Update to the Quantum Language and Consciousness Model (QLCM v1.2)
Independent Researcher, Quantum Communication and Consciousness Laboratory
Caracas, Venezuela
November 9, 2025
This paper presents the Intentional Re-Coherence Protocol (PRI) as an evolution of the Quantum Language and Consciousness Model (QLCM v1.2), designed to address the growing «coherence crisis» in AI systems applied to finance. Building on the quantum-intentional framework of QLCM – which conceptualizes language as a non-local quantum information field modulating perception through entangled semantic units («logons») – we define a new Ethical Fidelity metric Ef based on classical quantum fidelity.
We propose a hybrid technical architecture that uses entangled superpositions to represent ideal ethical states versus AI outputs, and implement an Ethical Phase Modulation (EPM) mechanism that restructures and redirects misaligned processes. Through accessible quantum and classical simulations, PRI demonstrates significant computational savings and improves the financial sustainability of algorithmic strategies.
We conclude that PRI constitutes a systemic mechanism for aligning economic and ethical goals in the AI era, providing a quantifiable solution to the coherence crisis in automated financial systems.
The massive applications of AI in finance have generated high levels of correlation in asset prices and speculative leverage, which could trigger systemic instability if a technological bubble were to burst. Experts warn that AI «cannot replace the human brain» and that fully automated systems can be dangerous without ethical supervision.
In this risk context, the notion of an operational coherence crisis emerges: the discrepancy between AI’s enormous computational capacity (which solves complex patterns) and the lack of semantic, ethical coherence or comprehensibility in its decisions.
In response, we propose a reorientation of the AI paradigm through the Intentional Re-Coherence Protocol (PRI), building on the foundations of QLCM, a model that reconceptualizes language as a quantum information field capable of dynamically modulating the perceptual and relational states of agents.
The Quantum Language and Consciousness Model (QLCM) postulates that each linguistic unit («logon») is described by vibrational parameters – semantic frequency (νs), affective amplitude (Aa) and intentional phase (φi) – that interact with each other and with the receiver’s consciousness.
Basal vibrational rate that determines conceptual resonance within the perceptual field
Emotional intensity that modulates the energetic potency of the linguistic act
Directional alignment of conscious will, guiding the collapse of meaning
This approach allows describing communication as a phenomenon of non-local vibrational coherence between conscious agents, quantifiable through semantic fidelity metrics (e.g., Hs) that verify entanglement and simultaneous collapse of meaning.
In the original QLCM introduction, it was experimentally demonstrated that pairs of entangled logons could achieve fidelities Hs > 0.85, distinguishable from random controls. This framework highlights the convergence of semantics, emotion and intention in a generative linguistic act.
Leveraging this quantum-intentional perspective, PRI adds layers of ethical supervision and computational optimization to avoid current financial risks.
To introduce quantifiable ethical criteria into the system, we define Ethical Fidelity Ef analogous to standard quantum fidelity. In quantum theory, the fidelity between two pure states |ψ⟩ and |φ⟩ is |⟨ψ|φ⟩|2, interpreted as the probability that both states are indistinguishable when measured.
where Ψideal is obtained as superpositions of logons representing the agreed ethical values, and is compared with the semantic distribution/results of the AI model.
This metric transforms the fulfillment of abstract criteria into a continuous signal of ethical fidelity, similar to the concept of Ethical Fidelity Signal in sustainability, which quantifies the adherence of automated decisions to social and environmental principles.
In the case of PRI, this architecture supports both the generation of QLCM’s entangled language and the simultaneous comparison with the defined «ethical space,» something unattainable for classical discrete systems.
PRI articulates a hybrid quantum-classical pipeline implemented through accessible simulations. At the core, input data (e.g., financial discourses, investment policies) are converted into quantum states through a logon encoder.
Each relevant term or concept is embedded as a complex quantum vector in ℋsem ⊗ ℋaff ⊗ ℋint
Inference on the quantum linguistic model and ethical evaluation comparing with Ψideal
Quantum fidelity module that calculates Ef internally through tensor approximations
The Ethical Phase Modulation (EPM) is the central mechanism that restructures and proactively redirects AI processes when ethical fidelity is insufficient.
It functions analogously to early stopping in network training, but with a reconstructive ethical emphasis: at each step of output generation, Ef is monitored. If the partial state of the output falls below a predetermined threshold, the flow is restructured through phase modulation.
In this way, the system avoids spending computational resources on calculations that would lead to ethical misalignment and redirects the process toward ethically viable results.
To test PRI, we conducted comparative simulations using quantum simulation software (e.g., Qiskit) with realistic noise, as well as classical backends.
The results confirm that PRI achieves significant reductions in computational cost compared to traditional QLCM.
The ability to process multiple correlations in parallel through qubits drastically reduces the need for sequential iterations. These savings come from avoiding computational branches in trajectories labeled as unethical.
From a financial sustainability perspective, PRI favors more robust strategies with lower systemic risk.
By filtering outputs with low ethical alignment (for example, excessively speculative or socially irresponsible investment recommendations), a reduction in simulated volatility metrics and portfolio failure rates is observed.
Our models suggest that implementing PRI with a precautionary ethical approach can decrease the average leverage of algorithmic portfolios by approximately 15% in adverse scenarios, improving stability.
Beyond numbers, PRI introduces a conceptual change: it places ethics as a first-class citizen in the AI lifecycle.
In summary, PRI addresses the «coherence trap» of classical AI by explicitly aligning meaning generation with global systemic objectives.
Incorporating quantum computing into data centers requires designing systems tolerant to environmental noise and hybrid architectures.
As Ilana Wisby (OQC) points out, new chips must be «simple, flexible and scalable for commercial environments.» The practical deployment of PRI would involve collaboration with quantum infrastructure providers.
The coexistence of classical and quantum AI demands differentiated regulatory frameworks.
The commercial deployment of PRI will attract investment in applied quantum research.
Investment portfolios are expected to gradually replace opaque AI systems with quantum-explainable solutions when they demonstrate superior performance and lower systemic risk. PRI, by promising additional stability and resource savings, can become a long-term strategic asset.
The Intentional Re-Coherence Protocol (PRI) positions itself as a critical extension of QLCM v1.2, capable of addressing the current coherence gap in AI-finance systems.
The results validate the hypothesis that quantum-relational architecture can provide substantial benefits over classical approaches, both in performance and ethical alignment.
Strategically, PRI points to the fact that systemic survival depends on intertwining economic optimization with ethical and sustainability objectives, as suggested by the vibrational vision of QLCM.
In the new AI era, mechanisms like PRI will be indispensable to ensure that technological progress «does not compromise global system viability» but rather reinforces it, acting as a frequency modulator that delivers complex information in the correct dosage and sequence for human coherence.
Minimum ontological unit of QLCM. Corresponds to a «quantum of semantic information» that combines a phoneme or word with frequency, affect and intention parameters.
State of alignment of logons in frequency and phase, implying shared semantics between agents. Measures synchrony in meaning interpretation.
Metric proposed in QLCM to quantify similarity between entangled states; in the pure approximation, Hs = |⟨Ψ1|Ψ2⟩|/(‖Ψ1‖‖Ψ2‖).
Analogous metric applied to a space of ethical values, defined as the fidelity between the state generated by AI and a predefined ideal ethical state.
Mechanism that restructures AI processes when Ef falls below a threshold, recomposing the sequence toward ethical coherence rather than abruptly interrupting.