QKSA

Quantum Knowledge Seeking Agent

Conference paper (2023)

Authors

A. Sarkar QBee.eu, Quantum & Computer Engineering
Z. Al-Ars Quantum & Computer Engineering , Computer Engineering - EEMCS
K.L.M. Bertels QBee.eu, QCD/Almudever Lab - QuTech Advanced Research Centre
Research Group
Computer Engineering (EEMCS) (TU Delft)
Copyright: © 2023 A. Sarkar, Z. Al-Ars, K.L.M. Bertels
DOI: https://doi.org/10.1007/978-3-031-19907-3_37
Quantum computing Reinforcement learning Algorithmic information theory Mutating quine
More Info
expand_more

Published Date

2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Computer Engineering

Abstract

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 information-based reconstruction of quantum mechanics. A proof-of-concept is implemented and available as open-sourced software.