodes trained only at the input and at the readout. Here we employ biological complex systems, i.e., living three-dimensional tumour brain models, and demonstrate a random neural network (RNN) trained to detect tumour morphodynamics via image transmission. The RNN, with the tumour spheroid 19 as a three-dimensional deep computational reservoir, performs programmed optical functions and detects cancer morphodynamics from laser-induced hyperthermia inaccessible by optical imaging. Moreover, the RNN quantifies the effect of chemotherapy inhibiting tumour growth. We realize a non-invasive smart probe for cytotoxicity assay, which is at least one order of magnitude more sensitive with respect to conventional imaging. Our random and hybrid photonic/living system is a novel artificial machine for computing and for the real-time investigation of tumour dynamics.
Authors: D. Pierangeli, V. Palmieri, G. Marcucci, C. Moriconi, G. Perini, M. De Spirito, M. Papi, C. Conti
The study and exploitation of disorder is a vital research area in the broader field of material science. Structural and compositional randomness is ubiquitous in nature, and is often key tool for specific purposes, as mimicry or colouring. The benefits of disorder are a useful guide in engineering, and in visionary developments of novel advanced materials with unexpected and surprising properties. The general subject of disorder is rapidly emerging into an area of interdisciplinary scientific interest, which is, however, still in its infancy.
To accommodate these developments, the purpose of the inaugural Disordered Materials 2019 (DisoMAT) conference is to bring together experts from various scientific communities, e.g. the natural science disciplines biology and physics, and material scientists and engineers to advance the field of disorder in material science by combining fundamental and applied research with emphasis on multidisciplinary approaches and processing routes. Contributions from the fields of theoretical, computational, and applied physics, theoretical and experimental biology, and optics and photonics are envisioned to be combined. The development of novel approaches and design routes to realize tailored disorder in materials will be one of the main topics of this conference.
The inaugural Disordered Materials 2019 conference will have a special focus on disordered materials in optics and photonics. It is by now accepted that optical media do not necessarily have to be regular. Quite in contrast, photonic materials with a deliberately introduced disorder in their respective geometries and compositions show interesting novel and tuneable unforeseen properties. These aspects shall be showcased at the conference. International experts will give keynote/invited lectures about applications in nano-optics and -photonics, in biology, and materials science. Thus, the three-day conference comes up as a discussion panel for researchers, manufacturers, and users of materials with interesting novel and tunable properties. During the conference, the best three posters will be honored.
The Disordered Materials 2019 conference will be held from 24 to 26. September 2019 in Potsdam, the city of palaces and gardens, Germany. We cordially invite you to join the Disordered Materials 2019 conference, to share your experience in disordered materials with your fellow colleagues and to enjoy the very beautiful and special atmosphere during our conference.
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