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arxiv: 1806.01421 · v2 · pith:5WREIJGXnew · submitted 2018-06-04 · 🪐 quant-ph

Towards Quantum Integrated Information Theory

classification 🪐 quant-ph
keywords integratedinformationconsciousnessholisticnetworkneuralquantumtheory
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Integrated Information Theory (IIT) has emerged as one of the leading research lines in computational neuroscience to provide a mechanistic and mathematically well-defined description of the neural correlates of consciousness. Integrated Information ($\Phi$) quantifies how much the integrated cause/effect structure of the global neural network fails to be accounted for by any partitioned version of it. The holistic IIT approach is in principle applicable to any information-processing dynamical network regardless of its interpretation in the context of consciousness. In this paper we take the first steps towards a formulation of a general and consistent version of IIT for interacting networks of quantum systems. A variety of different phases, from the dis-integrated ($\Phi=0$) to the holistic one (extensive $\log\Phi$), can be identified and their cross-overs studied.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Quantum Superpositions of Conscious States in a Minimal Integrated Information Model

    quant-ph 2023-09 unverdicted novelty 7.0

    A minimal IIT-based quantum circuit shows that Lindblad collapse dynamics require proliferation of operators to tie rates solely to differences in conscious states.

  2. Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle

    q-bio.NC 2026-05 unverdicted novelty 6.0

    Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.