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Quantum enhanced agents

Michael J. Kewming, Sally Shrapnel, Gerard J Milburn

Published Jul 8, 2020• 0 citations
OpenAlex

Abstract

The concept of an embodied intelligent agent is a key concept in modern AI and robotics. Physically, an agent---like a Turing machine---is an open system embedded in an environment which it interacts with through sensors and actuators. It contains a learning algorithm that correlates the sensor and actuator results by learning features about its environment. The sensor-actuator system is similar to a measurement based control system. Quantum mechanics enables new measurement and control protocols capable of exceeding what can be achieved classically. We demonstrate how quantum optical sensors and actuators can dramatically improve an agent's ability to learn in a thermal environment. Furthermore we use the Jarzynski equality to show that learning maximises the exchange in free energy $\Delta F$ between the agent's sensor and actuator when considered as a stochastic feedback cycle.

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The concept of an embodied intelligent agent is a key concept in modern AI and robotics. Physically, an agent---like a Turing machine---is an open system embedded in an environment which it interacts with through sensors and actuators. It contains a learning algorithm that correlates the sensor and actuator results by learning features about its environment. The sensor-actuator system is similar to a measurement based control system. Quantum mechanics enables new measurement and control protocols capable of exceeding what can be achieved classically. We demonstrate how quantum optical sensors and actuators can dramatically improve an agent's ability to learn in a thermal environment. Furthermore we use the Jarzynski equality to show that learning maximises the exchange in free energy ΔF\Delta FΔF between the agent's sensor and actuator when considered as a stochastic feedback cycle.
As the field of artificial intelligence is pushed forward, the question arises of how fast autonomous machines can learn. Within artificial intelligence, an important paradigm is reinforcement learning, where agents - learning entities capable of decision making - interact with the world they are placed in, called an environment. Thanks to these interactions, agents receive feedback from the environment and thus progressively adjust their behaviour to accomplish a given goal. An important question in reinforcement learning is how fast agents can learn to fulfill their tasks. To answer this question we consider a novel reinforcement learning framework where quantum mechanics is used. In particular, we quantize the agent and the environment and grant them the possibility to also interact quantum-mechanically, that is, by using a quantum channel for their communication. We demonstrate that this feature enables a speed-up in the agent's learning process, and we further show that combining this scenario with classical communication enables the evaluation of such an improvement. This learning protocol is implemented on an integrated re-programmable photonic platform interfaced with photons at telecommunication wavelengths. Thanks to the full tunability of the device, this platform proves the best candidate for the implementation of learning protocols, where a continuous update of the learning process is required.
The chapter examines the integration of Agentic Artificial Intelligence (Agentic AI) and quantum computing, highlighting the emergence of quantum agents that enhance decision-making beyond classical limits. It reviews existing research in quantum machine learning and proposes a Quantum-Agentic Architecture (QAA) that combines classical AI with quantum processors to improve operational efficiency. Mathematical models for quantum-enhanced learning, including reinforcement learning equations, are presented, along with a comparative analysis of classical and quantum-enhanced agents in terms of complexity and convergence metrics. Experimental simulations show significant performance improvements in multi-agent environments. The chapter also addresses challenges such as noisy intermediate-scale quantum (NISQ) limitations and ethical considerations in the deployment of autonomous quantum intelligence systems.
Autonomous agents are being used in more complex and dynamic environments where efficient learning, adaptive decision-making, and scalable optimization are required. Although classical machine learning (ML) and reinforcement methods have greatly contributed to agent intelligence, they are usually restricted by constraints of computational complexity, scaling, and convergence to high-dimensional decision space. The current developments in quantum computing have created new possibilities to solve these problems using quantum ML (QML). In this chapter, the authors discuss the implementation of QML in collaboration with autonomous agents in the larger paradigm of agentic artificial intelligence (AI) and quantum AI agents. It offers a hybrid classical–quantum architectural model where quantum learning modules improve perception, policy learning, and decision optimization, and do not conflict with classical agent pipelines. The chapter analyzes application cases in autonomous robotics, intelligent transportation systems, multi-agent coordination, smart energy management, as well as communication networks, and the chapter shows the potential benefits of quantum-enhanced learning in these areas. The issue of quantum hardware neediness, data encoding, scaling, hybrid integration, and real-life application are critically evaluated and unanswered. In summary, the chapter is a well-organized approach to the purpose of QML in the development of the next-generation autonomous agent and an insight into creating practical, scalable, and intelligent quantum AI agents.
This paper investigates the optimization of quantum control algorithms using reinforcement learning (RL). The core claim is that RL can significantly enhance the control precision and efficiency of quantum devices. We propose a framework where the quantum control algorithm is treated as an intelligent agent, interacting with the quantum environment to learn optimal control strategies. This approach allows for adaptive control, addressing the challenges inherent in designing fixed control protocols for complex quantum systems. The methodology involves defining a reward function that reflects the desired quantum state evolution, using RL algorithms to train the agent, and validating the learned control strategy through simulations. We demonstrate the potential of this approach to improve control accuracy and robustness, particularly in scenarios with noisy environments or rapidly changing system parameters. The results suggest a promising direction for leveraging machine learning techniques to unlock the full potential of quantum control.
With multi-agent systems advancing to high-dimensional and uncertain spaces, classical learning and control methods encounter fundamental challenges related to the curse of dimensionality and limited expressivity. Quantum computing offers the potential to overcome the shortcomings by embedding data into exponentially large Hilbert spaces, capturing complex correlations. We first propose a Distributed Quantum Gaussian Process (DQGP) framework enabling agents to collaboratively learn a high-fidelity global model of the environment through improved modeling capabilities and scalability. Numerical evaluations on non-stationary NASA SRTM datasets demonstrate the enhanced predictive and uncertainty estimation performance of DQGP compared to the classical Distributed Gaussian Processes. The findings lead to the next research phase: developing a Quantum-enhanced Learning Model Predictive Control architecture that results in robust, adaptive, and scalable coordination and control of multiple agents in complex scenarios.