Training machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

Surface-enhanced Raman scattering (SERS) is an ultrahigh sensitive detection technique for a variety of research fields. Both electromagnetic and chemical enhancement mechanisms are generally considered to contribute simultaneously to SERS signals. However, it is difficult to actively control the enhancement of SERS signals after the substrate is fabricated, since tuning one or both of the aforementioned enhancement mechanisms remains an experimental challenge.Here, we propose a method for actively implementing the photoinduced modulation of SERS signals, which is that under UV irradiation, the Fermi level of graphene can be dynamically modulated due to the adsorption and desorption of gas molecules. The method is validated in gas atmospheres of O 2 , CO 2 , N 2 , and air and also demonstrate its generality by different analytes. In addition, the method was successfully applied to the trace detection of pesticides on fruit peels in air environment, which show its practical implications in sensing.

The short timescale spin dynamics in antiferromagnets is an attractive feature from the standpoint of ultrafast spintronics. Yet generating highly polarized spin-current at room temperature remains a fundamental challenge for antiferromagnets. We propose a spin circular photogalvanic effect (spin-CPGE), in which circularly polarized light can produce a highly spin-polarized current at room temperature, through an "injection-current-like" mechanism in parity-time (PT) -symmetric antiferromagnetic (AFM) insulators. We demonstrate this effect by first-principles simulations of bilayer CrI3 and room-temperature AFM hematite. The spin-CPGE is significant, and the magnitude of spin photocurrent is comparable with the widely observed charge photocurrent in ferroelectric materials. Interestingly, this spin photocurrent is not sensitive to spin-orbit interactions, which were regarded as fundamental mechanisms for generating spin-current. Given the fast response of light-matter interactions, large energy scale, and insensitivity to spin-orbit interactions, our work gives hope to realizing fast-dynamic and temperature-robust pure spincurrent in a wide range of PT-symmetric AFM materials, including topological axion insulators and weak-relativistic magnetic insulators.

In this work, we attempt to solve the integerweight knapsack problem using the D-Wave 2000Q adiabatic quantum computer. The knapsack problem is a well-known NP-complete problem in computer science, with applications in economics, business, finance, etc. We attempt to solve a number of small knapsack problems whose optimal solutions are known; we find that adiabatic quantum optimization fails to produce solutions corresponding to optimal filling of the knapsack in all problem instances. We compare results obtained on the quantum hardware to the classical simulated annealing algorithm and two solvers employing a hybrid branch-and-bound algorithm. The simulated annealing algorithm also fails to produce the optimal filling of the knapsack, though solutions obtained by simulated and quantum annealing are no more similar to each other than to the correct solution. We discuss potential causes for this observed failure of quantum adiabatic optimization.