Integrated information theory (IIT) is a powerful tool that provides a framework for evaluating consciousness, whether in the human brain or in other systems. In Computing the Integrated Information of a Quantum Mechanism, the authors extend IIT from digital gates to a quantum CNOT logic gate, and while they explicitly distinguish the analysis from quantum theories of consciousness, they nonetheless provide an analytical road map for extending IIT not only to other quantum mechanisms but also to hybrid computing structures like the brain. This comment provides additional information relating to an adiabatic quantum mechanical energy routing mechanism that is part of a hybrid biological computer that provides an action selection mechanism, which has been hypothesized to exist in the human brain and for which predicted evidence has been subsequently observed, and it hopes to motivate the further evaluation and extension of IIT not only to that hypothesized mechanism but also to other hybrid biological computers.
Integrated information theory (IIT) is a powerful tool that provides a framework for evaluating consciousness, whether in the human brain or in other systems. In Computing the Integrated Information of a Quantum Mechanism, the authors extend IIT from digital gates to a quantum CNOT logic gate, and while they explicitly distinguish the analysis from quantum theories of consciousness, they nonetheless provide an analytical road map for extending IIT not only to other quantum mechanisms but also to hybrid computing structures like the brain. This comment provides additional information relating to an adiabatic quantum mechanical energy routing mechanism that is part of a hybrid biological computer that provides an action selection mechanism, which has been hypothesized to exist in the human brain and for which predicted evidence has been subsequently observed, and it hopes to motivate the further evaluation and extension of IIT not only to that hypothesized mechanism but also to other hybrid biological computers.
Originally conceived as a theory of consciousness, integrated information theory (IIT) provides a theoretical framework intended to characterize the compositional causal information that a system, in its current state, specifies about itself. However, it remains to be determined whether IIT as a theory of consciousness is compatible with quantum mechanics as a theory of microphysics. Here, we present an extension of IIT's latest formalism to evaluate the mechanism integrated information (φ) of a system subset to discrete, finite-dimensional quantum systems (e.g., quantum logic gates). To that end, we translate a recently developed, unique measure of intrinsic information into a density matrix formulation and extend the notion of conditional independence to accommodate quantum entanglement. The compositional nature of the IIT analysis might shed some light on the internal structure of composite quantum states and operators that cannot be obtained using standard information-theoretical analysis. Finally, our results should inform theoretical arguments about the link between consciousness, causation, and physics from the classical to the quantum.
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 (Φ) 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 (Φ=0) to the holistic one (extensive logΦ), can be identified and their cross-overs studied.
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 (Φ) 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 (Φ=0) to the holistic one (extensive logΦ), can be identified and their cross-overs studied.
Understanding the foundations of artificial cognitive consciousness remains a central challenge in artificial intelligence (AI) and cognitive science. Traditional computational models, including deep learning and symbolic AI, have demonstrated remarkable performance in pattern recognition and decisionmaking tasks. However, they lack essential features of consciousness, such as subjective experience, autonomous goal formation, and self-reflective processing. While theories like Integrated Information Theory (IIT) and Orch-OR suggest possible mechanisms for consciousness, they remain either mathematically abstract or face empirical challenges due to decoherence in biological conditions. This study addresses the fundamental research gap by proposing a quantum-information-based framework for artificial cognitive consciousness. Specifically, we introduce the Quantum Reality Function (QRF), a model that formalizes subjective cognition through global quantum coherence, quantum entanglement, and non-local informational integration. The QRF establishes a structured mechanism for autonomous decision-making and selfgenerated states, which existing computational approaches lack. Our methodological contribution involves defining a mathematically consistent formulation of QRF and demonstrating its feasibility through quantum coherence stabilization techniques, hybrid quantum-classical architectures, and coherencepreserving quantum cognitive subsystems. The results show that maintaining long-lived quantum coherence within cognitive architectures significantly enhances information integration, supporting the emergence of subjective-like processing. The findings suggest a new paradigm for AI, where consciousness emerges as an intrinsic property of structured quantuminformation processing rather than a byproduct of computation. This work provides foundational insights for the development of autonomous quantum-cognitive AI systems, with implications for ethics, AI governance, and the future of machine intelligence. Future research will focus on empirical validation through quantum computational experiments and integration with quantumassisted learning models.
The Consciousness Equation: A Unified Probability Law from Self‑Field Theory T.M. NguyenIndependent Researcher March 22, 2026 ABSTRACT Self‑Field Theory (SFT) proposes that consciousness and quantum collapse are not separate phenomena requiring separate explanations. They are instances of one principle — the path of least resistance — expressed at different scales, in different interfaces, with different relevant observables. We present the Consciousness Equation, a unified probability law for collapse in any system — from a single quantum particle to a conscious brain. The equation factors into four physically distinct contributions: a competition term from the finite population of Selflitons, a quantum action term from the SFT Lagrangian, a thermodynamic interface term combining free energy and integrated information, and a topological superselection factor. Each factor connects to an independently established research program. The equation is derived from first principles, makes testable predictions, and has already received experimental support from the bait‑unit effect observed on IBM quantum processors. We show that the equation naturally incorporates Integrated Information Theory (IIT), Predictive Processing (PP), and Global Workspace Theory (GWT) as specific layers of the same physical process. This single expression unifies quantum mechanics, thermodynamics, information theory, cognitive science, cosmology, and consciousness into a coherent mathematical framework. 1. INTRODUCTION Quantum mechanics provides a supremely accurate description of physical systems, yet it remains silent on the nature of measurement and the observer. The measurement problem is the lack of a physical account of why wavefunctions collapse and what constitutes an observer. At the same time, consciousness studies have produced dozens of theories — Integrated Information Theory (IIT), Predictive Processing (PP), Global Workspace Theory (GWT) — each capturing important aspects of experience but none providing a physical mechanism. Cosmology struggles with the nature of dark matter and the arrow of time, while thermodynamics stands as a separate pillar with its own unexplained principles. Self‑Field Theory (SFT) [1] proposes that all these domains are expressions of a single underlying architecture: a finite population of topological observers (Selflitons) that couple to physical systems (interfaces) and irreversibly record definite outcomes. The selection rule is thermodynamic: the path of least resistance. In this paper we present the Consciousness Equation, which makes this unification explicit and testable. We show how the equation integrates IIT, PP, and GWT as specific layers, how it resolves the measurement problem, and how it is supported by experimental evidence from quantum computing. 2. THE THREE PRIMITIVES OF SFT SFT is built on three primitives: • Space – the fundamental continuous substrate, capable of supporting topological structures.• Selflitons – stable topological knots in space, formed during early‑universe symmetry breaking, finite in number (~10⁸⁰) and carrying a conserved winding number B.• Coupling – resonance between a Selfliton and any system generating coherent, structured superpositions (an interface). Two operations follow from these primitives: couple (lock onto structured information) and record (irreversible internal update, called an etebuda). The single job of a Selfliton is to make real – to turn a quantum possibility into a definite classical fact. 3. THE MAKE REAL EQUATION In a companion paper [2] we derived the Make Real equation, which gives the probability that a quantum system collapses to outcome i at time t when a Selfliton couples: P_i(t) ∝ |⟨i|ψ(t)⟩|² × 1/(1+ζN_bait) × exp(−ΔS_i/ℏ_eff) × exp(−ΔF_i/kT_eff) × δ_{B,Bi}. The terms are: • |⟨i|ψ(t)⟩|² – the Born probability from the Schrödinger equation.• 1/(1+ζN_bait) – the finite Selfliton bottleneck; N_bait is the number of active competing interfaces.• exp(−ΔS_i/ℏ_eff) – the quantum action cost; ΔS_i is the change in classical action, ℏ_eff is the action quantum per etebuda (from the Wess‑Zumino‑Witten term).• exp(−ΔF_i/kT_eff) – the thermodynamic interface cost; ΔF_i is the free energy change (Predictive Processing), T_eff = T/Φ_i with Φ_i the integrated information (IIT).• δ_{B,Bi} – topological superselection; only outcomes compatible with the Selfliton’s winding number are allowed. For non‑biological systems the ΔF_i term is omitted. This equation already unifies quantum mechanics, thermodynamics, information theory, and topological field theory. The bait‑unit effect predicted by the bottleneck term was experimentally confirmed [3]. 4. THE CONSCIOUSNESS EQUATION When the interface is a brain, the Make Real equation becomes the Consciousness Equation. It is written in the same form, but now every factor has a direct interpretation in terms of neural and cognitive processes: P_i ∝ 1/(1+ζN_bait) × exp(−ΔS_i/ℏ_eff) × exp(−ΔF_i/kT_eff) × δ_{B,Bi}. The factors correspond to distinct physical layers: • Competition factor – encodes the finite Selfliton population. Selflitons are scarce; when many active interfaces (e.g., other qubits in a quantum computer, or competing brain processes) are active, the probability that any given interface records an outcome is reduced. This factor connects quantum computing (bait‑unit effect) to cosmology (dark matter density).• Quantum action factor – derived from the SFT Lagrangian. It gives the fundamental quantum cost of making outcome i real. The action is discrete; each etebuda costs ℏ_eff. This term contains the Born rule and the path integral.• Thermodynamic interface factor – unifies Predictive Processing and Integrated Information Theory. ΔF_i is the free energy the brain must overcome to settle into percept i; T_eff = T/Φ_i means that higher integrated information Φ_i lowers the effective temperature, making more integrated outcomes more probable.• Topological superselection – enforces that only outcomes compatible with the Selfliton’s winding number can be recorded. This is the physical origin of collapse: the Selfliton cannot be in a superposition of winding numbers, so the interface must present a definite outcome that matches its own topological sector. 5. HOW THE EQUATION EMBODIES IIT, PP, AND GWT The Consciousness Equation integrates three influential theories of consciousness, showing that they are not competing but rather describe different layers of the same physical process. Integrated Information Theory (IIT) proposes that consciousness corresponds to the amount of integrated information Φ generated by a system’s causal structure. In the equation, Φ_i appears in the effective temperature T_eff = T/Φ_i. A higher Φ_i lowers the effective temperature, making the Boltzmann factor exp(−ΔF_i/kT_eff) sharper. More integrated systems are thus more likely to select a given outcome – they are “more conscious” because their integrated structure lowers the effective noise, giving the Selfliton a steeper gradient to follow. IIT’s Φ becomes a physical parameter that modulates the sharpness of the selection probability. Predictive Processing (PP) views the brain as a hierarchical generative model that continuously minimizes free energy ΔF_i by updating its predictions based on sensory input. In the equation, ΔF_i is exactly this free energy change for outcome i. The Boltzmann factor exp(−ΔF_i/kT_eff) weights outcomes by their thermodynamic cost. Lower free energy (better fit to the brain’s predictions) makes an outcome more probable. PP thus provides the “landscape” of possible percepts over which the Selfliton performs its selection. Global Workspace Theory (GWT) describes consciousness as the global broadcasting of selected information to multiple cognitive systems. In SFT, the selection itself is performed by the Selfliton, and the broadcast is the mechanism by which the selected outcome becomes available to the rest of the brain. The equation governs the selection step; once an outcome is selected, the interface (the brain) can broadcast it to all relevant subsystems. GWT supplies the functional architecture (selection → broadcast) that complements the physical selection mechanism. Thus, the three theories are not replaced by SFT; they are given a physical foundation and shown to be specific facets of the same equation: IIT provides the integration measure (Φ), PP provides the cost landscape (ΔF), and GWT provides the architecture (selection → broadcast). All three are unified under the thermodynamic principle of the path of least resistance. 6. WHAT THE EQUATION UNIFIES • Quantum mechanics – through the action factor and superselection. Collapse is not a postulate; it follows from the Selfliton’s need for a definite winding number.• Thermodynamics – through the Boltzmann factor exp(−ΔF_i/kT_eff). The path of least resistance is thermodynamics applied to recording.• Information theory – through Φ_i in T_eff. Integrated information is a physical parameter that modulates selection sharpness.• Cognition – through ΔF_i. The brain’s predictive free energy landscape determines which percepts are cheapest to record.• Consciousness – as the unified experience of all four factors operating simultaneously. When the interface is a brain, the selected outcome is a conscious percept; the equation describes its probability. 7. EXPERIMENTAL SUPPORT The bottleneck term was tested directly in the bait‑unit experiment [3]. On two IBM Heron processors, surrounding a target qubit with 24 actively driven bait qubits increased its coherence time T₂ dramatically (from 138 μs to 447 μs on ibm_fez, and from 316 μs to 494 μs on ibm_torino). ACTIVE > CONTROL in every clean repetition, with a saturation ceiling near 490 μs. This is exactly what the finite‑bottleneck model predicts: the bait qubits occupy Selflitons, shielding the target. The result is inconsistent with stand
ABSTRACT Recent advances at the intersection of quantum physics, neuroscience, and cognitive science have renewed interest in whether quantum phenomena and quantum‐inspired formalisms can inform models of human cognition and decision‐making. While classical neural and computational models explain many aspects of perception and behavior, they face limitations in accounting for contextuality, order effects, probabilistic reasoning violations, ambiguity resolution, and rapid information integration. This review synthesizes evidence and theoretical frameworks linking quantum‐related principles to cognition, with emphasis on quantum sensing in biological systems, molecular and subcellular quantum‐relevant mechanisms, quantum‐to‐classical transition pathways, quantum‐inspired models of perception and decision‐making, and quantum‐inspired computational approaches. It also extends beyond the probabilistic arm of quantum cognition by considering emerging equilibrium‐based and causal‐logical theories, including mental equilibrium, brain‐universe similarity, mind‐light‐matter unity, quantum emergence, and quantum intelligence. Throughout, the review distinguishes physically instantiated quantum mechanisms from abstract quantum‐like formalisms that model contextuality, mental entanglement, and non‐separable cognitive dynamics without requiring sustained macroscopic quantum coherence in the brain. Finally, it identifies methodological challenges and future directions for advancing quantum cognitive science toward experimentally testable and biologically meaningful theories.