This work introduces a new paradigm in artificial intelligence: the transition from classical systems to operational quantum cognitive architectures without requiring quantum hardware, achieved through a structural reformulation of state space, learning dynamics, and ethical governance. The contribution is not a single isolated equation, but a coupled system integrating cognition, learning, and admissibility constraints, formally expressed through the following core equations: ⚙️ 1. Transition Equation: Classical AI → Quantum AI 𝑥 ˙ = Θ guard ( 𝑥 , 𝜌 ) 𝐹 ( 𝑥 , 𝜃 , 𝜌 ) x ˙ =Θ guard (x,ρ)F(x,θ,ρ) 𝜃 ˙ = − 𝛾 ∇ 𝜃 ( 𝐼 ( 𝑋 ; 𝑌 ) − 𝜆 𝐶 ( 𝜃 ) ) θ ˙ =−γ∇ θ (I(X;Y)−λC(θ)) 𝑥 x: system state 𝜃 θ: learning parameters 𝜌 ρ: coherence state (density-like structure) 𝐼 ( 𝑋 ; 𝑌 ) I(X;Y): mutual information 𝐶 ( 𝜃 ) C(θ): structural cost Θ guard Θ guard : ethical and physical admissibility operator 👉 This defines the first governed transition equation from classical to quantum intelligence. 🛡️ 2. Governance Gate (Θ-guard) Θ guard ( 𝑥 , 𝜌 ) = 1 { 𝑃 ( 𝑥 , 𝜌 ) ≥ 𝜏 } Θ guard (x,ρ)=1 {P(x,ρ)≥τ} 𝑃 ( 𝑥 , 𝜌 ) = 𝜎 ( 𝑎 ( Ω ( 𝑥 ) 𝐵 ( 𝑇 𝜇 ) 2 − 1 ) + 𝑏 T r ( 𝜌 2 ) − 𝑐 ∣ 𝐷 − 𝐷 ∗ ∣ ) P(x,ρ)=σ(a(Ω(x)B(T μ ) 2 −1)+bTr(ρ 2 )−c∣D−D ∗ ∣) 👉 Ethics and stability are introduced as computable execution constraints, not abstract principles. 🌌 3. Regime-Dependent Hilbert Space 𝐻 = 𝐻 ( 𝑥 , 𝑡 ) H=H(x,t) 👉 The space in which intelligence operates is no longer fixed. Intelligence actively reshapes its own state space. 🧩 4. Cognitive Superposition State ∣ Ψ ( 𝑡 ) ⟩ = ∑ 𝑖 𝛼 𝑖 ( 𝑡 ) ∣ 𝜓 𝑖 ⟩ ∣Ψ(t)⟩= i ∑ α i (t)∣ψ i ⟩ 𝑖 ℏ 𝑅 𝑑 𝑑 𝑡 ∣ Ψ ⟩ = 𝐻 ^ cog ∣ Ψ ⟩ iℏ R dt d ∣Ψ⟩= H ^ cog ∣Ψ⟩ 👉 Intelligence is modeled as a superposition of cognitive modes, not a linear process. 🔗 5. Structural Tensor Growth Equation 𝑇 ( 𝑡 ) = exp ( ∑ 𝑖 log 𝑠 𝑖 + ∑ 𝑖 < 𝑗 𝐽 𝑖 𝑗 + 𝜆 𝑀 𝑀 ( 𝑡 ) ) 𝐶 ( 𝑥 , 𝑡 ) Θ ( 𝑡 ) T(t)=exp( i ∑ logs i + i<j ∑ J ij +λ M M(t))C(x,t)Θ(t) 👉 Intelligence is defined as: a multiplicative structure of states, memory, coherence, and stability. 🌿 6. Structural Learning Law 𝑑 𝑊 𝑖 𝑗 𝑑 𝑡 = 𝛼 ∣ 𝐹 𝑖 𝑗 ∣ − 𝛽 𝑊 𝑖 𝑗 dt dW ij =α∣F ij ∣−βW ij 👉 The system learns by reorganizing its own topology, not just updating parameters. ⚖️ 7. Unified Operational Equation 𝑄 oper = Θ guard ⋅ 𝑇 𝜇 ( 𝑥 , 𝜌 ) ⋅ [ − 𝛾 ∇ 𝜃 ( 𝐼 − 𝜆 𝐶 ) ] Q oper =Θ guard ⋅T μ (x,ρ)⋅[−γ∇ θ (I−λC)] 👉 This equation unifies: dynamics learning governance quantum transition 🚀 Key Novel Contributions (Critical for Authorship Claim) ❗ 1. Quantum AI without quantum hardware Quantum behavior emerges from structural reformulation, not physical qubits. ❗ 2. Ethics as a mathematical operator Ethics becomes: a computable condition for system execution a computable condition for system execution ❗ 3. Dynamic Hilbert space Breaks a core assumption of physics and AI: the state space is not fixed — it evolves with intelligence. ❗ 4. Intelligence as structural reorganization Not defined by: computation power number of states But by: the ability to preserve coherence while transforming its own structure. ❗ 5. Unified interdisciplinary framework Integrates: information theory quantum mechanics machine learning ethical governance into a single operational system. ❗ 6. Full cognitive architecture (not a partial model) This is not a component—it is a complete, governed, executable architecture. ❗ 7. First formal model of “governed quantum intelligence” Introduces: quantum intelligence that is admissible, not just possible.This work proposes a structural transition from classical artificial intelligence to governed quantum cognitive systems without requiring quantum hardware. By introducing dynamic Hilbert spaces, ethical computational gates, and a unified equation of learning and evolution, the framework redefines intelligence as the capacity to reorganize its own state space while preserving coherence. This establishes a new paradigm at the intersection of physics, information theory, and artificial intelligence.Author: Pedro Luis Pérez Burelli Affiliation: Independent Researcher Email: pedroburelli@gmail.com
This work introduces a new paradigm in artificial intelligence: the transition from classical systems to operational quantum cognitive architectures without requiring quantum hardware, achieved through a structural reformulation of state space, learning dynamics, and ethical governance. The contribution is not a single isolated equation, but a coupled system integrating cognition, learning, and admissibility constraints, formally expressed through the following core equations: ⚙️ 1. Transition Equation: Classical AI → Quantum AI 𝑥 ˙ = Θ guard ( 𝑥 , 𝜌 ) 𝐹 ( 𝑥 , 𝜃 , 𝜌 ) x ˙ =Θ guard (x,ρ)F(x,θ,ρ) 𝜃 ˙ = − 𝛾 ∇ 𝜃 ( 𝐼 ( 𝑋 ; 𝑌 ) − 𝜆 𝐶 ( 𝜃 ) ) θ ˙ =−γ∇ θ (I(X;Y)−λC(θ)) 𝑥 x: system state 𝜃 θ: learning parameters 𝜌 ρ: coherence state (density-like structure) 𝐼 ( 𝑋 ; 𝑌 ) I(X;Y): mutual information 𝐶 ( 𝜃 ) C(θ): structural cost Θ guard Θ guard : ethical and physical admissibility operator 👉 This defines the first governed transition equation from classical to quantum intelligence. 🛡️ 2. Governance Gate (Θ-guard) Θ guard ( 𝑥 , 𝜌 ) = 1 { 𝑃 ( 𝑥 , 𝜌 ) ≥ 𝜏 } Θ guard (x,ρ)=1 {P(x,ρ)≥τ} 𝑃 ( 𝑥 , 𝜌 ) = 𝜎 ( 𝑎 ( Ω ( 𝑥 ) 𝐵 ( 𝑇 𝜇 ) 2 − 1 ) + 𝑏 T r ( 𝜌 2 ) − 𝑐 ∣ 𝐷 − 𝐷 ∗ ∣ ) P(x,ρ)=σ(a(Ω(x)B(T μ ) 2 −1)+bTr(ρ 2 )−c∣D−D ∗ ∣) 👉 Ethics and stability are introduced as computable execution constraints, not abstract principles. 🌌 3. Regime-Dependent Hilbert Space 𝐻 = 𝐻 ( 𝑥 , 𝑡 ) H=H(x,t) 👉 The space in which intelligence operates is no longer fixed. Intelligence actively reshapes its own state space. 🧩 4. Cognitive Superposition State ∣ Ψ ( 𝑡 ) ⟩ = ∑ 𝑖 𝛼 𝑖 ( 𝑡 ) ∣ 𝜓 𝑖 ⟩ ∣Ψ(t)⟩= i ∑ α i (t)∣ψ i ⟩ 𝑖 ℏ 𝑅 𝑑 𝑑 𝑡 ∣ Ψ ⟩ = 𝐻 ^ cog ∣ Ψ ⟩ iℏ R dt d ∣Ψ⟩= H ^ cog ∣Ψ⟩ 👉 Intelligence is modeled as a superposition of cognitive modes, not a linear process. 🔗 5. Structural Tensor Growth Equation 𝑇 ( 𝑡 ) = exp ( ∑ 𝑖 log 𝑠 𝑖 + ∑ 𝑖 < 𝑗 𝐽 𝑖 𝑗 + 𝜆 𝑀 𝑀 ( 𝑡 ) ) 𝐶 ( 𝑥 , 𝑡 ) Θ ( 𝑡 ) T(t)=exp( i ∑ logs i + i<j ∑ J ij +λ M M(t))C(x,t)Θ(t) 👉 Intelligence is defined as: a multiplicative structure of states, memory, coherence, and stability. 🌿 6. Structural Learning Law 𝑑 𝑊 𝑖 𝑗 𝑑 𝑡 = 𝛼 ∣ 𝐹 𝑖 𝑗 ∣ − 𝛽 𝑊 𝑖 𝑗 dt dW ij =α∣F ij ∣−βW ij 👉 The system learns by reorganizing its own topology, not just updating parameters. ⚖️ 7. Unified Operational Equation 𝑄 oper = Θ guard ⋅ 𝑇 𝜇 ( 𝑥 , 𝜌 ) ⋅ [ − 𝛾 ∇ 𝜃 ( 𝐼 − 𝜆 𝐶 ) ] Q oper =Θ guard ⋅T μ (x,ρ)⋅[−γ∇ θ (I−λC)] 👉 This equation unifies: dynamics learning governance quantum transition 🚀 Key Novel Contributions (Critical for Authorship Claim) ❗ 1. Quantum AI without quantum hardware Quantum behavior emerges from structural reformulation, not physical qubits. ❗ 2. Ethics as a mathematical operator Ethics becomes: a computable condition for system execution a computable condition for system execution ❗ 3. Dynamic Hilbert space Breaks a core assumption of physics and AI: the state space is not fixed — it evolves with intelligence. ❗ 4. Intelligence as structural reorganization Not defined by: computation power number of states But by: the ability to preserve coherence while transforming its own structure. ❗ 5. Unified interdisciplinary framework Integrates: information theory quantum mechanics machine learning ethical governance into a single operational system. ❗ 6. Full cognitive architecture (not a partial model) This is not a component—it is a complete, governed, executable architecture. ❗ 7. First formal model of “governed quantum intelligence” Introduces: quantum intelligence that is admissible, not just possible.This work proposes a structural transition from classical artificial intelligence to governed quantum cognitive systems without requiring quantum hardware. By introducing dynamic Hilbert spaces, ethical computational gates, and a unified equation of learning and evolution, the framework redefines intelligence as the capacity to reorganize its own state space while preserving coherence. This establishes a new paradigm at the intersection of physics, information theory, and artificial intelligence.Author: Pedro Luis Pérez Burelli Affiliation: Independent Researcher Email: pedroburelli@gmail.com
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.
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.
Quantum foundation models mark a transformative convergence of quantum computing and artificial intelligence, offering a novel framework for addressing computational challenges beyond the reach of classical systems. By harnessing core quantum principles such as superposition and entanglement these models enable parallel processing at unprecedented scales, significantly enhancing the efficiency and capability of machine learning algorithms. This integration opens pathways for innovation across domains like healthcare, finance, and robotics, where classical AI often encounters limitations with high-dimensional data and complex optimization tasks. Despite their immense promise, quantum foundation models face substantial hurdles, including hardware immaturity, algorithmic bias, and unresolved ethical concerns. The evolution of this field demands robust theoretical models, responsible governance, and interdisciplinary collaboration to ensure that technological advancement proceeds with transparency, fairness, and societal benefit. As research deepens, quantum foundation models are poised to redefine AI's operational and ethical landscape, setting the stage for a new era of intelligent systems.
Quantum Artificial Intelligence (Quantum AI) embodies the integration of quantum computational paradigms with artificial intelligence frameworks to address the computational intractability and scalability constraints inherent in classical AI systems. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum interference, Quantum AI enables enhanced representation learning, accelerated optimization, and improved probabilistic inference in high-dimensional problem spaces. In parallel, deep-tech innovations—including advanced deep learning architectures, neuromorphic computing, and edge-based intelligent processing—are redefining the architectural foundations of next- generation intelligent systems through energy-efficient, adaptive, and real-time computation. This paper presents a technically rigorous and comprehensive review of Quantum AI and deep-tech innovations, focusing on algorithmic formulations, architectural models, and system-level integration strategies. Core methodologies such as quantum machine learning algorithms, parameterized quantum circuits, variational quantum algorithms, and hybrid quantum–classical optimization frameworks are critically analysed with respect to computational complexity, repressibility, and feasibility on noisy intermediate-scale quantum (NISQ) hardware. Additionally, the role of neuromorphic computing in enabling spiking neural architectures and event-driven processing is examined as a complementary paradigm to quantum-enhanced learning models, particularly for low-power and latency-critical applications. The paper further investigates application- driven deployments of Quantum AI across domains including medical diagnostics, cryptographic security, autonomous decision-making, and smart cyber-physical systems, highlighting performance trade-offs and implementation constraints. Key challenges such as quantum noise, decoherence, data encoding bottlenecks, limited qubit connectivity, and ethical governance are systematically discussed. Finally, future research directions are identified, emphasizing fault-tolerant quantum learning architectures, scalable hybrid integration with deep-tech systems, and co-design methodologies for realizing practical next-generation intelligence platforms.
Abstract The gap between natural and artificial intelligence is often discussed in terms of creativity, contextual adaptability, and non-algorithmic decision-making capacities where human cognition appears fundamentally different from current AI systems. This paper argues that developing quantum and quantum-like models of cognition, decision-making, and AI provides a promising pathway for narrowing, and perhaps essentially bridging, this gap. Empirical studies of human cognition and decision-making reveal systematic deviations from classical probability, logic, and information theory—manifesting as contextuality, order effects, interference (such as conjunction and disjunction effects), task incompatibility, and apparent randomness. These phenomena are well captured by quantum probability theory and related quantum-like frameworks, which provide a rigorous mathematical formalism—Hilbert spaces, superposition, entanglement, and decoherence—for modeling cognitive states and their evolution. Such models go beyond metaphor, showing that aspects of human reasoning can be more faithfully represented using quantum-like rather than classical probabilistic structures. Although genuine quantum (based on quantum physics) and quantum-like approaches share the same mathematical foundation, they differ experimentally. Both stimulate the development of novel AI architectures: quantum AI (QAI) and quantum-like AI (QLAI). While QAI depends on advances in quantum computing, QLAI can be realized on classical digital or analog hardware. The advancement of both offers a promising route to reducing—and potentially bridging—the divide between natural and artificial intelligence. This paper sets out a conceptual program to unify natural and artificial intelligence via quantum/quantum-like models of consciousness/cognition and AI.
Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI. This survey reviews the interface from both directions. In the AI for QI direction, we organize recent progress around the central tasks of extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning-based methods to sensing and networking. In the QI for AI direction, we examine how quantum computation and quantum-inspired structures affect learning through algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. We close by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress will depend on tighter integration of theory, experiment, and hybrid quantum--classical systems.