Daniel Casado Fauli
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Daniel Casado Fauli has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Parfait Atchade-Adelomou
MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2 shared publications - Elisabet Golobardes
La Salle, Universitat Ramon Llull
1 shared publication - Guillermo Alonso-Linaje
Facultad de Ciencias, Campus Miguel Delibes, Universidad de Valladolid, C/Plaza de Santa Cruz, 8, 47002 Valladolid, Spain
1 shared publication - Jordi Albo-Canals
CEEO - Tufts University
1 shared publication - Xavier Vilasis Cardona
La Salle, Universitat Ramon Llull
1 shared publication
Latest Publications
- 2 August 2022
- Published by Springer Nature in Artificial Intelligence Review
- Vol. 56 (3) , 2639-2665
Quantum case-based reasoning (qCBR)
- 2 August 2022
- Published by Springer Nature in Artificial Intelligence Review
- Vol. 56 (3) , 2639-2665
Quantum case-based reasoning (qCBR)
- Open Access
- 26 June 2021
- Published by MDPI AG in Algorithms
- Vol. 14 (7) , 194
qRobot: A Quantum Computing Approach in Mobile Robot Order Picking and Batching Problem Solver Optimization
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.
- 26 June 2021
- Published by MDPI AG in Algorithms
- Vol. 14 (7) , 194
Open AccessqRobot: A Quantum Computing Approach in Mobile Robot Order Picking and Batching Problem Solver Optimization
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.