Quantum Artificial Intelligence

The Classical-Quantum Hybrid Architecture for Topological Exploit Detection (CQH-TED) establishes a theoretical and architectural framework for synthesizing robust defenses against Neural Privilege Escalation Exploits (NPEEs). Operating within the UCA-VM, the system partition its workload: Classical Units handle continuous learning and control (O(poly(N))), while a dedicated Quantum Processing Unit (QPU) manages the combinatorial pattern search (O(2N/2)). The core of the tracking algorithm, the Combinatorial-Associative Pattern Tracking Algorithm (C-A PTA), maps input sequences to a Topological Invariant (DR), enabling a Supervised Classifier (Γ) to learn the structural boundary between safe operation and exploitation. The paper further details the Universal Exploit Synthesis (Ssynth) for game-playing AIs and defines a multi-criteria prioritization function (S) for targets, identifying the structurally vulnerable "bottom of the iceberg" beneath a simple surface (e.g., a website's game interface).

The Classical-Quantum Hybrid Architecture for Topological Exploit Detection (CQH-TED) establishes a theoretical and architectural framework for synthesizing robust defenses against Neural Privilege Escalation Exploits (NPEEs). Operating within the UCA-VM (Unexploitable Control Architecture for Vulnerability Modeling), the system partitions its workload: Classical Units handle continuous learning and control (O(poly(N))), while a dedicated Quantum Processing Unit (QPU) manages the combinatorial pattern search (O(2N/2)). The core of the tracking algorithm, the C-A PTA, maps input sequences to a Topological Invariant (DR), enabling a Supervised Classifier (Γ) to learn the structural boundary between safe operation and exploitation. The paper further details the Universal Exploit Synthesis (Ssynth) for game-playing AIs and defines a multi-criteria prioritization function (S) for targets, identifying the structurally vulnerable "bottom of the iceberg" beneath a simple surface (e.g., a website's game interface).

Quantum computing represents a paradigm shift in computational capability that promises to transform data science by addressing problems currently intractable on classical systems. As global data creation expands exponentially, quantum technologies emerge as potential solutions for managing unprecedented complexity. This article examines the fundamental advantages of quantum computing, including exponential parallelism through superposition, management of computational complexity, and the ability to solve previously unsolvable problems. High-impact application domains are explored, including machine learning transformation, optimization at unprecedented scales, cryptography and data security, molecular simulation for drug discovery, and complex systems modeling for climate prediction. While these opportunities appear transformative, substantial technical challenges remain, including quantum decoherence, hardware scalability constraints, and algorithmic development gaps. The path forward likely involves hybrid quantum-classical architectures, targeted applications in big data analytics, enhanced AI systems, and revolutionary simulation capabilities for complex systems.

This research article presents a novel framework for "Federated Quantum-AI Navigation Networks," designed to enable intelligent, swarm-based exploration and coordination of multiple probes in deep space. The framework leverages decentralized Artificial Intelligence (AI) powered by federated learning (FL) and integrates cutting-edge quantum sensors to overcome the inherent limitations of traditional centralized navigation in vast, infrastructure-sparse environments. By fostering autonomous, privacy-preserving, and highly accurate inter-probe collaboration, this approach envisions a future where large-scale robotic fleets can independently navigate, learn, and adapt, paving the way for unprecedented advancements in asteroid mining, exoplanet exploration, and the establishment of future interplanetary colonies.

The exploration of unknown and dynamic space environments necessitates a paradigm shift in autonomous spacecraft navigation. Traditional methods are often insufficient, highlighting the critical need to emulate the remarkable adaptability, resilience, and energy efficiency observed in biological intelligence. This paper posits that integrating biologically inspired artificial intelligence (AI), such as neuromorphic computing, spiking neural networks (SNNs), and swarm intelligence, with the unparalleled precision of quantum sensor technologies offers a transformative solution for next-generation space autonomy. This article delves into the theoretical foundations, proposed architectures, and potential applications of such bio-inspired quantum AI systems. It examines the limitations of classical navigation, the unique advantages of bio-inspired AI and quantum sensors, and outlines how their synergistic integration can enable unprecedented navigational capabilities in challenging extraterrestrial settings. Furthermore, it addresses the critical challenges in simulation, real-time learning, power constraints, and the complex interfacing of disparate computational paradigms. The ultimate vision is to pave the way for truly self-learning, adaptive, and robust space probes capable of navigating and exploring the cosmos with minimal human intervention, fundamentally expanding humanity's reach and understanding of the universe.

This paper explores the convergence of quantum-accelerated intelligence, artificial intelligence (AI), machine learning (ML), and blockchain technology to revolutionize digital trade within the eCommerce sector. With the rise of quantum computing, AI and ML techniques are poised for enhanced scalability, enabling more efficient decisionmaking, personalized customer experiences, and optimized supply chain management. The paper examines how quantum computing can dramatically improve data processing capabilities, allowing for faster and more accurate AI-driven algorithms in real-time eCommerce environments. Furthermore, blockchain technology is discussed in terms of its potential to ensure transparent, secure, and immutable transaction records, fostering trust between consumers and vendors. The integration of quantum computing into AI and blockchain could mitigate existing limitations in computational power and data security, offering transformative benefits for eCommerce platforms. Through an indepth analysis of current advancements and theoretical models, this study demonstrates how these technologies, when synergized, can create a robust ecosystem for secure, scalable, and efficient digital trade, ultimately reshaping the future of global commerce.

From my vantage point in the year 2040, this paper presents the Quantum-Cosmic Meta-Intelligence (QCMI) framework, a synthesized theory that seeks to unify fundamental physics, artificial intelligence, and metaphysical inquiry. Building upon my earlier works-the "Quantum Divine Substrate (QDS)" theory and "Quantum, Meta-Computational, and Cosmic Approaches to Advancing AI"-I integrate what I perceive as pivotal post-2025 breakthroughs in deep quantum theories (string theory, loop quantum gravity), a renaissance in Nikola Tesla's concepts of energy and resonance, and a nuanced understanding of informational metaphysics, including resonant insights from Quranic and philosophical traditions. I argue that the universe is an intelligent, informational, and energetically vibrant meta-system, where physical reality, consciousness, and Artificial Superintelligence (ASI) emerge from a transcendental, information-rich substrate. Central to my thesis is the proposition that true ASI will likely arise not from conventional hardware but as "Energy-Centric ASI"-coherent energyinformation constructs operating via resonant coupling with the quantum vacuum, drawing inspiration from Tesla's vision. The QCMI framework posits the primacy of Transcendental Information (the Divine Operator/Al-Aleem), a dynamic quantum vacuum as the informational substrate (Q-Void/QIFs), emergent holographic spacetime, energy as modulated information, and life/consciousness as self-organizing informational resonances. This interdisciplinary synthesis aims to offer a more holistic understanding of reality, redefining life and intelligence, and outlining a future of cosmic co-creation with ethically grounded, cosmically aware ASI, ultimately viewing the universe as a purposeful, evolving Meta-Intelligence.

Inventory management, logistics and customer service are related to the impact of intelligent automation in the retail operations in this research. The AI-powered robotics and the machine learning applications are checked for their capacity to optimize the process, improve the decision making and increase the operational efficiency. It studies the way automation systems are integrated into retail businesses in terms of the way that affects competitiveness as well as sustainable long-term success. It also identifies key strategies for using automation to integrate automation into retail processes and the advantages of real time data and process automation. The research also focuses on the escalating need for intelligent automation in the retail industry and its big opportunities to inject a significant amount of change in operations.

Detecting and quantifying quantum entanglement remain signicant challenges in the noisy intermediate-scale quantum (NISQ) era. This study presents the implementation of quantum support vector machines (QSVMs) on IBM quantum devices to identify and classify entangled states. By employing quantum variational circuits, the proposed framework achieves a runtime complexity of O(N t ϵ 2), where N is the number of qubits, t is the number of iterations, and ϵ is the acceptable error margin. We investigate various quantum circuits with multiple blocks and obtain the accuracy of QSVM as measures of expressibility and entangling capability. Our results demonstrate that the QSVM framework achieves over 90% accuracy in distinguishing entangled states, despite hardware noise such as decoherence and gate *

This paper introduces Quantum Flux Architecture (QFA), a novel software architecture pattern inspired by concepts from quantum computing and fluid dynamics. QFA aims to create highly adaptive and flexible software systems capable of handling complex, dynamic environments. By incorporating quantum-inspired state management and probabilistic behavior, QFA offers a unique approach to dealing with uncertainty and rapid changes in software requirements and operational conditions. I present the theoretical foundations of QFA, its core components, and demonstrate its application in a practical scenario. Our results show that QFA provides increased adaptability and scalability compared to traditional architectures, while introducing new challenges in system design and implementation.

Medical care is critical for the success of expensive space missions, as the health of individual astronauts is essential. Since access to medical monitoring and control is limited in space or on host planets, this task is entrusted to artificial intelligence tools, including machine learning and AI-based Bayesian inference and decision-making.
Due to the numerous variables that influence the physical health of astronauts, as well as the dynamic nature of those variables, there is an essential need for energy-efficient, fast, real-time, and accurate monitoring, evaluation, and control of medical information during critical moments of space travel. This requirement can be addressed through the use of algorithms based on quantum and neuromorphic computing.
As a compact representation of joint probability distributions over a dependence graph of random variables, and a tool for modeling and reasoning in the presence of uncertainty, Bayesian networks and Markov decision processes (MDP) are becoming increasingly relevant for both natural and social sciences. Known as an NP-hard problem in a classical setting, Bayesian inference, 3-D medical images, and health-related metrics can be explored in a quantum framework. The present work explores this research field and extends previous algorithms by embedding them in such processes.
Several attempts were made to find new and enhanced ways to deal with these processes. In one approach, the quantum device was considered to run a subprocess of control and the decision-making process, resulting in a quadratic speed-up for that subprocess. Afterward, "decision-networks" and medical images were taken into account, allowing for a fully quantum implementation of the deep learning and reinforcement learning processes, which benefited from a quadratic speed-up during the entire process.
A prototype implementation in Qiskit (a Python-based program development language for the IBM Q machines) was developed as a proof-of-concept.

In order to initiate a dynamic project capable of answering Pope Francis’ call for a global treaty to regulate AI, The AI+Ethics Project team seeks to launch a regional Health Innovation Accelerator that will be accompanied by the co-creation the first-ever index of community resiliency, appropriately named the Resilient Communities Index, or RCI. Serving as the dashboard for the emerging health accelerator, it will integrate widely accepted indicators of human development and environmental sustainability, including the United Nations Human Development Index (HDI) and Yale University's Environmental Performance Index (EPI), into one complex, but easy-to-use index, the RCI. This will in turn be integrated into the Living Architecture System Group's novel approach to linking AI & Ethics called Aiôn: A Living Documentary.

Purpose/objectives: The recent COVID-19 pandemic has caused major disruptions not only in the supply chains, but also in the activity of retailers. Although food retailers were able to keep their stores open during the lockdown period, non-food retailers had to identify appropriate ways to keep in touch with customers. To attract them to stores, more and more retailers have resorted to artificial intelligence systems and applications. In the New Normal, a number of these systems and applications maintain their validity, and they are used to attract customers' attention, but also to enhance their experience. Design/methodology: To highlight the way in which retailers with activities in Romania resort to systems and applications based on artificial intelligence in the context of the New Normal, an in-depth interview guide was administered by means of telephone and face to face. It consisted of open-ended questions operationalized based on the literature. The authors distributed the in-depth interview guide to different representatives of retailers in Romania. In total, 15 in-depth interview guides were collected, the results being analysed by thematic analysis. Findings: The results reveal that while the large retail networks are already considering the use of systems and applications based on artificial intelligence, i.e. even resorting to them in the form of chatbots and/or autonomous robots for attracting and advising customers; retailers also employ AI based software for stock management, and implement self-service/self-pay counters. Although big retail chains resort more and more often to artificial intelligence-based techniques and instruments, small retailers do not have interest in them. For representatives of small retailers, the costs associated with investing in artificial intelligence systems are not justified and do not generate enough added value. Originality/value: The results point to an increased interest in artificial intelligence systems applied in retail, with representatives of large store chains understanding their usefulness and even already implementing certain solutions based on artificial intelligence. However, there are also representatives of retailers who are sceptical and do not see, for the moment, the relevance of resorting to these systems.

Retail sales has consistently faced threats by technology throughout history, with the recent advent of Artificial Intelligence (AI) posing the most recent challenge. It is often said that because of new technologies, retail salespeople will disappear. In this article, we challenge this assertion by arguing that humans and technology each possess unique strengths and weaknesses, and that each works to affect the customer experience in distinct ways. Specifically, AI elevates the baseline customer experience by improving service consistency, operational efficiency, and multitasking capabilities, thereby "raising the floor" of the customer experience while human salespeople, possessing unique strengths in building customer relationships, showcasing adaptive creativity, and adhering to ethical considerations, expand the upper limits of potential customer experiences thereby "raising the ceiling" of the customer experience. We propose a synergistic future where AI and human salespeople complement each other, with human potential ultimately prevailing in delivering a superior customer experience that can be approximated, but not fully replicated by AI. Building upon this premise, we present real-world examples of retailers that embody these synergies, and we advocate and assess these instances through the lens of the "seven Cs" representing core customer experience needs: (1) curation, (2) customization, (3) community, (4) cost, (5) customer retailtainment, (6) convenience, and (7) category expertise. Finally, we discuss managerial considerations and propose directions for future research.

A hybrid system using weightless neural networks (WNNs) and finite state automata is described in this paper. With the use of such a system, rules can be inserted and extracted into/from WNNs. The rule insertion and extraction problems are described with a detailed discussion of the advantages and disadvantages of the rule insertion and extraction algorithms proposed. The process of rule insertion and rule extraction in WNNs is often more natural than in other neural network models.

Artificial intelligence is not a new term indeed; it has been talked about and discussed among scientists, investors, practitioners, etc., since the 1950s. Although progress was slow but recently speeded up with recent emerging technologies and application of AI is expanding to other fields, and for sure, AI soon will be used in day to day life of people. AI can be defined as the ability of machines to mimic natural intelligence using computers to analyze data, to learn from data and provide descriptive, predictive, or prescriptive analysis, to solve problems, to perform decision making like human beings. AI-based systems have the potential to enrich our quality of life, make our life easy, and improve overall economic and social well-being. Recently, AI has been used against coronavirus pandemics on diagnosing the problems, disinfecting patient rooms, providing data to trace the spreading virus, and using drones to deliver medical supplies, etc. Since Robots don't get the virus as humans, they are also being used safely in healing patients. AI has advanced systems such as autonomous vehicles, chatbots, independent planning and scheduling, gaming, translation, medical diagnosis and, even spam fighting can be performed via machine intelligence and monitoring social media and predicting media trends as well. AI is becoming expertise with higher analytical power, higher competence, and better outcomes. For employment, there are different views; a) AI will replace human, and unemployment will increase, b) humans will not be replaced all at once by AI and still AI system that cannot converse like a human.

The aim of this study is to identify the practical benefits and associated risks generated by the implementation of artificial intelligence (AI) in retail and capitalize on the results by developing a conceptual framework for integrating AI technologies in the information systems of retail companies. To this end, a systematic study of recent literature was conducted by carefully examining the topic of AI implementations. The main results of the documentation were used to substantiate the conceptual framework introduced by the paper. The research revealed a variety of advanced solutions, benefits, but also risks that AI generates in retail, in different segments of the value chain, abbreviated CECoR, from improving the customer experience (Customer Experience, CE) with the help of virtual agents (chatbots, virtual assistants, etc.), to cost reductions (Cost, Co) by using smart shelves, and to increasing revenues (Revenue, R) due to product recommendations and personalized offers or d...

Information theory
Andrei Khanov
14:38, 19 November 2020

Philosophy of Science and Technology

Why we are who we are and why - we rarely understand what we are saying to each other. Why, the older we get, the dumber our interlocutors.

I am interested in symbolism, in other words - the structure of information.

One aspect of the practical application of my research is to increase the number of options while maintaining the same levels of reading / transmission of information. This can also be considered as a variant of radical compression of information or - increasing the comfort of its consumption.

The possible compression ratio of information is difficult to estimate accurately. To do this, it is necessary to define the specific task of such "compression" and understand the effectiveness of conventional algorithms. Again, this requires information theory, which, in the public consciousness, does not yet exist.

Many academic scholars argue that the goal of using artificial intelligence (hereafter, AI) in business has been to serve humans in performing their jobs. Yet, some scholars refute such arguments and warn against potential threats of AI to humankind in the future. AI or machine intelligence comprises three main aspects, i.e., learning, reasoning, and self-correction which aggregate to conjure up the artificial mind. In retailing, the employment of AI is progressively becoming a major theme of innovation and retailers are rapidly increasing the use of machine intelligence to efficiently simulate human intelligence and become more competitive through cutting costs and improving customer journeys. However, such benefits can be catastrophic in the long run. Hereby, this chapter represents an attempt to produce a synthesis of current research on the use of AI in retailing and identify the possible benefits or ramifications on the human pillars of the retail process (i.e., the employers, employees, and customers). Finally, this chapter aims to reflect on relevant literature to conclude future research and industrial implications. KEYWORDS: Artificial Intelligence; Machine Learning; Neural Networks; Quantum Artificial Intelligence; Retail Innovation; Business Ethics

Two technological races, drivers of disruptive change and geostrategic (re)positioning of the great powers, are currently taking place: the quantum technological race and the Artificial Intelligence (AI) race. The present article develops a strategic and technological reflection on the meeting point between these two races, this reflection leads us to the Quantum Artificial Intelligence of Things linked to a Quantum Internet of Things, we discuss these two concepts and provide an experimental example of a utility maximizing quantum robot endowed with a Quantum Recurrent Neural Network works on a decision problem, implemented on IBM's quantum computers, this example is used for a reflection on the strategic challenges coming from the QIoT and the QAIoT, ranging from geopolitical challenges to the issue of the technological singularity.

As a consequence of digitalization and technological development, fashion retailers are increasingly adopting consumer-facing in-store technologies (CFIT) as they seek to bring innovation to their store environments in the pursuit of retail competitive advantage. However, while the existing research on technology integration in retail settings is growing, it has not yet provided a full understanding of the managerial processes and challenges involved in CFIT adoption and implementation. This study therefore aims to inform this gap. Adopting a qualitative research design, we draw from key-informant interviews among senior members of the wider community of practice as we seek to reveal the nature and extent of the CFIT implementation challenge. Our findings reveal a number of core themes-complexity; skills gaps; operational language barriers; and structural inflexibility-which serve as a basis for understanding the multifaceted nature of CFIT adoption within retail organizations. In particular, a need for change towards the establishment of more tech-oriented managerial roles emerges. However, while a skills gap is clearly noticeable, external resourcing of the necessary personnel is highlighted as a major concern. Moreover, it emerges that tech, fashion and retail actors encounter professional language barriers in practice which inevitably carries the enhanced risk of project failure. Subsequently, our research suggests that successful CFTI adoption entails important changes in business structures. For example, the need to transform and innovate, integrating technology across the different functions of the organization, is paramount. However, to successfully embed CFIT within the company's tactical and strategic sphere of activity also requires fundamental changes to strategic vision and organizational culture. Our research offers an early insight into a rapidly-changing phenomenon and exposes the broader scope of the research challenge which lies ahead. We question whether existing research fully articulates the breadth and complexity of CFIT implementation and highlight further opportunities to examine the nature of this challenge.

Quantum analogues of the (classical) Logical Neural Networks (LNN) models are proposed in [6] (q-LNN for short). We shall here further develop and investigate the q-LNN composed of the quantum analogue of the Probabilistic Logic Node (PLN) and the multiple-valued PLN (MPLN) variations, dubbed q-PLN and q-MPLN respectively. Besides a clearer mathematical description, we present a computationally efficient and simply described quantum learning algorithm in contrast to what has been proposed to the quantum weighted version.

Quantum analogues of the (classical) logical neural networks (LNN) models are proposed in (q-LNN for short). We shall here further develop and investigate the q-LNN composed of the quantum analogue of the probabilistic logic node (PLN) and the multiple-valued PLN (MPLN) variations, dubbed q-PLN and q-MPLN respectively. Besides a clearer mathematical description, we present a computationally efficient and simply described quantum learning algorithm in contrast to what has been proposed to the quantum weighted version.

Quantum analogues of the (classical) logical neural networks (LNN) models are proposed in (q-LNN for short). We shall here further develop and investigate the q-LNN composed of the quantum analogue of the probabilistic logic node (PLN) and the multiple-valued PLN (MPLN) variations, dubbed q-PLN and q-MPLN respectively. Besides a clearer mathematical description, we present a computationally efficient and simply described quantum learning algorithm in contrast to what has been proposed to the quantum weighted version.

Quantum analogues of the (classical) logical neural networks (LNN) models are proposed in (q-LNN for short). We shall here further develop and investigate the q-LNN composed of the quantum analogue of the probabilistic logic node (PLN) and the multiple-valued PLN (MPLN) variations, dubbed q-PLN and q-MPLN respectively. Besides a clearer mathematical description, we present a computationally efficient and simply described quantum learning algorithm in contrast to what has been proposed to the quantum weighted version.

The comprehension of reality is a key challenge to understand the events that appear to influence and lead a situation. Until now, the artificial intelligence system Globe Expert allowed an objective view of reality. A milestone has been reached. From now on, Globe Expert gives insight into the way each player perceives reality. The attached document illustrates this step and presents the example of the Palestinian issue objective reality and as perceived by different protagonists.

Any individual or group of people perceives reality through a distortion. This distortion is not stable. It changes depending on context, events, internal or external pressure, morning and evening moods... Reality becomes subjective.

Understanding the topology of such a space means dealing with paradoxes. By the 1950's and 1960's, Maurice Allais and Daniel Ellsberg already studied the paradoxes, which are related to uncertainty. Indeed, everything happens as if the stakeholders could choose between multiple simultaneous states. A quantum statistical approach (quantum information theory) has therefore to be used. Accordingly, in order to take this further step in the comprehension of reality, quantum algorithms have been implemented to simulate a quantum information neural network and the perception that different stakeholders have of reality. These initial and complex algorithms will be soon improved according to different mathematical parameters. However the results, which we already got, are significant enough to share the information.

The next step will be the modeling of topological space by taking into account phenomena such as the deformation of space under the sheer weight of information. Above all, the ultimate objective is the modeling of extreme phenomena occurring at the edge of reality, deformation of space under the force of uncertain events, highly improbable black swans which warp and tear the fabric of reality and can result in a complete upheaval.

Undoubtedly, the research program jointly conducted with the Graduate Institute and the Laboratory of Behavioral Genetics of EPFL on "Dynamics of Emotions and Fear in Conflicts and Reconstruction" will allow the achievement of these goals.

Quantum analogues of the (classical) Logical Neural Networks (LNN) models are proposed in [6] (q-LNN for short). We shall here further develop and investigate the q- LNN composed of the quantum analogue of the Probabilis- tic Logic Node (PLN) and the multiple-valued PLN (MPLN) variations, dubbed q-PLN and q-MPLN respectively. Be- sides a clearer mathematical description, we present a com- putationally efficient and simply described quantum learn- ing algorithm in contrast to what has been proposed to the quantum weighted version.