University of Kwazulu-Natal

Whitepaper Toward Human Like Intelligence Via Quantum Inspired

Bonisiwe Shabane
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whitepaper toward human like intelligence via quantum inspired

Artificial intelligence (AI) has surged forward, yet it often lacks the depth of human cognition as it struggles with uncertainty, memory, and reasoning. A study by Milan Maksimovic and Ivan S. Maksymov from Charles Sturt University, published in Technologies, introduces a cutting-edge method to blend quantum-cognitive principles into neural networks, the computational systems that emulate brain activity. This approach enables anyone with basic machine learning (ML) knowledge to create quantum-inspired models on a standard laptop, opening new possibilities for human-like AI across various domains. This innovative approach exhibits the potential to enhance AI capabilities in areas requiring detailed understanding of uncertainty, such as financial modeling and risk assessment. The research confronts a core limitation in AI.

That is while systems adeptly handle vast datasets, they fall short in imitating human thought processes, such as managing ambiguity or holding contradictory ideas. By leveraging quantum cognition theory (QCT)—a blend of quantum mechanics and psychology—the study equips neural networks with traits resembling human decision-making, aiming for applications in healthcare, finance, defense, and beyond. This work broadens access to quantum technology, typically reserved for specialized research labs. The study contrasts classical mechanics, which governs the motion of tangible objects, with quantum mechanics, a framework for atomic and subatomic behaviors. Quantum mechanics supports technologies like medical imaging and optical networks, introducing concepts that defy intuition. Superposition allows a system to exist in multiple states simultaneously until observed.

This ability to exist in multiple states simultaneously could potentially allow AI models to explore a wider range of possibilities in decision-making, similar to how humans consider various options, addressing AI’s limitations in reasoning. Entanglement links particles so their states depend on each other, regardless of distance, offering potential for enhanced memory through interconnected data processing. The double-slit experiment reveals electrons exhibiting wave-like interference patterns, their positions probabilistic until measured, illustrating the inherent uncertainty at the quantum level. This probabilistic nature resonates with the challenges AI faces in handling uncertainty and making predictions in complex environments. Similarly, Heisenberg’s uncertainty principle, which fundamentally constrains the precision with which certain pairs of properties like position and momentum can be known simultaneously, directly highlights the difficulties AI encounters when dealing with incomplete or... Central to this novel approach is quantum tunnelling (QT), a phenomenon where particles can traverse energy barriers that would be insurmountable according to classical physics.

This is attributed to their wave-like nature, a concept mathematically described by Schridinger’s equation, which essentially governs how these quantum waves evolve over time. This effect, linked to Heisenberg’s uncertainty principle, allows for a non-zero probability of a particle existing in classically forbidden regions. The likelihood of tunnelling is governed by the barrier’s width and height; a narrower barrier significantly increases this probability. Consider an electron encountering a barrier: it possesses a small but definite chance of tunnelling through, and this chance grows considerably as the barrier narrows. Notably, these barrier parameters also serve as tunable settings within the Quantum-Cognitive Theory (QCT) models of human mental states. QT finds practical application in technologies such as tunnel diodes and scanning tunnelling microscopy, and it influences biological processes like enzyme reactions and photosynthesis.

Within artificial intelligence, QT connects to early quantum-theoretic concepts, such as the Menneer-Narayanan model, which involved training neural networks in superposition. It is theorized that QT may relate to human cognition by enabling models to bypass rigid cognitive biases, thereby exploring less obvious solutions in a manner analogous to how the brain navigates complex decision... The pursuit of Artificial General Intelligence (AGI) requires a strategic evolution beyond the brute-force scaling of current models, which, despite their power, exhibit fundamental limitations in achieving integrated, robust, and adaptable forms of intelligence. This white paper introduces Quantarithmia, a conceptual framework engineered to address this challenge. The central thesis of our work is that the synthesis of principles from quantum mechanics, neurobiology, and information theory creates an architecture capable of emergent cognitive behaviors unattainable through existing paradigms. Quantarithmia is a quantum-inspired, not quantum-native, AGI framework.

This distinction is critical: it leverages the conceptual and mathematical elegance of quantum principles as metaphors to manage complex information states within a classical computing environment, rather than requiring quantum hardware. The primary goal of this approach is to create a more holistic, robust, and adaptable form of artificial intelligence, capable of cognitive functions analogous to those found in biological systems. The objective of this document is to provide a comprehensive technical overview of the Quantarithmia framework for an audience of AI researchers, developers, and strategists. We will examine its theoretical underpinnings, core architectural components, and the governing principles that guide its operation. This exploration begins with the foundational principles that inform the framework’s unique design. --------------------------------------------------------------------------------

The strategic innovation of the Quantarithmia framework lies not in a single breakthrough but in the deliberate synthesis of its theoretical foundations. Its novelty emerges from the integration of three distinct scientific domains—quantum mechanics, neurobiology, and information theory—to forge a new, comprehensive model for artificial intelligence. These principles are not merely academic; they are the bedrock upon which the entire architecture is built, each contributing a critical element to the framework’s capacity for emergent intelligence. Despite the impressive performance of deep learning models, particularly Convolutional Neural Networks (CNNs), their lack of interpretability and explainability renders them "black boxes." This opacity is problematic, especially in critical domains such as healthcare,... This paper introduces the QIXAI Framework (Quantum-Inspired Explainable AI), a novel approach for enhancing neural network interpretability using layer-wise quantum-inspired methods. By leveraging principles from quantum mechanics—such as Hilbert spaces, superposition, entanglement, and eigenvalue decomposition—the QIXAI framework uncovers how different layers in neural networks process and combine features to make decisions.

We critically examine popular model-agnostic interpretability methods like SHAP and LIME and layer-specific techniques like Layer-wise Relevance Propagation (LRP), identifying their limitations in providing a comprehensive understanding of neural networks’ inner workings. The QIXAI framework addresses these limitations, offering deeper insights into feature importance, dependencies between layers, and information propagation across layers. Using a CNN for malaria parasite detection as a case study, we demonstrate how quantum-inspired methods such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA), and Mutual Information (MI) yield actionable, interpretable explanations... Additionally, we explore how the QIXAI framework may be extended to other architectures, such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Transformers, and Natural Language Processing (NLP) models, as well as... The framework’s applicability spans both quantum and classical implementations, demonstrating its potential to improve interpretability and transparency across a wide range of models. These insights contribute to the broader goal of building transparent, trustworthy, and interpretable AI systems.

Deep learning models, particularly Convolutional Neural Networks (CNNs), have achieved state-of-the-art results in numerous fields, such as image recognition (Krizhevsky et al., 2012), medical diagnostics (Esteva et al., 2017), and natural language processing (Vaswani... Despite these successes, the inner workings of these models remain largely opaque, leading to concerns about their interpretability and explainability (Lipton, 2018). In high-stakes domains like healthcare, finance, and autonomous systems, this lack of transparency can hinder trust and adoption (Rudin, 2019; Doshi-Velez and Kim, 2017). Consequently, interpretability has emerged as a critical need for making AI systems more accountable and transparent (Samek et al., 2019). Numerous methods have been developed to address this challenge, including model-agnostic techniques like SHAP (Lundberg and Lee, 2017) and LIME (Ribeiro et al., 2016), and layer-specific approaches like Layer-wise Relevance Propagation (LRP) (Montavon et... However, these methods often fall short of providing a comprehensive, global understanding of how different layers in a neural network contribute to predictions.

Model-agnostic methods are limited in scope, offering only local interpretability for individual predictions, while LRP suffers from instability in deeper layers and lacks a rigorous mathematical framework for explaining the propagation of information across... In this paper, we introduce the QIXAI Framework (Quantum-Inspired Explainable AI), a novel approach for enhancing neural network interpretability through quantum-inspired mathematical methods. Drawing on concepts from quantum mechanics—such as Hilbert spaces, superposition, and entanglement—we propose a layer-wise analysis that provides more comprehensive and mathematically grounded insights into neural network behavior. These quantum-inspired methods, including Singular Value Decomposition (SVD), Principal Component Analysis (PCA), and Mutual Information (MI), offer a deeper understanding of feature importance, layer correlations, and how information flows through the network.

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