This article aims to bring quantum computing to robotics. A quantum algorithm is developed to minimize the distance traveled in warehouses and distribution centers where order picking is applied. For this, a proof of concept is proposed through a Raspberry Pi 4, generating a quantum combinatorial optimization algorithm that saves the distance travelled and the batch of orders to be made. In case of computational need, the robot will be able to parallelize part of the operations in hybrid computing (quantum + classical), accessing CPUs and QPUs distributed in a public or private cloud. We developed a stable environment (ARM64) inside the robot (Raspberry) to run gradient operations and other quantum algorithms on IBMQ, Amazon Braket (D-Wave), and Pennylane locally or remotely. The proof of concept, when run in the above stated quantum environments, showed the execution time of our algorithm with different public access simulators on the market, computational results of our picking and batching algorithm, and analyze the quantum real-time execution. Our findings are that the behavior of the Amazon Braket D-Wave is better than Gate-based Quantum Computing over 20 qubits, and that AWS-Braket has better time performance than Qiskit or Pennylane.
This article aims to bring quantum computing to robotics. A quantum algorithm is developed to minimize the distance traveled in warehouses and distribution centers where order picking is applied. For this, a proof of concept is proposed through a Raspberry Pi 4, generating a quantum combinatorial optimization algorithm that saves the distance travelled and the batch of orders to be made. In case of computational need, the robot will be able to parallelize part of the operations in hybrid computing (quantum + classical), accessing CPUs and QPUs distributed in a public or private cloud. We developed a stable environment (ARM64) inside the robot (Raspberry) to run gradient operations and other quantum algorithms on IBMQ, Amazon Braket (D-Wave), and Pennylane locally or remotely. The proof of concept, when run in the above stated quantum environments, showed the execution time of our algorithm with different public access simulators on the market, computational results of our picking and batching algorithm, and analyze the quantum real-time execution. Our findings are that the behavior of the Amazon Braket D-Wave is better than Gate-based Quantum Computing over 20 qubits, and that AWS-Braket has better time performance than Qiskit or Pennylane.
The thesis deals with Quantum Algorithms for solving Hard Constrained Optimization Problems. It shows how quantum computers can solve difficult everyday problems such as finding the best schedule for social workers or the path of a robot picking and batching in a warehouse. The path to the solution has led to the definition of a new artificial intelligence paradigm with quantum computing, quantum Case-Based Reasoning (qCBR) and to a proof of concept to integrate the capacity of quantum computing within mobile robotics using a Raspberry Pi 4 as a processor (qRobot), capable of operating with leading technology players such as IBMQ, Amazon Braket (D-Wave) and Pennylane. To improve the execution time of variational algorithms in this NISQ era and the next, we have proposed EVA: a quantum Exponential Value Approximation algorithm that speeds up the VQE, and that is, to date, the flagship of the quantum computation. To improve the execution time of variational algorithms in this NISQ era and the next, we have proposed EVA: a quantum Exponential Value Approximation algorithm that speeds up the VQE, and that is, to date, the flagship of the quantum computation.
Quantum computing has garnered significant interest in robotics applications over the past few years. Demonstrating its superiority over traditional methods, it has shown many advantages in precision, rapid computation, and robustness for Manipulators and cell robots. In this paper, a remote control-based quantum computation tool is presented to ensure the trajectory tracking of a car-like mobile robot. In the proposed architecture, remote communication between the robot and the controller is achieved using quantum teleportation circuits. The controller is a classic PID controller, with its parameters optimized using a quantum-inspired particle swarm optimization (PSO) algorithm. The control scheme's performance is estimated using the root mean square error (RMSE), calculated from the tracking error, which is the difference between the reference position and the actual position of the robot. Simulation results show that the quantum-inspired PSO-based optimization of the PID controller yielded better performance in terms of prompt convergence and stability. Quantum teleportation also provided high accuracy when transferring control signals and measurement data. This work demonstrates that it is feasible to use quantum computing tools to enhance the real-time performance of long-distance, remotely controlled robotic systems, and that such telecontrol is inherently possible even at great distances or in hostile environments.
Quantum computing has become an increasingly relevant topic in computer science due to the development of functional quantum computers in recent years. Quantum processing units (QPUs) use quantum particles in the form of qubits as information carriers. By using properties of quantum particles, quantum algorithms have been developed that can solve problems in polynomial time, for which only exponentially scaling classical algorithms are known. QPUs will not run on their own in the foreseeable future, due to high error rates and low stability of qubits. Instead, quantum devices will be one component in a quantum-classical (hybrid) computing system (distributed system). Quantum computers embedded in distributed hybrid quantum-classical systems get instructions from a main CPU, execute them and report their results. To fully utilize quantum calculations, a QPU should be able to communicate with a CPU in real-time. This means the qubits information content should stay stable during communication time. In this thesis, we will look at quantum computing systems with real-time feedback between a QPU and a CPU. As quantum computers are going to be used in real-time hybrid systems, we need optimization routines for code running on these systems. Existing works on the optimization of quantum code focus on the optimization of quantum circuits only, and neglect potential classical calculations that are necessary on a hybrid distributed system. We want to examine which kind of optimization routines are possible and sensible for hybrid quantum-classical computations. We will evaluate some of todays quantum programming languages (QPLs), especially with respect to their ability to support and optimize real-time hybrid calculations. We will find that most languages support real-time calculations to some extent, but are more adapted to the programming approach of static circuit creation. Additionally, none of the QPLs offer optimization routines targeted at quantum-classical calculations. Therefore, we will examine options for real-time quantum-classical optimization. For this, we use the low-level QPL Quil. We will introduce optimization operations and apply them to real-time quantum-classical algorithms. We will present metrics to evaluate the performance of hybrid calculations. The results of applying our optimization operations to Quil code will be evaluated against our performance metrics. We will find that we are in principle able to optimize hybrid quantum-classical programs. We encourage more research in this field. QPLs will become more abstract in the future and need more compilation steps until they can be executed on hardware. This will make the optimization of quantum-classical hybrid code more relevant.
Autonomous mobile robots are being used increasingly as consumer devices around the globe, such as for fetching items and cleaning purposes, to name a few, in households and industries. Such robots are being employed to traverse a set of target locations and provide the necessary services. In a 2-dimensional environment, these robots are required to traverse following a Hamiltonian path to reduce energy consumption and time requirements. This problem can be formulated as a Travelling Salesman Problem (TSP), an NP-hard problem. Moreover, upon urgent requirements, these robots must traverse in real-time, demanding speedy path planning from the TSP instance. Among the well-known optimization techniques for solving the TSP problem, Ant Colony Optimization has a good stronghold in providing good approximate solutions. Moreover, ACO not only provides near-optimal solutions for TSP instances but can also output optimal or near-optimal solutions for many other demanding hard optimization problems. However, most of the implementations of Ant Colony Optimization on quantum or hybrid quantum architecture proposed in the literature require conversion of classical data to qubits before being fed to the algorithm, and cannot be automated. But quantum-enabled mobile robots require automated path formation after receiving the commands from the environment. The novelties of the proposed work are many-fold. Firstly, the proposed work allows ACO to be applied as its classical counterparts, allowing automation in path formation in quantum-enabled mobile robots. Secondly, this allows a new way of incorporating quantum processing unit in the research of quantum-enabled mobile robots. Researchers around the globe have been trying to incorporate quantum computing in autonomous mobile robots, and true to the best of authors’ knowledge, no work in path planning for multiple targets in quantum-enabled mobile robots have been found in literature. Thirdly, quantum processing unit has been applied at exactly that point where it will be most useful, as in NISQ era quantum computer is not reliable for arithmetic processing. Simulation results of the proposed Hybrid Quantum Ant Colony Optimization algorithm on several TSP instances have shown promising results with average error percentage from optimumresults of only 6.985%. Hence, it is expected that the proposed work to be important in future research of fusing the two rising domains of quantum computing and autonomous mobile robots.
The article considers a method for constructing a trajectory of a collaborative mobile robot using quantum computing in combination with classical optimization algorithms.The purpose of the study is to develop a mathematical model and numerical simulation of a quantum-oriented approach to constructing a trajectory of a collaborative robot, which allows minimizing energy costs in motion planning and ensuring obstacle avoidance in a dynamic environment.The scientific novelty of the work lies in the application of quantum optimization methods for multivariate planning, which allows avoiding local minima and guaranteeing the search for globally optimal solutions even in the case of complex configuration spaces.During the numerical simulation, it was demonstrated that the formed trajectories ensure successful obstacle avoidance and reaching the target point without deviations from the grid.Conclusions: analysis of the results showed stabilization of the energy function at the level of -350…-420, which confirms the effectiveness of optimization and convergence to the best solutions after 100-150 iterations.The constructed graphs confirmed that the robot's movement is consistent with the calculated quantum plans, and the deviation between the predicted and executed trajectories is minimized.The use of the energy efficiency criterion allowed us to evaluate different route construction scenarios, where the best plans stabilized with a margin of optimality relative to the worst options by 15-20%.Qualitative analysis of the constructed trajectories confirmed the consistency between the predicted and executed paths, and quantitative results proved a reduction in the number of steps when reaching the target point.The results obtained prove that the proposed method is promising for constructing reliable and energy-optimal trajectories in collaborative robotic systems Industry 5.0.
Quantum computation injection into robot automation opens up new avenues for boosting multi-robot complicated task scheduling and real-time adaptive control. Conventional optimization methods barely keep up with the challenges of scalability and efficiency in dynamic environments, thus becoming one of the bottlenecks for successful robotic coordination. This paper puts forward two leading quantum algorithms, Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigen solver (VQE), as a remedy to these shortcomings. Through the use of quantum parallelism and hybrid quantum-classical architectures, the intended solution is to elevate task assignment effectiveness and interactive response capacity in multi-robotic systems. Among other things, the proposed work aims at setting up a quantum inspired optimization model to schedule a task, an adaptive and real-time decision-support control framework, and the experimental demonstration of the algorithm's capability to handle complexity and high volume in robotic environments. Computer-based simulations alongside actual experiments, as a matter of fact, will be able to outperform regular optimizations, hence proving the transformative potentials of quantum algorithms on robotic automation.