Search Results (11)

Shared quantum-cloud runtimes expose service outcomes shaped by compilation, backend conditions, and scheduling. We study whether workload-dependent information remains in tenant-visible outputs after scheduler mediation, while separating such evidence from provider-side boundary traces and internal diagnostics. Experiments use a provider-inspired prototype with synthetic IBM-style backend abstractions rather than IBM’s proprietary scheduler. Across 16 independent workload seeds, leave-one-seed-out evaluation using provider-reported waiting time yields a mean accuracy of 0.6010 and a classification-based signed advantage of 0.1010 over the majority-class baseline. Monotone padding and delayed release reduce the estimated advantage to 0.0324 and 0.0418, with mean added delays of 0.185 and 0.131 in normalized simulator-time units. Controlled workload pairs and compilation- and backend-level diagnostics show configuration-dependent, nonmonotonic variation across internal observability channels, but do not attribute the external signal to a uniquely quantum mechanism. A six-run experiment on one public backend provides only weak and variable in-sample timing separability. These results demonstrate preliminary scheduler-boundary separability in the controlled prototype, but do not establish a practical public-cloud attack. Full article

We analyze three distinct approaches to time of arrival (TOA) distributions for discrete quantum systems using a spin-$\frac{1}{2}$ particle in a constant magnetic field as a paradigmatic example. We argue that these distributions should not be regarded as competing predictions for the same notion of arrival time, but rather relate to fundamentally different notions whose relevance depends on the physical context. These results are used to analyze information propagation arrival time distributions in XX spin chain systems, and discuss potential applications in quantum information science. Full article

This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load Balancer (SLB), an AI-driven intrusion detection system (AIDS), and a Real-Time Analytics Engine (RAE). These parts use advanced machine learning methods like Support Vector Machines (SVMs), autoencoders, and reinforcement learning (RL) to make the system more flexible, secure, and efficient. The framework uses federated learning (FL) to protect data privacy and make decisions in a decentralized way, which lowers the risks that come with centralizing data. The framework makes load distribution 23.5% more efficient, cuts average wait time by 17.8%, and predicts station-level demand with 94.2% accuracy, according to simulation results. The AI-based intrusion detection component has precision, recall, and F1-scores that are all over 97%, which is better than standard methods. The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent. Full article

In this paper, the problem of charging electric motor vehicles on a motorway is considered. Charging points are located alongside the motorway. It is assumed that there are a number of vehicles on a given section of a motorway. In the motorway, there are several nodes, and for each vehicle, the entering and the leaving nodes are known, as well as the time of entrance. For each vehicle, we know the total capacity of its battery, and the current amount of energy in the battery when entering the motorway. It is also assumed that for each vehicle, there is a finite set of speeds it can use when traveling the motorway. The speed is chosen when entering the motorway, and cannot be changed before reaching the charging station. For each speed, there is given a corresponding power usage; the higher the speed, the larger the power usage. Each vehicle can only use one charger, and when its battery is full, the amount of energy is sufficient for reaching the outgoing node. We look for a feasible solution to the problem, i.e., a solution in which no vehicle has to wait for a charger. The problem is formulated as a problem of scheduling independent, nonpreemptable jobs in parallel, unrelated machines under an additional doubly constrained resource, which is power. Quantum approaches to solve the defined problem are proposed. They use the quantum approximate optimization algorithm and the quantum annealing technique. A computational experiment is presented and discussed. Some conclusions and directions for future research are given. Full article

The uncertainty of the arrival time of trucks has increased the complexity of terminal operations. The truck appointment system (TAS) cannot respond to this problem in time, which can easily cause appointment invalidation and reduce the efficiency of truck operations and terminal operations. This paper comprehensively considers the related constraints of truck re-scheduling costs, gate waiting costs, and idle emission costs. With the goal of minimizing the comprehensive operating costs of truck companies and port companies, a dynamic appointment rescheduling model for external trucks based on mixed integer nonlinear programming is established. This paper designs an adaptive quantum revolving door update mechanism and proposes a double-chain real quantum genetic algorithm. The simulation experiment results show that compared with the traditional scheduling, the truck dynamic appointment rescheduling model can effectively reduce the comprehensive operating costs of the truck company and the port company and alleviate the congestion of the port. The probability that the truck cannot arrive at the port on time, the advance time for the truck to confirm the arrival time, and the length of time that the external truck cannot arrive at the port on time have a significant impact on the cost of the reschedule of the TAS. This paper favorably supports the manager’s operational decision-making. Full article

We extend collisional quantum thermometry schemes to allow for stochasticity in the waiting time between successive collisions. We establish that introducing randomness through a suitable waiting time distribution, the Weibull distribution, allows us to significantly extend the parameter range for which an advantage over the thermal Fisher information is attained. These results are explicitly demonstrated for dephasing interactions and also hold for partial swap interactions. Furthermore, we show that the optimal measurements can be performed locally, thus implying that genuine quantum correlations do not play a role in achieving this advantage. We explicitly confirm this by examining the correlation properties for the deterministic collisional model. Full article

Simple, controllable models play an important role in learning how to manipulate and control quantum resources. We focus here on quantum non-Markovianity and model the evolution of open quantum systems by quantum renewal processes. This class of quantum dynamics provides us with a phenomenological approach to characterise dynamics with a variety of non-Markovian behaviours, here described in terms of the trace distance between two reduced states. By adopting a trajectory picture for the open quantum system evolution, we analyse how non-Markovianity is influenced by the constituents defining the quantum renewal process, namely the time-continuous part of the dynamics, the type of jumps and the waiting time distributions. We focus not only on the mere value of the non-Markovianity measure, but also on how different features of the trace distance evolution are altered, including times and number of revivals. Full article

Recently, there has been significant growth in the popularity of cloud computing systems. One of the main issues in building cloud computing systems is task scheduling. It plays a critical role in achieving high-level performance and outstanding throughput by having the greatest benefit from the resources. Therefore, enhancing task scheduling algorithms will enhance the QoS, thus leading to more sustainability of cloud computing systems. This paper introduces a novel technique called the dynamic round-robin heuristic algorithm (DRRHA) by utilizing the round-robin algorithm and tuning its time quantum in a dynamic manner based on the mean of the time quantum. Moreover, we applied the remaining burst time of the task as a factor to decide the continuity of executing the task during the current round. The experimental results obtained using the CloudSim Plus tool showed that the DRRHA significantly outperformed the competition in terms of the average waiting time, turnaround time, and response time compared with several studied algorithms, including IRRVQ, dynamic time slice round-robin, improved RR, and SRDQ algorithms. Full article

In this work, the waiting time distribution (WTD) statistics of electron transport through a two-channel quantum system in a strong Coulomb blockade regime and non-interacting dots are investigated by employing a particle-number resolved master equation with the Born–Markov approximation. The results show that the phase difference between the two channels, the asymmetry of the dot-state couplings to the left and right electrodes, and Coulomb repulsion have obvious effects on the WTD statistics of the system. In a certain parameter range, the system manifests the coherent oscillatory behavior of WTDs in the strong Coulomb blockade regime, and the phase difference between the two channels is clearly reflected in the oscillation phase of the WTDs. The two-channel quantum dot (QD) system for non-interacting dots manifests nonrenewal characteristics, and the electron waiting time of the system is negatively correlated. The different phase differences between the two channels can clearly enhance the negative correlation. These results deepen our understanding of the WTD statistical properties of electron transport through a mesoscopic QD system and help pave a new path toward constructing nanostructured QD electronic devices. Full article

The statistical properties of photon emission counting, especially the waiting time distributions (WTDs) and large deviation statistics, of a cavity coupled with the system of double quantum dots (DQDs) driven by an external microwave field were investigated with the particle-number-resolved master equation. The results show that the decay rate of the WTDs of the cavity for short and long time limits can be effectively tuned by the driving external field Rabi frequency, the frequency of the cavity photon, and the detuning between the microwave driving frequency and the energy-splitting of the DQDs. The photon emission energy current will flow from the thermal reservoir to the system of the DQDs when the average photon number of the cavity in a steady state is larger than that of the thermal reservoir; otherwise, the photon emission energy current will flow in the opposite direction. This also demonstrates that the effect of the DQDs can be replaced a thermal reservoir when the rate difference of a photon absorbed and emitted by DQDs is larger than zero; otherwise, it is irreplaceable. The results deepen our understanding of the statistical properties of photon emission counting. It has a promising application in the construction of nanostructured devices of photon emission on demand and of optoelectronic devices. Full article

With the aim of reducing cost, carbon emissions, and service periods and improving clients’ satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time. Full article