arXiv AI

Adaptive Entangled Neurons in the Brain and Natural Intelligence Simulations for Artificial General Intelligence

The paper proposes that quantum entanglement underlies the synchronization of millions of neurons, introducing the Liu‑Chen‑Ao (LCA) hypothesis of entangled nerve fibers. Using a nonlocal many‑body wave equation, the authors model collective behaviors among adaptive agents and apply this framework to Chinese intraday stock market data, finding that adaptive entangled game modes explain 82‑94% of decision patterns and that only a small fraction of actions are truly independent. These empirical results support the LCA hypothesis and suggest that incorporating probability‑wave‑based entangled modules could improve artificial general intelligence by creating more efficient, human‑like processing units. whyItMatters":"The study provides empirical evidence for the LCA hypothesis and highlights the limitations of conventional artificial neural networks, pointing toward more efficient and robust AGI architectures."

arXiv AI

Sep 11

Adaptive Entangled Game Modules in Artificial General Intelligence

The paper presents a probability‑wave framework for modeling the collective behavior of adaptive agents, deriving testable eigenmodes via a generalized behavioral intelligence (GBI) nonlocal probability‑wave equation. Empirical analysis of Chinese intraday stock market data shows that adaptive entangled game modes explain 89% of observed decision patterns, far exceeding predictions from neoclassical finance and supporting the Liu‑Chen‑Ao hypothesis of nonlocal entangled nerve fibers. The authors argue that incorporating these adaptive entangled game modules into AGI architectures can overcome limitations of conventional ANN‑based AI and enable more compact, efficient, and robust human‑like processing units for embodied intelligence and robotics.

By Haochen Li, Xinshuai Guo, Jingdong Ouyang, Wei Zhang, Leilei Shi

agents robotics

arXiv AI

Jun 3

Identifying Quantum Structure in AI Language: Evidence for Evolutionary Convergence of Human and Artificial Cognition

arXiv:2511. 21731v2 Announce Type: replace-cross Abstract: We present the results of cognitive tests on conceptual combinations, performed using specific Large Language Models (LLMs) as test subjects.

By Diederik Aerts, Jonito Aerts Argu\"elles, Lester Beltran, Suzette Geriente, Roberto Leporini, Massimiliano Sassoli de Bianchi, Sandro Sozzo

llms agents

arXiv Machine Learning

Aug 4

Searching for Quantum Effects in the Brain: A Bell-Type Test for Nonclassical Latent Representations in Autoencoders

arXiv:2601. 10588v2 Announce Type: replace-cross Abstract: Whether neural information processing is entirely classical or involves quantum-mechanical elements remains an open question.

By I. K. Kominis, C. Xie, S. Li, M. Skotiniotis, G. P. Tsironis

diffusion

arXiv AI

Jul 3

Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders

arXiv:2607. 01336v1 Announce Type: cross Abstract: Neural Quantum States (NQS) are a remarkably expressive class of variational ans\"atze for quantum many-body wavefunctions, yet little is understood about their internal mechanisms: trained on variational objectives alone, how do NQS accurately capture physical observables that they have never been explicitly optimized for?

By Zihao Qi, Christopher Earls

safety

arXiv AI

Oct 1

A memory-based active inference model of DishBrain-like adaptive behaviour

arXiv:2508.06980v2 Announce Type: replace Abstract: Recent and rapid advances in artificial intelligence (AI) make it increasingly important to understand the foundations of adaptive behaviour in aut...

By Aswin Paul, Moein Khajehnejad, Forough Habibollahi, Brett J. Kagan, Adeel Razi

agents

arXiv AI

Jul 2

When AI meets quantum information: A comprehensive review

arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.

By Min Chen, Yu Gan, Xin Jin, Yuqing Li, Junqi Wang, Zeguan Wu, Yunfei Wang, Bingzhi Zhang, Priyam Srivastava, Tianlong Chen, Ankit Kulshrestha, Yuan Liu, Juan Jos\'e Mendoza-Arenas, Kaushik P. Seshadreesan, Sarvagya Upadhyay, Xueyue Zhang, Quntao Zhuang, Junyu Liu

arXiv Machine Learning

Jul 8

Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions

arXiv:2607. 06230v1 Announce Type: cross Abstract: Parameterized quantum circuits (PQCs) are increasingly used as policies and value functions in quantum reinforcement learning, yet it remains unclear when and why quantum policies generalize.

By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao

reinforcement-learning

arXiv Machine Learning

Sep 22

Watching Quantum Models Think: Hilbert-Space Interpretability in Quantum Transformer Blocks

The paper demonstrates that quantum transformer blocks can be intrinsically interpretable by tracking quantum mutual information, entanglement entropy, and state fidelity across layers. Experiments on four synthetic tasks show that learned mutual information aligns with task structure, entanglement is essential for accuracy, and mutual information predicts prediction correctness. These findings are validated on IBM Quantum hardware, illustrating that quantum computation’s physics can provide observable interpretability signals.

By Diego Iacopetta, Andrea Gasparini

llms safety

arXiv AI

Jul 7

A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference

arXiv:2603. 09789v3 Announce Type: replace-cross Abstract: Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data.

By Yixiong Chen

efficiency

arXiv Machine Learning

Aug 21

Quantum Gaussian processes for prediction of channel observations

arXiv:2608. 19306v1 Announce Type: cross Abstract: Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolution, using only a limited number of measurements.

By Jonas J\"ager, Yaroslav Khmelnitskiy, Paolo Braccia, Artur Miroszewski, Diego Garc\'ia-Mart\'in, M. Cerezo, Piotr Czarnik

safety

arXiv AI

Jun 4

Quantum entanglement provides a competitive advantage in adversarial games

arXiv:2603. 10289v2 Announce Type: replace-cross Abstract: Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question.

By Peiyong Wang, Kieran Hymas, James Quach

agents reinforcement-learning safety

arXiv AI

Jun 10

Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations

arXiv:2606. 10448v1 Announce Type: cross Abstract: The financial market is a typical low signal-to-noise ratio (SNR) setting, which often destabilizes off-policy maximum-entropy methods like Soft Actor-Critic (SAC).

By Zeyu Liu, Xuanzhi Feng, Sing Kwong Lai, Yuanchen Gao, Xiaoyi Pang, Hualei Zhang, Jingcai Guo, Jie Zhang, Song Guo

reinforcement-learning safety

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