Title
QKSA: Quantum Knowledge Seeking Agent
Author
Sarkar, A. (TU Delft Quantum & Computer Engineering; QBee.eu)
Al-Ars, Z. (TU Delft Quantum & Computer Engineering; TU Delft Computer Engineering) 
Bertels, K.L.M. (TU Delft QCD/Almudever Lab; QBee.eu)
Contributor
Goertzel, Ben (editor)
Iklé, Matt (editor)
Potapov, Alexey (editor)
Ponomaryov, Denis (editor)
Department
Quantum & Computer Engineering
Date
2023
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.
Subject
Algorithmic information theory
Mutating quine
Quantum computing
Reinforcement learning
To reference this document use:
http://resolver.tudelft.nl/uuid:2b8140f4-236f-4cf4-8fe2-63b5fbdd2e5f
DOI
https://doi.org/10.1007/978-3-031-19907-3_37
Publisher
Springer
Embargo date
2023-07-17
ISBN
9783031199066
Source
Artificial General Intelligence - 15th International Conference, AGI 2022, Proceedings
Event
15th International Conference on Artificial General Intelligence, AGI 2022, 2022-08-19 → 2022-08-22, Seattle, United States
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 13539 LNAI
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2023 A. Sarkar, Z. Al-Ars, K.L.M. Bertels