qRobot: A Quantum computing approach in mobile robot order picking and batching problem solver optimization

Abstract

This article aims to bring quantum computing to robotics. A quantum algorithm is developed to minimize the distance travelled in warehouses and distribution centres 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. Before this, we must develop a stable environment (ARM64) inside the robot (Raspberry) to run gradient operations and other quantum algorithms on IBMQ, Amazon Braket, Dwave and Pennylane locally or remotely. The proof of concept will run in such quantum environments above.

EVA: a quantum Exponential Value Approximation algorithm

“…At the same time, a classical optimizer would adjust the parameters of the quantum circuit to find the energy of the ground state of said molecule. Since its publication, the VQE approach has been used in various optimization problems and for quantum chemistry [6][7][8] and finance [9], logistics [10,11], and quantum machine learning [12][13][14][15][16][17]. Little by little, the VQE has become the flagship of quantum computing, and optimization problems [18].…”