arXiv:2509.07940

Quantum deliberating machines

Andrei Galiautdinov

Published Sep 9, 2025• 0 citations

Abstract

Within the familiar circuit-based quantum computational setting, we introduce and analyze a toy model of a quantum physical device capable of internal, self-referential deliberation. The key idea is to represent ``deliberation'' as a coherent iterative branching process, in which competing branch-dependent system evolutions are maintained in superposition, with additional control and memory registers recording branch histories, and the policy register adaptively biasing subsequent development. We provide explicit quantum circuit realizations and carry out detailed step-by-step derivations of the entangled control--memory--system--policy dynamics. We carefully distinguish between internally adaptive and internally reinforced deliberations, proposing the architectures for both, and briefly discuss categorical and controlled--Stinespring reformulations, as well as their conceptual implications. The primary construction models a memory-driven deliberation where the policy update depends on which actions were taken, not on their results. We also present a simple extension that allows for minimalistic, outcome-driven policy updates, implementing a coherent feedback loop that steers the system toward a target state regardless of initial branch-dependent evolution. This loop can be interpreted as a quantum autopilot or search-and-rescue mechanism, illustrating how a device can autonomously correct and optimize its internal strategy in superposition. Finally, we briefly consider various implementations of a dialogue that may take place between two deliberating machines. Taken together, this frames the proposed model as a plausible setting for exploring how such devices may maintain multiple alternatives in parallel, while performing an internal decision-making process through coherent branching, entanglement, adaptive policy updates, and policy-driven self-modifying unitary dynamics.

DOI: 10.48550/arxiv.2509.07940

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