An Automatic Approach for Combinational Problems on a Hybrid Quantum Architecture

Quantum computing has shown great potential and advantages in solving integer factorization and disordered database search. However, it is not easy to solve specific problems with quantum computing device efficiently and widely, because a lot of professional background knowledge is required. In order to solve this problem, we propose an optimization problem’s automatic hybird quantum framework (OpAQ) for solving user-specified problems on a hybrid computing architecture including both quantum and classical computing resources. Such a solver can allow nonprofessionals who are not familiar with quantum physics and quantum computing to use quantum computing device to solve some classically difficult problems easily. Combinatorial optimization problem is one of the most important problems in both academic and industry. In this paper, we mainly focus on these problems and solve them with OpAQ, which is based on quantum approximation optimization algorithm (QAOA). We evaluate the performance of our approach in solving Graph Coloring, Max-cut, Traveling Salesman and Knapsack Problem. The experimental results show that quantum solver can achieve almost the same optimal solutions with the classical.

The Quantum Nature of Color Perception: Uncertainty Relations for Chromatic Opposition

In this paper, we provide an overview on the foundation and first results of a very recent quantum theory of color perception, together with novel results about uncertainty relations for chromatic opposition. The major inspiration for this model is the 1974 remarkable work by H.L. Resnikoff, who had the idea to give up the analysis of the space of perceived colors through metameric classes of spectra in favor of the study of its algebraic properties. This strategy permitted to reveal the importance of hyperbolic geometry in colorimetry. Starting from these premises, we show how Resnikoff’s construction can be extended to a geometrically rich quantum framework, where the concepts of achromatic color, hue and saturation can be rigorously defined. Moreover, the analysis of pure and mixed quantum chromatic states leads to a deep understanding of chromatic opposition and its role in the encoding of visual signals. We complete our paper by proving the existence of uncertainty relations for the degree of chromatic opposition, thus providing a theoretical confirmation of the quantum nature of color perception.

Coarse, Medium or Fine? A Quantum Mechanics Approach to Single Species Population Dynamics

Standard heuristic mathematical models of population dynamics are often constructed using ordinary differential equations (ODEs). These deterministic models yield pre-dictable results which allow researchers to make informed recommendations on public policy. A common immigration, natural death, and fission ODE model is derived from a quantum mechanics view. This macroscopic ODE predicts that there is only one stable equilibrium point . We therefore presume that as t → ∞, the expected value should be . The quantum framework presented here yields the same standard ODE model, however with very unexpected quantum results, namely . The obvious questions are: why isn’t , why are the probabilities ≈ 0.37, and where is the missing probability of 0.26? The answer lies in quantum tunneling of probabilities. The goal of this paper is to study these tunneling effects that give specific predictions of the uncertainty in the population at the macroscopic level. These quantum effects open the possibility of searching for “black–swan” events. In other words, using the more sophisticated quantum approach, we may be able to make quantitative statements about rare events that have significant ramifications to the dynamical system.

Breast Cancer Detection Using Quantum Convolutional Neural Networks: A Demonstration on a Quantum Computer

Deep learning have paved the way for scientists to achieve great technical feats. In an endeavor to hone and perfect these techniques, quantum deep learning is a promising and important tool to utilize to the fullest. Using the techniques of deep learning and supervised learning in a quantum framework, we are able to propose a quantum convolutional neural network and showcase its implementation. Using the techniques of deep learning and supervised learning in a quantum framework, we are able to propose a quantum convolutional neural network and showcase its implementation. We keep our focus on training of the ten qubits system in a way so that it can learn from labeling of the breast cell data of Wisconsin breast cancer database and optimize the circuit parameters to obtain the minimum error. Through our study, we also have showcased that quantum convolutional neural network can outperform its classical counterpart not only in terms of accuracy but also in the aspect of better time complexity.

Exciton-plasmon polariton coupling and hot carrier generation in two-dimensional SiB semiconductors: a first-principles study

Exciton (strong electron–hole interactions) and hot carriers (HCs) assisted by surface plasmon polaritons show promise to enhance the photoresponse of nanoelectronic and optoelectronic devices. In the current research, we develop a computational quantum framework to study the effect of coupled exciton and HCs on the photovoltaic energy distribution, scattering process, polarizability, and light emission of two-dimensional (2D) semiconductors. Using a stable 2D semiconductor (semihydrogenated SiB) as our example, we theoretically show that external strain and thermal effect on the SiB can lead to valley polarized plasmon quasiparticles and HC generation. Our results reveal that the electron–phonon and electron–electron (e–e) interactions characterize the correlation between the decay rate, scattering of excitons, and generation of HCs in 2D semiconductors. Moreover, phonon assisted luminescence spectra of SiB suggest that light emission can be enhanced by increasing strain and temperature. The polarized plasmon with strong coupling of electronic and photonics states in SiB makes it as a promising candidate for light harvesting, plasmonic photocurrent devices, and quantum information.

Canonical Divergence for Flat α-Connections: Classical and Quantum

A recent canonical divergence, which is introduced on a smooth manifold M endowed with a general dualistic structure ( g , ∇ , ∇ * ) , is considered for flat α -connections. In the classical setting, we compute such a canonical divergence on the manifold of positive measures and prove that it coincides with the classical α -divergence. In the quantum framework, the recent canonical divergence is evaluated for the quantum α -connections on the manifold of all positive definite Hermitian operators. In this case as well, we obtain that the recent canonical divergence is the quantum α -divergence.

Quantum-inspired minimum distance classification in a biomedical context

We propose an application of a quantum-inspired version of the Nearest Mean Classifier (NMC) (G. Sergioli, E. Santucci, L. Didaci, J. A. Miszczak and R. Giuntini, A quantum inspired version of the NMC classifier, Soft Comput. 22(3) (2018) 691. G. Sergioli, G. M. Bosyk, E. Santucci and R. Giuntini, A quantum-inspired version of the classification problem, Int. J. Theo. Phys. 56(12) (2017) 3880. E. Santucci and G. Sergioli, Classification problem in a quantum framework, in quantum foundations, probability and information, Proc. Quantum and Beyond Conf., 13–16 June 2016, Vaxjo, Sweden, A. Khrennikov and T. Bourama, Springer-Berlin, Germany, 2018 (in press, 2018). E. Santucci, Quantum minimum distance classifier, Entropy 19(12) (2017) 659.) to a biomedical context. In particular, we benchmark the performances of such a quantum-variant of NMC against NMC and other (nonlinear) classifiers with respect to the problem of classifying the probability of survival for patients affected by idiopathic pulmonary fibrosis (IPF).