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In cellular-connected unmanned aerial vehicle (UAV) network, a minimization problem on the weighted sum of time cost and expected outage duration is considered. Taking advantage of UAV’s adjustable mobility, a UAV navigation approach is formulated to achieve the aforementioned optimization goal. Conventional offline optimization techniques suffer from inefficiency in accomplishing the formulated UAV navigation task due to the practical consideration of local building distribution and directional antenna radiation pattern. Alternatively, after mapping the navigation task into a Markov decision process (MDP), a deep reinforcement learning (DRL)-aided solution is proposed to help the UAV find the optimal flying direction within each time slot, and thus the designed trajectory towards the destination can be generated. To help the DRL agent commit a better trade-off between sampling priority and diversity, a novel quantum-inspired experience replay (QiER) framework is proposed, via relating experienced transition’s importance to its associated quantum bit (qubit) and applying Grover iteration based amplitude amplification technique. Compared to several representative DRL-related and non-learning baselines, the effectiveness and supremacy of the proposed DRL-QiER solution are demonstrated and validated in numerical results. [ABSTRACT FROM AUTHOR]

In this article, a novel training paradigm inspired by quantum computation is proposed for deep reinforcement learning (DRL) with experience replay. In contrast to the traditional experience replay mechanism in DRL, the proposed DRL with quantum-inspired experience replay (DRL-QER) adaptively chooses experiences from the replay buffer according to the complexity and the replayed times of each experience (also called transition), to achieve a balance between exploration and exploitation. In DRL-QER, transitions are first formulated in quantum representations and then the preparation operation and depreciation operation are performed on the transitions. In this process, the preparation operation reflects the relationship between the temporal-difference errors (TD-errors) and the importance of the experiences, while the depreciation operation is taken into account to ensure the diversity of the transitions. The experimental results on Atari 2600 games show that DRL-QER outperforms state-of-the-art algorithms, such as DRL-PER and DCRL on most of these games with improved training efficiency and is also applicable to such memory-based DRL approaches as double network and dueling network. [ABSTRACT FROM AUTHOR]

For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously. Traditional methods usually maintain an internal map of the environment, and then design several simple rules, in conjunction with a localization and planning approach, to navigate through the internal map. These approaches often involve a variety of assumptions and prior knowledge. In contrast, recent reinforcement learning (RL) methods can provide a model-free, self-learning mechanism as the robot interacts with an initially unknown environment, but are expensive to deploy in real-world scenarios due to inefficient exploration. In this article, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of time. First, we use the rule of wall-following to generate a closed-loop trajectory. Second, we employ a reduction rule to shrink the trajectory, which in turn effectively reduces the redundant exploration space. Besides, we give the detailed theoretical guarantee that the optimal navigation path is still in the reduced space. Third, in the reduced space, we utilize the Pledge rule to guide the exploration strategy for accelerating the RL process at the early stage. Experiments conducted on real robot navigation problems in hex-grid environments demonstrate that RuRL can achieve improved navigation performance. [ABSTRACT FROM AUTHOR]

A hybrid quantum–classical filtering problem, where a qubit system is disturbed by a classical stochastic process, is investigated. The strategy is to model the classical disturbance by using an optical cavity. The relations between classical disturbances and the cavity analog system are analyzed. The dynamics of the enlarged quantum network system, which includes a qubit system and a cavity system, are derived. A stochastic master equation for the qubit–cavity hybrid system is given, based on which estimates for the state of the cavity system and the classical signal are obtained. The quantum-extended Kalman filter is employed to achieve efficient computation. The numerical results are presented to illustrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

In this brief, we present a Hamiltonian-identification method for a closed quantum system whose time-trace observables are measured with colored measurement noise. The dynamics of the quantum system are described by a Liouville equation that can be converted into a coherence vector representation. Since the measurement process is disturbed by classical colored noise, we introduce an augmented system model to describe the total dynamics, where the classical colored noise is parameterized. Based on the augmented system model as well as the measurement data, we can find a realization of the quantum system with unknown parameters by employing an eigenstate-realization algorithm. The unknown parameters can be identified using a transfer-function-based technique. An example of a two-qubit system with colored measurement noise is shown to verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Quantum detector tomography is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, a novel quantum detector tomography method is proposed. First, a series of different probe states are used to generate measurement data. Then, using constrained linear regression estimation, a stage-1 estimation of the detector is obtained. Finally, the positive semidefinite requirement is added to guarantee a physical stage-2 estimation. This Two-stage Estimation (TSE) method has computational complexity $O(nd^{2}M)$ , where $n$ is the number of $d$ -dimensional detector matrices and $M$ is the number of different probe states. An error upper bound is established, and optimization on the coherent probe states is investigated. We perform simulation and a quantum optical experiment to testify the effectiveness of the TSE method. [ABSTRACT FROM AUTHOR]

This paper solves the problem of discrete-time fault-tolerant quantum filtering for a class of laser–atom open quantum systems subject to the stochastic faults. We show that by using the discrete-time quantum measurements, optimal estimates of both the atomic observables and the classical fault process can be simultaneously determined in terms of recursive quantum stochastic difference equations. A dispersive interaction quantum system example is used to demonstrate the proposed filtering approach. [ABSTRACT FROM AUTHOR]

Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry, and atomic physics. In this paper, an improved differential evolution algorithm, referred to as multiple-samples and mixed-strategy DE (msMS_DE), is proposed to search robust fields for various quantum control problems. In msMS_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMS_DE algorithm is applied to the control problems of: 1) open inhomogeneous quantum ensembles and 2) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMS_DE is experimentally implemented on femtosecond (fs) laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH2BrI. The experimental results demonstrate the excellent performance of msMS_DE in searching for effective fs laser pulses for various tasks. [ABSTRACT FROM AUTHOR]

This paper proposes a new multilayer Lyapunov control scheme based on energy-level connectivity graphs of quantum systems, which enables a high-probability population transfer to any eigenstate in high-dimensional quantum systems. We provide a graph connectivity result for stability analysis and explore the selectivity characteristic of the Lyapunov control fields. A systematic layering method for the energy-level connectivity graph is presented to achieve a layer-by-layer population transfer to the target state. Several applications including a five-level quantum system, a three-qubit superconducting system, and a spin chain of five qubits are presented to demonstrate the effectiveness of the multilayer Lyapunov control method. [ABSTRACT FROM AUTHOR]

Quantum measurements disturb the quantum system being measured, and this is known as measurement-induced backaction. In this paper, we consider a double quantum dot monitored by a nearby quantum point contact, where the measurement-induced backaction plays an important role. Taking advantage of the quantum master equation approach, we calculate the tunneling current and propose a simple feedback-control law to realize and stabilize the tunneling current. Theoretical analysis and numerical simulations show that the feedback control law can make the current quickly convergent to the desired value. [ABSTRACT FROM AUTHOR]

We present filtering equations for a class of hybrid quantum-classical systems where a quantum optical cavity system is continuously disturbed by a classical linear stochastic process. Another quantum cavity system is employed to describe the classical stochastic process as an analog. The initial hybrid quantum-classical system consisting of both a classical signal and a quantum cavity system is then modeled by a combined quantum system with two quantum cavity systems. The stochastic master equations, or filtering equations, are then derived using quantum filtering theory. A quantum extended Kalman filter method is adopted as a convenient solution to this filtering problem. The effectiveness of both methods is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, a novel incremental learning algorithm is presented for reinforcement learning (RL) in dynamic environments, where the rewards of state-action pairs may change over time. The proposed incremental RL (IRL) algorithm learns from the dynamic environments without making any assumptions or having any prior knowledge about the ever-changing environment. First, IRL generates a detector-agent to detect the changed part of the environment (drift environment) by executing a virtual RL process. Then, the agent gives priority to the drift environment and its neighbor environment for iteratively updating their state-action value functions using new rewards by dynamic programming. After the prioritized sweeping process, IRL restarts a canonical learning process to obtain a new optimal policy adapting to the new environment. The novelty is that IRL fuses the new information into the existing knowledge system incrementally as well as weakening the conflict between them. The IRL algorithm is compared to two direct approaches and various state-of-the-art transfer learning methods for classical maze navigation problems and an intelligent warehouse with multiple robots. The experimental results verify that IRL can effectively improve the adaptability and efficiency of RL algorithms in dynamic environments. [ABSTRACT FROM AUTHOR]