QKSA: Quantum Knowledge Seeking Agent

In this research, we extend the universal reinforcement learning agent models of artificial general intelligence to quantum environments. The utility function of a classical exploratory stochastic Knowledge Seeking Agent, KL-KSA, is generalized to distance measures from quantum information theory on density matrices. Quantum process tomography (QPT) algorithms form a tractable subset of programs for modeling environmental dynamics. The optimal QPT policy is selected based on a mutable cost function based on algorithmic complexity as well as computational resource complexity. The entire agent design is encapsulated in a self-replicating quine which mutates the cost function based on the predictive value of the optimal policy choosing scheme. Thus, multiple agents with pareto-optimal QPT policies evolve using genetic programming, mimicking the development of physical theories each with different resource trade-offs. This formal framework, termed Quantum Knowledge Seeking Agent (QKSA), is a resource-bounded participatory observer modification to the recently proposed algorithmic informationbased reconstruction of quantum mechanics. A proof-of-concept is implemented and available as open-sourced software.

Automated summary

In this research, the universal reinforcement learning agent models are extended to quantum environments. The utility function of a classical exploratory stochastic Knowledge Seeking Agent is generalized to distance measures from quantum information theory. Quantum process tomography algorithms are used to model environmental dynamics. The optimal policy is selected based on a mutable cost function, and multiple agents with pareto-optimal policies evolve using genetic programming.