Abstract
Can quantum mechanics help us in building intelligent robots and agents? One of the defining characteristics of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in any real-life situation is the size and complexity of the corresponding task environment. Owing to, e.g., a large space of possible strategies, learning is typically slow. Even for a moderate task 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 significant speed-up for active learning as a genuine problem of artificial intelligence. We introduce a large class of quantum learning agents for which we show a quadratic boost in their active learning efficiency over their classical analogues. This result will be particularly relevant for applications involving complex task environments.
Highlights
The levels of modern-day technology have, in many aspects, surpassed the predictions made in the mid-20th century, as is witnessed, for example, by the sheer computing power of the average “smart” mobile phone
We have presented a class of quantum learning agents that use quantum memory for their internal processing of previous experience. These agents are situated in a classical task environment that rewards a certain behavior but is otherwise unknown to the agent, which corresponds to the situation of conventional learning agents
The agent’s internal “program” is realized by physical processes that correspond to quantum walks
Summary
The levels of modern-day technology have, in many aspects, surpassed the predictions made in the mid-20th century, as is witnessed, for example, by the sheer computing power of the average “smart” mobile phone. Emphasis was placed to specific algorithmic AI tasks— modules, such as data clustering, pattern matching, binary classification, and similar—and reduced from the holistic task of designing an autonomous and intelligent agent. To our knowledge, it has not been demonstrated so far that quantum physics can help in the complemental task of designing autonomous and learning agents. We show that in such an embodied framework of AI, provable advancements of a broad class of learning agents can be achieved when we take the full laws of quantum mechanics into account
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