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arXiv:2007.04426 · Physical Review A

Designing a physical quantum agent

Michael J. Kewming, Sally Shrapnel, Gerard J Milburn

Published Mar 12, 2021• 9 citations
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Abstract

A physical agent must interact with its environment using actuators (to act on the environment) and sensors (to read the environment). In this article, the authors show that an optical agent which interacts with its environment using quantum mechanical actuators and sensors can experience a dramatic enhancement in its ability to learn its environment, and they study the thermodynamic properties of learning in such a physical agent.

DOI
10.1103/physreva.103.032411

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A physical agent must interact with its environment using actuators (to act on the environment) and sensors (to read the environment). In this article, the authors show that an optical agent which interacts with its environment using quantum mechanical actuators and sensors can experience a dramatic enhancement in its ability to learn its environment, and they study the thermodynamic properties of learning in such a physical agent.
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.
The ability to gather information and to act upon it is at the core of every learning agent. But what is the impact of quantum mechanics on an agent's ability to sense external inputs and to translate them into actions? Here we address the question for a prototype task of learning agency at the quantum scale: rotating a single spin based on information gathered by a single atom. We determine the ultimate performance limit for this task, revealing a fundamental tradeoff between entanglement at the sensing stage and coherence at the action stage: if the single-atom sensor is not entangled with the quantum system serving as the agent's internal memory, then the best learning strategy requires a coherent transfer of quantum information from the sensor to the system that controls the agent's actions. In contrast, if the sensor is initially entangled with the agent's memory, then the transfer of quantum information is no longer necessary. Our results indicate that the quantum properties of the sensor radically affect the optimal way to convert external stimuli into actions, revealing a link between quantum sensing and the behavior of quantum agents.
Can quantum mechanics help us build intelligent learning agents?A defining signature of intelligent behavior is the capacity to learn from experience.However, a major bottleneck for agents to learn in reallife situations is the size and complexity of the corresponding task environment.Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation.If the environment is impatient, allowing only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all.Here, we show that quantum physics can help and provide a quadratic speedup for active learning as a genuine problem of artificial intelligence.This result will be particularly relevant for applications involving complex task environments.
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.
In this paper we provide a broad framework for describing learning agents in general quantum environments. We analyze the types of classically specified environments which allow for quantum enhancements in learning, by contrasting environments to quantum oracles. We show that whether or not quantum improvements are at all possible depends on the internal structure of the quantum environment. If the environments are constructed and the internal structure is appropriately chosen, or if the agent has limited capacities to influence the internal states of the environment, we show that improvements in learning times are possible in a broad range of scenarios. Such scenarios we call luck-favoring settings. The case of constructed environments is particularly relevant for the class of model-based learning agents, where our results imply a near-generic improvement.