Abstract
Central to the success of adaptive systems is their ability to interpret signals from their environment and respond accordingly—they act as agents interacting with their surroundings. Such agents typically perform better when able to execute increasingly complex strategies. This comes with a cost: the more information the agent must recall from its past experiences, the more memory it will need. Here we investigate the power of agents capable of quantum information processing. We uncover the most general form a quantum agent need adopt to maximize memory compression advantages and provide a systematic means of encoding their memory states. We show these encodings can exhibit extremely favorable scaling advantages relative to memory-minimal classical agents, particularly when information must be retained about events increasingly far into the past.
- Received 21 August 2020
- Revised 5 October 2021
- Accepted 2 November 2021
DOI:https://doi.org/10.1103/PhysRevX.12.011007
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
Published by the American Physical Society
Physics Subject Headings (PhySH)
Popular Summary
To thrive in ever-changing environments, systems must be able to adapt their actions to respond appropriately to the stimuli they receive. These adaptive systems, or agents, exist at all scales, from microscopic bacteria to self-driving vehicles. Common to all is that they interact and compete with other agents, mounting a drive to develop and deploy increasingly complex strategies. This requires the agent to track ever more information about past and present events, imposing a performance bottleneck and making tools for efficient distillation of relevant information essential. We show that quantum processing can provide a competitive edge, by allowing agents to execute considerably more complex strategies than classical counterparts with access to the same memory capacity.
We pinpoint precisely which quantum effects are behind these advantages and what structures an agent should adopt to maximize efficiency, allowing them to execute more complex strategies with a smaller memory. We provide a systematic means of encoding strategies for quantum agents that harness these advantages. Moreover, we show that the quantum enhancement in efficiency can scale without bound.
The utility of these findings extends beyond the agents themselves. Agent-based modeling and adaptive systems permeate the quantitative sciences; our work highlights the beneficial role quantum technologies can play in these endeavors.
