Improving Urban Traffic Mobility via a Versatile Quantum Annealing Model

The growth of cities and the resulting increase in vehicular traffic pose significant challenges to the environment and citizens’ quality of life. To address these challenges, a new algorithm has been proposed that leverages the quantum annealing paradigm and D-wave’s machines to optimize the control of traffic lights in cities. The algorithm considers traffic information […]

Analysis of the Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in the Presence of Noisy Channels

The vehicle routing problem (VRP) is an NP-hard optimization problem that has been an interest of research for decades in science and industry. The objective is to plan routes of vehicles to deliver goods to a fixed number of customers with optimal efficiency. Classical tools and methods provide good approximations to reach the optimal global […]

Extensible Gauge-Invariant FDM With Spin–Orbit Coupling for Quantum Devices

We present a novel derivation and implementation of the finite-difference method (FDM) that is gauge invariant and incorporates spin–orbit coupling for the study of quantum systems. This version of FDM is meant to assist in the design and simulation of quantum devices that utilize multiple internal degrees of freedom (e.g., spin) by providing a way […]

Prediction of Solar Irradiance One Hour Ahead Based on Quantum Long Short-Term Memory Network

The short-term forecasting of photovoltaic (PV) power generation ensures the scheduling and dispatching of electrical power, helps design a PV-integrated energy management system, and enhances the security of grid operation. However, due to the randomness of solar energy, the output of the PV system will fluctuate, which will affect the safe operation of the grid. […]

Efficient Quantum State Preparation for the Cauchy Distribution Based on Piecewise Arithmetic

The benefits of the quantum Monte Carlo algorithm heavily rely on the efficiency of the superposition state preparation. So far, most reported Monte Carlo algorithms use the Grover–Rudolph state preparation method, which is suitable for efficiently integrable distribution functions. Consequently, most reported works are based on log-concave distributions, such as normal distributions. However, non-log-concave distributions […]