Quantum Computing &Amp; Reinforcement Learning: Partners in Achieving Artificial General Intelligence

Samuel Yen-Chi Chen

Hafsa Ihteshamuddin Ahmed, Thakur Monika Singh, Dadireddy Manoj Kumar Reddy

Ved Seetohul, Hamid Jahankhani, Stefan Kendzierskyj +1 more

Gyu Seon Kim, Jaehyun Chung, Soyi Jung +2 more

Reusable rockets have emerged as a pivotal technology for cost reduction and efficiency improvement in the space industry. Joint control, communication, and computing technologies must be considered for the mission performance of these reusable rockets. This paper introduces a quantum reinforcement learning for the joint control, communication, and computing (QRL-C3) algorithm designed for quantum reusable rockets. The QRL-C3 aims to solve the issue of diminished training performance in high-dimensional states and action spaces prevalent in conventional reinforcement learning (RL). Leveraging quantum neural networks (QNN) with superposition and entanglement, QRL-C3 enhances information representation and processing. The framework integrates a (i) robust quantum control system for precise re-entry and landing, a (ii) quantum multi-agent reinforcement learning (QMARL)-based scheduler for seamless communication with multiple ground stations (GSs) for enabling Internet-of-Things (IoT) services, and (iii) energy-efficient computing algorithms considering queue backlog for optimal image processing and transmission power management. These components synergistically improve mission performance and rocket re-usability. Experimental validation using SpaceX's Falcon 9 model demonstrates that QRL-C3 outperforms conventional RL approaches, achieving higher training efficiency. This study is the first to integrate control, communication, and computing systems in reusable rockets through quantum artificial intelligence (QAI), paving the way for future advancements in QAI-based space technologies.

Leonhard Kunczik

Sumin Wang, Ning Wang, Tongyu Ji +2 more

The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence (AI). As Moore's Law approaches its limit, it is imperative to break the constraints that are holding back artificial intelligence. Quantum computing and artificial intelligence have been advancing along the highway of human civilization for many years, emerging as new engines driving economic and social development. This article delves into the integration of quantum computing and artificial intelligence in both research and application. It introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum effects. The discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives: quantum supervised learning, quantum unsupervised learning, quantum reinforcement learning, and quantum deep learning. In an effort to address the limitations of smart cities, this article explores the formidable potential of quantum artificial intelligence in the realm of smart cities. It does so by examining aspects such as intelligent transportation, urban operation assurance, urban planning, and information communication, showcasing a plethora of practical achievements in the process. In the foreseeable future, Quantum Artificial Intelligence (QAI) is poised to bring about revolutionary development to smart cities. The urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers, constructing an efficient, stable, and adaptive hybrid computing architecture that integrates quantum and classical computing, preparing quantum data as needed, and advancing controllable qubit hardware equipment to meet actual demands. The ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities, robustness, excellent generalization capabilities, and interpretability.