The use of quantum annealers in black-box optimization to obtain the desired properties of a product with a small number of trials has attracted attention. However, the application of this technique to engineering design problems has been limited. Here, we demonstrate the applicability of black-box optimization with a quantum annealer to the design of electric circuit systems, focusing on π-type noise filters as an example. We develop a framework that uses quantum annealing to find the optimal location of electrical components and conductor paths connecting the components, and confirm that the learning process appropriately works over a number of trials to efficiently search for a design with high performance. The results show the potential applicability of quantum annealing to design problems of electric circuit systems.

The rectification of electro-osmotic flows is important in micro- and nano-fluidics applications such as micropumps and energy conversion devices. Here, we propose a simple electro-osmotic diode in which colloidal particles are contained between two parallel membranes with different pore densities. While the flow in the forward direction just pushes the colloidal particles toward the high-pore-density membrane, the backward flow is blocked by the particles near the low-pore-density membrane, which clog the pores. Nonequilibrium molecular dynamics simulations show a strong nonlinear dependence on the electric field for both the electric current and electro-osmotic flow, indicating diode characteristics. A mathematical model to reproduce the electro-osmotic diode behavior is constructed, introducing an effective pore diameter as a model for pores clogged by the colloidal particles. Good agreement is obtained between the proposed model with estimated parameter values and the results of direct molecular dynamics simulations. The proposed electro-osmotic diode has potential application in downsized microfluidic pumps, e.g., the pump induced under AC electric fields.

The development of quantum annealing has stimulated interest in solving NP-hard problems, including various industrial problems, such as quadratic unconstrained binary optimization (QUBO), with specialized solvers. This study focuses on the rectangle packing problem (RPP), which is an NP-hard problem applicable to industrial applications. The objective of this problem is to place given items in a minimum area without overlap. Naive QUBO representations, such as a direct binary encoding of locations, could require a large number of bits, which limits the problem size, hindering the acquisition of the optimal solution. To circumvent this difficulty, we employ the concept of sequence pairs to efficiently represent the locations of items. We also propose a strategy that uses multiple sampling solutions obtained with a QUBO solver, where we employ new constraints on the number of the rectangles contributing to the placement area for restricting the search space to smoothly bridge the QUBO formulation of the sequence pair and the minimization of area of rectangle placement.We estimate an approximate curve for the optimum number of rectangles contributing to the placement area. Then, we add the penalty term concerning the deviation from the estimated optimal value on the objective function. In numerical experiments with instances with 4 to 10 rectangles, we demonstrate that the proposed sampling-based strategy can efficiently find feasible solutions. This strategy would be useful for dealing with more complex problems, such as rectangle orientation and three-dimensional problems, with a relatively small number of bits using sequence pairs.

To decrease human and economic damage owing to earthquakes, it is necessary to discover signals preceding earthquakes. We focus on the concept of “early warning signals” developed in bifurcation analysis, in which an increase in the variances of variables precedes its transition. If we can treat earthquakes as one of the transition phenomena that moves from one state to the other state, this concept is useful for detecting earthquakes before they start. We develop a covariance matrix from multi-channel time series data observed by an observatory on the seafloor and calculate the first eigenvalue and corresponding eigenstate of the matrix. By comparing the time dependence of the eigenstate to some past earthquakes, it is shown that the contribution from specific observational channels to the eigenstate increases before earthquakes, and there is a case in which the eigenvalue increases as predicted in early warning signals. This result suggests the first eigenvalue and eigenstate of multi-channel data are useful to identify signals preceding earthquakes.

Optimal routing problems of electric vehicles (EVs) have attracted much attention in recent years, and installation of charging stations is an important issue for EVs. Hence, we focus on the joint optimization of the location of charging stations and the routing of EVs. When routing problems are formulated in the form of quadratic unconstrained binary optimization (QUBO), specialized solvers such as quantum annealers are expected to provide optimal solutions with high speed and accuracy. However, battery capacity constraints make it hard to formulate into QUBO form without a large number of auxiliary qubits. Here, we propose a sequential optimization method utilizing the Bayesian inference and QUBO solvers, in which the battery capacity constraints are automatically learned. This method enables us to optimize the number and location of charging stations and the routing of EVs with a small number of searches. Applying this method to a routing problem of 20 locations, we observed consistent convergence toward battery-feasible solutions across independent runs, demonstrating stable learning behavior of the proposed framework. Small-scale validation experiments using exhaustive enumeration show that the framework reliably discovers feasible configurations close to the global optimum, while runtime and QUBO-size analyses clarify its computational characteristics.

The use of quantum annealers in black-box optimization to obtain the desired properties of a product with a small number of trials has attracted attention. However, the application of this technique to engineering design problems is still limited. Here, we demonstrate the applicability of black-box optimization with a quantum annealer to the design of electric circuit systems, focusing on $π$-type noise filters as an example. We develop a framework that uses quantum annealing to find the optimal location of electrical components and conductor paths connecting the components, and confirm that the learning process appropriately works over a number of trials to efficiently search for a design with high performance. The results show the potential applicability of quantum annealing to design problems of electric circuit systems.