Quantum information science
Related papers: 4
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Quantum information science is a multidisciplinary field that applies principles of quantum mechanics—such as superposition, entanglement, and quantum interference—to the storage, processing, and transmission of information. Unlike classical bits, quantum bits (qubits) can exist in multiple states simultaneously, enabling computational capabilities that far exceed classical systems for certain problem classes. In robotics and AI, quantum information science is being explored to enhance multi-agent coordination, secure communications, and reinforcement learning, where quantum algorithms may offer exponential speedups in decision-making and optimization tasks. For example, quantum entanglement enables theoretically unhackable cryptographic channels between robotic systems, while quantum multi-agent reinforcement learning frameworks promise more efficient distributed learning in complex environments. A key challenge remains quantum decoherence, where environmental noise degrades qubit integrity, limiting practical deployment. Nevertheless, the field matters because it opens entirely new computational paradigms for autonomous systems, potentially transforming how robots perceive, communicate, and act—particularly as quantum hardware continues to mature and integrate with classical robotic architectures.
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Qubit devices and the issue of quantum decoherence
Howard E. Brandt
Citations: 98 • 1999
Automated Quantum Entanglement and Cryptography for Networks of Robotic Systems
Farbod Khoshnoud, Maziar Ghazinejad
Citations: 4 • 2021
Quantum Cooperative Robotics and Autonomy
Farbod Khoshnoud, Marco B. Quadrelli, I.I. Esat, Dario Robinson
Citations: 3 • 2020
Quantum Multi-Agent Reinforcement Learning as an Emerging AI Technology: A Survey and Future Directions
Wenhan Yu
Citations: 2 • 2023