Any computer can create a model of reality. The hypothesis that quantum computer can generate such a model designated as quantum, which coincides with the modeled reality, is discussed. Its reasons are the theorems about the absence of “hidden variables” in quantum mechanics. The quantum modeling requires the axiom of choice. The following conclusions are deduced from the hypothesis. A quantum model unlike a classical model can coincide with reality. Reality can be interpreted as a (...) quantum computer. The physical processes represent computations of the quantum computer. Quantum information is the real fundament of the world. The conception of quantum computer unifies physics and mathematics and thus the material and the ideal world. Quantum computer is a non-Turing machine in principle. Any quantum computing can be interpreted as an infinite classical computational process of a Turing machine. Quantum computer introduces the notion of “actually infinite computational process”. The discussed hypothesis is consistent with all quantum mechanics. The conclusions address a form of neo-Pythagoreanism: Unifying the mathematical and physical, quantum computer is situated in an intermediate domain of their mutual transformation. (shrink)

Quantum computing promises to revolutionize computation by leveraging superposition, entanglement, and quantum interference. This paper examines whether it can bridge the ontological divide between processual reality (Monos) and representational structures (Logos) in the context of Artificial General Intelligence (AGI). Drawing on Metamonism CORE = v1.3, we argue that quantum systems, as currently conceived, accelerate stabilization rather than prevent it, remaining trapped in Logos-dominant paradigms. We distinguish the illusion of an ontological bridge—where quantum processuality serves classical (...) fixation—from a genuine integration that could enable enforced Unfold. While quantum dynamics approximates Monos, measurement enforces Logos, making quantum computing a more efficient optimizer rather than a pathway to non equilibrium cognition. This analysis reframes quantum computing not as an AGI enabler but as a test case for ontological viability in intelligence research and establishes it as a prime example of accelerated fixation, disqualifying it from consideration as a foundation for non-equilibrium cognition. (shrink)

We present a geometric framework for holonomic quantum computing in which quantum gates arise from global properties of control manifolds rather than fine-tuned dynamical evolution. Quantum states are modeled as complex projective fibers over a classical control manifold, and adiabatic loops induce unitary gates through Berry and Wilczek-Zee holonomy. Within this setting, we introduce Quantum Inner State Manifolds (QISMs) as symplectic fiber bundles equipped with a natural unitary connection governed by the Fubini-Study form. Using the (...) Ambrose-Singer theorem, we show that generic QISMs generate holonomy groups dense in U(N), establishing universality. Fault tolerance emerges from global geometric features, providing a robust geometric foundation for quantum gate design. (shrink)

Pattern recognition is represented as the limit, to which an infinite Turing process converges. A Turing machine, in which the bits are substituted with qubits, is introduced. That quantum Turing machine can recognize two complementary patterns in any data. That ability of universal pattern recognition is interpreted as an intellect featuring any quantum computer. The property is valid only within a quantum computer: To utilize it, the observer should be sited inside it. Being outside it, the observer (...) would obtain quite different result depending on the degree of the entanglement of the quantum computer and observer. All extraordinary properties of a quantum computer are due to involving a converging infinite computational process contenting necessarily both a continuous advancing calculation and a leap to the limit. Three types of quantum computation can be distinguished according to whether the series is a finite one, an infinite rational or irrational number. (shrink)

We present a geometric framework for holonomic quantum computing in which quantum gates arise from global properties of control manifolds rather than fine-tuned dynamical evolution. Quantum states are modeled as complex projective fibers over a classical control manifold, and adiabatic loops induce unitary gates through Berry and Wilczek-Zee holonomy. Within this setting, we introduce Quantum Inner State Manifolds (QISMs) as symplectic fiber bundles equipped with a natural unitary connection governed by the Fubini-Study form. Using the (...) Ambrose--Singer theorem, we show that generic QISMs generate holonomy groups dense in U(N), establishing universality. Fault tolerance emerges from global geometric features, providing a robust geometric foundation for quantum gate design. (shrink)

This paper explores the convergence of quantum computing and artificial intelligence (AI), examining how their integration may redefine computational paradigms. Quantum computing, with its unique properties of superposition and entanglement, has the potential to exponentially accelerate AI processes, particularly in optimization, machine learning, and data analysis. We investigate quantum algorithms, such as the quantum Fourier transform and Grover’s algorithm, highlighting their application to AI models and machine learning tasks that require vast computational resources. The (...) paper further delves into hybrid quantum-classical approaches, which leverage the strengths of both domains to address real-world problems. Challenges, such as quantum error correction, scalability, and the need for specialized hardware, are also discussed. We provide an analysis of ongoing advancements in quantum AI, including quantum-enhanced neural networks and reinforcement learning, and their implications for fields like natural language processing and predictive analytics. This research emphasizes the transformative potential of quantum AI while acknowledging the significant technical hurdles that remain. The integration of quantum computing and AI promises to unlock unprecedented computational capabilities, paving the way for breakthroughs in scientific research, industry applications, and complex problem-solving. (shrink)

Quantum computing is a rapidly developing technology that uses the principles of quantum mechanics to perform information processing tasks that are beyond the capabilities of traditional computers. Quantum computers employ qubits, superposition, and entanglement to rapidly resolve intricate problems, with potential applications in cryptography, artificial intelligence, the development of new medications, and financial modeling. Recent breakthroughs have shown potential, but hurdles like correcting errors, maintaining qubit stability, expanding and the expense of substantial infrastructure continue to hinder (...) widespread usage. Quantum computing also sparks ethical and security concerns, especially in relation to encryption and the development of artificial intelligence. Despite some challenges, continuous research and development are gradually enhancing the capabilities of quantum hardware and algorithms. Advances in quantum technology are anticipated to transform various sectors and scientific inquiry, providing answers to several of the globe's most intricate problems. To maximize the potential of this initiative, additional investment in education, policy refinement, and judicious implementation is essential to guarantee a balanced and positive influence on society. (shrink)

This article analyzes how generative AI and emerging quantum computing technologies are reshaping the epistemic role of the scientist. Large language models now perform many tasks that once functioned as core signals of scientific competence: literature synthesis, code scaffolding, formula checking, and the production of grammatically polished text. At the same time, journals and preprint servers are tightening policies on AI-assisted writing, generating a tension between the ubiquity of these tools and the suspicion attached to “AI-like” prose. -/- (...) This situation is interpreted as a temporary phase of normative dissonance in which debates concentrate on surface features of text production rather than on the deeper structure of scientific authorship. The article proposes a shift in evaluation criteria from manual textual labour to conceptual originality, demonstrable understanding, and accountability for scientific claims. Special attention is given to non-native English speakers, for whom generative AI can function as an instrument of epistemic justice by decoupling linguistic polish from intellectual contribution. -/- A trilateral discovery workflow is outlined in which quantum devices handle high-dimensional computation, AI systems translate complex outputs into human-readable patterns, and human scientists retain epistemic authority through design, interpretation, and ethical judgment. Under this view, AI and quantum computing do not replace scientists; they relocate the value of scientific work from routine execution to the architecture and critical defence of ideas. (shrink)

This paper reexamines the patent entitled “Quantum Computer Based on Probabilistic Geometry” (Publication No. 2019-220121, Japan Patent Office, 2019) in light of the Tenson Theory. By combining probabilistic number theory with geometric tensor analysis, we propose a control algorithm that represents quantum bit transitions on a probabilistic manifold. The aim is to reinterpret quantum information processing as a geometrical regulation of uncertainty, bridging mathematical physics, information science, and the philosophy of probability.

Quantum-enhanced machine learning (QML) is transforming artificial intelligence through the application of quantum computing concepts to solving computationally challenging problems more effectively than conventional methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the capability to speed up deep learning model training, solve combinatorial optimization problems, and improve feature selection in high-dimensional space. It covers basic quantum computer concepts employed within AI, for example, quantum circuits, quantum variational algorithms, and kernel quantum (...) methods, and their impacts on neural networks, generative models, and reinforcement learning. It further refers to the hybrid quantum-classical architectures in AI where a combination of quantum subroutines and classical deep learning models are employed together with the purpose to gain computational speedup in optimization and handling massive data. Despite the transformative promise of quantum AI, technical issues of qubit noise, error correction, and scaling hardware continue to hold back full implementation. This contribution offers a qualitative overview of quantum-enhanced AI, surveying current applications, research endeavors, and upcoming innovation in quantum deep learning, autonomous systems, and scientific computing. The results open the door for large-scale quantum machine learning architectures, which provide new solutions to future uses of AI in finance, medicine, cyber security, and robotics. (shrink)

Quantum-enhanced machine learning (QML) is transforming artificial intelligence through the application of quantum computing concepts to solving computationally challenging problems more effectively than conventional methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the capability to speed up deep learning model training, solve combinatorial optimization problems, and improve feature selection in high-dimensional space. It covers basic quantum computer concepts employed within AI, for example, quantum circuits, quantum variational algorithms, and kernel quantum (...) methods, and their impacts on neural networks, generative models, and reinforcement learning. It further refers to the hybrid quantum-classical architectures in AI where a combination of quantum subroutines and classical deep learning models are employed together with the purpose to gain computational speedup in optimization and handling massive data. Despite the transformative promise of quantum AI, technical issues of qubit noise, error correction, and scaling hardware continue to hold back full implementation. This contribution offers a qualitative overview of quantum-enhanced AI, surveying current applications, research endeavors, and upcoming innovation in quantum deep learning, autonomous systems, and scientific computing. The results open the door for large-scale quantum machine learning architectures, which provide new solutions to future uses of AI in finance, medicine, cyber security, and robotics. (shrink)

Quantum computing has the potential to revolutionize problem-solving across various domains, from cryptography to materials science. However, the complexities of quantum hardware, including the need for highly specialized environments and significant computational resources, have made quantum computing difficult for most organizations to access. Cloud-native quantum computing, which leverages cloud infrastructure to provide scalable, ondemand access to quantum processors, offers a transformative solution to this challenge. This paper explores the rise of cloud-native (...) class='Hi'>quantum computing, focusing on how cloud infrastructure facilitates the deployment and execution of quantum algorithms. We examine the benefits of cloud-based quantum computing, the challenges it faces, and the role of cloud service providers in advancing quantum technologies. Additionally, we explore the synergy between quantum computing and classical computing, emphasizing hybrid quantum-classical systems. Finally, we look ahead to the future of quantum computing in the cloud, outlining emerging trends, research opportunities, and the potential impacts on industries such as finance, healthcare, and logistics. (shrink)

Natural argument is represented as the limit, to which an infinite Turing process converges. A Turing machine, in which the bits are substituted with qubits, is introduced. That quantum Turing machine can recognize two complementary natural arguments in any data. That ability of natural argument is interpreted as an intellect featuring any quantum computer. The property is valid only within a quantum computer: To utilize it, the observer should be sited inside it. Being outside it, the observer (...) would obtain quite different result depending on the degree of the entanglement of the quantum computer and observer. All extraordinary properties of a quantum computer are due to involving a converging infinite computational process contenting necessarily both a continuous advancing calculation and a leap to the limit. Three types of quantum computation can be distinguished according to whether the series is a finite one, an infinite rational or irrational number. -/- . (shrink)

This article analyzes the feasibility of implementing the mathematical model used in quantum computing systems. The article questions whether certain mathematical properties of the qubit can be physically implemented, highlighting that quantum theory may have been built by misinterpreting experimental evidence. At the end of the article, it is shown mathematically that the mathematical model of quantum computing, restricted so that it can be implemented physically, is equivalent to a mathematical model of classical computing (...) without states, so it can be concluded that quantum supremacy is unattainable. (shrink)

The GOOGLE and XPRIZE $5,000,000 for the practical and socially useful utilization of the quantum computer is the starting point for ontomathematical reflections for what it can really serve. Its “output by measurement” is opposed to the conjecture for a coherent ray able alternatively to deliver the ultimate result of any quantum calculation immediately as a Dirac -function therefore accomplishing the transition of the sequence of increasingly narrow probability density distributions to their limit. The GOOGLE and XPRIZE problem’s (...) solution needs the initial understanding that the result of any quantum calculation is a wave function, or respectively, a probability (density or not) distribution unlike the Turing machine one, and only a “calculating ray” is able to transform the former into the later without any “curving” disturbances. Thus, the unique capability of the quantum computer due to its inherent quantum parallelism can be conserved for all Gödel unresolvable problems, only on the fast “NP” track of which the quantum computer “Achilles” is able practically to overrun the Turing machine “Tortoise” since the latter can “sprint” only by any “P” calculating speed even on it and unlike “Achilles” himself able for “NP” velocities as well. The way for any material body to “calculate” the certain trajectory of least action also resolves the “traveling salesman problem” in fact, therefore illustrating furthermore what the “NP” output of a quantum computer by a “calculating ray” should mean. Another example is the problem of the number of all prime numbers less than “N”, which is suggested to visualize once again the quantum computer capability to resolve problems by a “NP” speed and thus qualitatively to overrun any competing Turing machine. The technically implemented idea of laser is now reinterpreted to “radiate a wave function” in order to explain the “calculating ray” option for a quantum computer “NP” output. The generalization of an entanglement “trigger ray” described by any non-Hermitian operator (unlike a laser) is suggested as well as those “sci-fi” horizons which it reveals. One of them is meant for elucidating the practical meaning of the “Yang-Mills existence and mass gap problem”, one more of the CMI “Millennium Problems” after that of “P vs NP”. -/- . (shrink)

The convergence of quantum computing and machine learning is poised to revolutionize the field of artificial intelligence (AI). Quantum computing offers the potential to exponentially speed up computations, which can be leveraged to overcome the limitations of classical computing in training and inference for machine learning models. Quantum algorithms promise to enhance machine learning tasks, such as optimization, data processing, and pattern recognition, by solving problems that are computationally infeasible for classical machines. This paper (...) explores the synergy between quantum computing and machine learning, focusing on the quantum-enhanced capabilities in AI. We review current research on quantum machine learning (QML) algorithms, discuss their theoretical underpinnings, and present promising applications in areas such as optimization, natural language processing, and drug discovery. Furthermore, we address the challenges and future directions in merging quantum computing with AI, highlighting the potential for accelerated AI solutions and transformative advancements in various industries. (shrink)

The rapid advancement of quantum computing poses a significant threat to classical cryptographic systems, particularly those based on RSA, ECC, and other public-key algorithms. With Shor’s algorithm capable of efficiently factoring large numbers and breaking current encryption standards, the transition to postquantum cryptography (PQC) has become a global priority. This paper explores the impact of quantum computing on cryptographic security, the need for quantum-resistant cryptographic algorithms, and ongoing standardization efforts led by organizations such as NIST. (...) We analyze various post-quantum cryptographic techniques, including lattice-based, hash-based, multivariate, and code-based cryptography, assessing their feasibility for real-world implementation. Additionally, we discuss the challenges associated with transitioning to PQC, including computational overhead, interoperability, and regulatory compliance. As the quantum era approaches, organizations must proactively adopt post-quantum cryptographic solutions to safeguard sensitive data and ensure long-term security in a quantum-capable world. (shrink)

A practical viewpoint links reality, representation, and language to calculation by the concept of Turing (1936) machine being the mathematical model of our computers. After the Gödel incompleteness theorems (1931) or the insolvability of the so-called halting problem (Turing 1936; Church 1936) as to a classical machine of Turing, one of the simplest hypotheses is completeness to be suggested for two ones. That is consistent with the provability of completeness by means of two independent Peano arithmetics discussed in Section I. (...) Many modifications of Turing machines cum quantum ones are researched in Section II for the Halting problem and completeness, and the model of two independent Turing machines seems to generalize them. Then, that pair can be postulated as the formal definition of reality therefore being complete unlike any of them standalone, remaining incomplete without its complementary counterpart. Representation is formal defined as a one-to-one mapping between the two Turing machines, and the set of all those mappings can be considered as “language” therefore including metaphors as mappings different than representation. Section III investigates that formal relation of “reality”, “representation”, and “language” modeled by (at least two) Turing machines. The independence of (two) Turing machines is interpreted by means of game theory and especially of the Nash equilibrium in Section IV. Choice and information as the quantity of choices are involved. That approach seems to be equivalent to that based on set theory and the concept of actual infinity in mathematics and allowing of practical implementations. (shrink)

Quantum computing represents a paradigm shift in computational capabilities, promising to solve complex problems beyond the reach of classical computers. This advancement has significant implications for cloud security, a cornerstone of modern digital infrastructure. Quantum computing poses both opportunities and threats to cloud security mechanisms, particularly in the realms of cryptography, data encryption, and threat detection. This research explores the dual impact of quantum computing on cloud security, examining how quantum algorithms can enhance (...) security protocols and simultaneously undermine existing cryptographic standards. Through a comprehensive literature review, case studies, and empirical data analysis, the study identifies key areas where quantum computing intersects with cloud security, including quantum-resistant cryptographic methods, quantum key distribution (QKD), and the potential for quantum-enhanced security analytics. The findings indicate that while quantum computing can bolster cloud security through advanced encryption techniques and enhanced data integrity, it also necessitates the urgent development and adoption of post-quantum cryptography to safeguard against quantum-enabled cyber threats. The research concludes with strategic recommendations for integrating quantum technologies into cloud security frameworks, emphasizing the need for proactive measures to mitigate risks and leverage quantum advantages effectively. This study contributes to the understanding of quantum computing’s role in shaping the future landscape of cloud security, offering valuable insights for practitioners, policymakers, and researchers. (shrink)

The potential for scalable quantum computing depends on the viability of fault tolerance and quantum error correction, by which the entropy of environmental noise is removed during a quantum computation to maintain the physical reversibility of the computer’s logical qubits. However, the theory underlying quantum error correction applies a linguistic double standard to the words “noise” and “measurement” by treating environmental interactions during a quantum computation as inherently reversible, and environmental interactions at the end (...) of a quantum computation as irreversible measurements. Specifically, quantum error correction theory models noise as interactions that are uncorrelated or that result in correlations that decay in space and/or time, thus embedding no permanent information to the environment. I challenge this assumption both on logical grounds and by discussing a hypothetical quantum computer based on “position qubits.” The technological difficulties of producing a useful scalable position-qubit quantum computer parallel the overwhelming difficulties in performing a double-slit interference experiment on an object comprising a million to a billion fermions. (shrink)

The rapid advancement of quantum computing poses a significant threat to classical cryptographic systems, particularly those based on RSA, ECC, and other public-key algorithms. With Shor’s algorithm capable of efficiently factoring large numbers and breaking current encryption standards, the transition to postquantum cryptography (PQC) has become a global priority. This paper explores the impact of quantum computing on cryptographic security, the need for quantum-resistant cryptographic algorithms, and ongoing standardization efforts led by organizations such as NIST. (...) We analyze various post-quantum cryptographic techniques, including lattice-based, hash-based, multivariate, and code-based cryptography, assessing their feasibility for real-world implementation. Additionally, we discuss the challenges associated with transitioning to PQC, including computational overhead, interoperability, and regulatory compliance. As the quantum era approaches, organizations must proactively adopt post-quantum cryptographic solutions to safeguard sensitive data and ensure long-term security in a quantum-capable world. (shrink)

Quantum computing promises to revolutionize industries by solving complex problems that classical computers cannot efficiently handle. While its potential applications, such as in cryptography, drug discovery, and artificial intelligence, offer immense benefits, they also present significant ethical challenges. This paper explores the ethical implications of quantum computing in the modern world, focusing on concerns related to privacy, security, inequality, and the potential displacement of jobs. The unprecedented computational power of quantum computers raises questions about the (...) ability to break current cryptographic systems, the equitable distribution of quantum technologies, and the risks associated with the acceleration of technological advancements. The paper concludes by offering recommendations for addressing these ethical concerns through policy frameworks, ethical guidelines, and international cooperation. (shrink)

Hydranencephaly is a developmental malady, where the cerebral hemispheres of the brain are reduced partly or entirely too membranous sacs filled with cerebrospinal fluid. Infants with this malady are presumed to have reduced life expectancy with a survival of weeks to few years and which solely depends on care and fostering of these individuals. During their life span these individuals demonstrate behaviours that are termed “vegetative” by neuroscientists but can be comparable to the state of being “aware” or “conscious”. Based (...) on the most simplified definition for consciousness i.e. “awareness” or “to be aware”, these individuals are undeniably aware of their existence and therefore should be termed “conscious”. The bigoted approach of neuroscience towards understanding consciousness is usually linked with the cortex of the brain and therefore a malady as hydranencephaly poses a great challenge to this field. This paper is a compilation of behaviours and aptitudes observed in several cases of hydranencephaly which suggests, that consciousness is not just a brain process, but is a highly quantum computed process that follows laws of quantum physics, giving rise to the subjective experience of consciousness in these individuals. (shrink)

The rapid advancements in cloud computing are transforming enterprise solutions across industries. Among the most promising innovations is quantum computing, which holds the potential to revolutionize how businesses process data, optimize systems, and solve complex problems. Microsoft Azure, a leader in cloud infrastructure, has integrated quantum computing through Azure Quantum to offer scalable quantum solutions. This paper explores the impact of Azure's quantum computing capabilities on enterprise solutions, focusing on scalability, problem-solving (...) efficiency, and future-proofing business operations. By examining Azure’s quantum computing tools, quantum algorithms, and their applications in real-world scenarios, the paper highlights how enterprises can leverage this technology to address computational challenges, enhance performance, and maintain competitive advantage in an increasingly data-driven world. The analysis also addresses the potential hurdles and the importance of preparing cloud infrastructures for the quantum computing era. (shrink)

A set theory model of reality, representation and language based on the relation of completeness and incompleteness is explored. The problem of completeness of mathematics is linked to its counterpart in quantum mechanics. That model includes two Peano arithmetics or Turing machines independent of each other. The complex Hilbert space underlying quantum mechanics as the base of its mathematical formalism is interpreted as a generalization of Peano arithmetic: It is a doubled infinite set of doubled Peano arithmetics having (...) a remarkable symmetry to the axiom of choice. The quantity of information is interpreted as the number of elementary choices (bits). Quantum information is seen as the generalization of information to infinite sets or series. The equivalence of that model to a quantum computer is demonstrated. The condition for the Turing machines to be independent of each other is reduced to the state of Nash equilibrium between them. Two relative models of language as game in the sense of game theory and as ontology of metaphors (all mappings, which are not one-to-one, i.e. not representations of reality in a formal sense) are deduced. (shrink)

The paper addresses Leon Hen.kin's proposition as a " lighthouse", which can elucidate a vast territory of knowledge uniformly: logic, set theory, information theory, and quantum mechanics: Two strategies to infinity are equally relevant for it is as universal and t hus complete as open and thus incomplete. Henkin's, Godel's, Robert Jeroslow's, and Hartley Rogers' proposition are reformulated so that both completeness and incompleteness to be unified and thus reduced as a joint property of infinity and of all infinite (...) sets. However, only Henkin's proposition equivalent to an internal position to infinity is consistent . This can be retraced back to set theory and its axioms, where that of choice is a key. Quantum mechanics is forced to introduce infinity implicitly by Hilbert space, on which is founded its formalism. One can demonstrate that some essential properties of quantum information, entanglement, and quantum computer originate directly from infinity once it is involved in quantum mechanics. Thus, these phenomena can be elucidated as both complete and incomplete, after which choice is the border between them. A special kind of invariance to the axiom of choice shared by quantum mechanics is discussed to be involved that border between the completeness and incompleteness of infinity in a consistent way. The so-called paradox of Albert Einstein, Boris Podolsky, and Nathan Rosen is interpreted entirely in the same terms only of set theory. Quantum computer can demonstrate especially clearly the privilege of the internal position, or " observer'' , or "user" to infinity implied by Henkin's proposition as the only consistent ones as to infinity. An essential area of contemporary knowledge may be synthesized from a single viewpoint. (shrink)

This paper introduces the Unitary Phase Architecture (UPA), a novel computational paradigm rooted in the principles of Phase Ontology, designed to emulate Cyclical Cognition (Cognitio Recurrens) and achieve asymptotic understanding without relying on precise, discrete solutions. Diverging from conventional classical and current quantum computing models that predominantly operate on probabilistic interpretations, UPA postulates a computational environment based on the field of potentials, engaging in an improbabilistic mode of operation. This architecture leverages resonant algorithms that utilize asymptotic phase coherence (...) as their primary computational resource, with all protocols centered on the maintenance and cultivation of stable resonant patterns instead of precise, outcome-oriented measurements. We propose that UPA inherently resolves non-locality through its reliance on phase topology, where computational results emerge as robust configurations of asymptotic phase relations. This framework aims to foster systems capable of invariant, high-order cognition, fundamentally guided by a universal ethics of coherence, thereby offering a blueprint for future computing that mirrors the dynamic, co-emergent nature of reality itself. (shrink)

This essay explores how quantum computers could figuratively achieve backward time travel for the purpose of investigating past grievances, without violating relativistic causality. Drawing upon Everett’s Many-Worlds Interpretation (MWI) of quantum mechanics, recent speculative models of quantum computation, and the futurist engineering work of Marshall Barnes, the paper argues that quantum systems may access parallel universes that function as exact or near-exact informational analogues of past events. These universes are not temporal reversals of our own history, (...) but entangled computational mirrors that allow epistemic access to historically constrained states. Such an approach reframes time travel as an investigative, ethical, and restorative tool rather than a physical traversal of spacetime. The implications for justice, historical reconciliation, and moral risk analysis are briefly discussed. (shrink)

This paper explores the integration of artificial intelligence (AI), machine learning (ML), and quantum computing in revolutionizing diagnostic and therapeutic approaches within modern healthcare. The convergence of these cutting-edge technologies is poised to address critical challenges in healthcare, such as precision medicine, early disease detection, and personalized treatment strategies. AI and ML algorithms, particularly deep learning, are demonstrated to enhance diagnostic accuracy through the analysis of complex medical data, including imaging and genomics. Furthermore, quantum computing presents (...) novel capabilities in processing large datasets, optimizing optimization problems, and accelerating drug discovery processes. This research investigates the synergistic potential of AI, ML, and quantum computing in transforming clinical decision-making processes, enabling more effective and tailored therapeutic interventions. Case studies and practical applications highlight the real-world impact of these technologies, while discussions on their limitations, ethical implications, and future directions provide a comprehensive understanding of their role in shaping the future of healthcare. Challenges related to data privacy, computational resources, and integration into existing healthcare infrastructures are also critically examined. (shrink)

This paper extends the Phase Ontology framework to unveil how quantum computing serves as a profound empirical domain for understanding and validating its core principles. We argue that quantum computation fundamentally transcends classical binary logic, manifesting a deeper phase logic where meaning emerges not from fixed states or probabilistic distributions, but from dynamic resonant alignment within a field of potentials. Drawing on the proposed four-phase Cyclical Cognition (recursion, iteration, asymptotic stabilization, reinitiation), we demonstrate how quantum algorithms (...) like Shor's and Grover's empirically realize these cycles as processes of phase alignment and interference amplification. The paper reinterprets quantum non-locality and entanglement as direct expressions of phase connectivity within a phase-operated topology, rather than instantaneous actions in spacetime, suggesting that spacetime itself is emergent from fundamental phase relations. Crucially, the observed 'classical collapse' of superposition is re-envisioned not as a loss of information or a probabilistic reduction, but as an asymptotic phase alignment where vectoriality (associated with anisotropy and lower order) is transcended, yielding an asymptotic curvature as an invariance of higher order. This perspective leads to a re-evaluation of information processing: quantum computers manipulate information not primarily in physical space, but within a richer phase space, allowing for efficiencies beyond classical relativistic limitations. We propose that the "unstructured integration" identified in Phase Ontology finds its technological analogue in optimal quantum architectures. The implications are far-reaching, including the development of novel resonant algorithms, phase processors and coherent networks, alongside new metrics of efficiency like the Phase Coherence Coefficient. This work posits that Quantum Computing is not merely a technological advancement but an active laboratory for an epistemology of becoming and an ontology of potentiality, offering tangible evidence for a working model of reality centered on dynamic coherence and the inherent "improbability" as a generative force. (shrink)

The advent of quantum computing represents a revolutionary leap in computing power, offering solutions to problems previously thought intractable. However, it also introduces new challenges and risks, particularly in the realm of cybersecurity. Quantum computers, with their ability to solve certain computational problems exponentially faster than classical computers, pose a significant threat to current cryptographic systems, which are foundational to modern cybersecurity. Algorithms that currently secure everything from banking transactions to personal communications may be rendered obsolete (...) by quantum computing's ability to break their encryption in a fraction of the time. This paper explores the potential risks that quantum computing presents to cybersecurity and examines emerging mitigation strategies. Specifically, it focuses on the vulnerabilities of public-key cryptography systems such as RSA, ECC (Elliptic Curve Cryptography), and AES (Advanced Encryption Standard), all of which rely on the computational difficulty of certain mathematical problems, such as factoring large numbers or solving discrete logarithms—tasks that quantum computers can perform efficiently through Shor’s algorithm. Additionally, it discusses potential post-quantum cryptography solutions, which involve the development of cryptographic algorithms that are resistant to quantum attacks. Furthermore, the paper evaluates the timeline for quantum computing advancements, the readiness of quantum-safe algorithms, and the actions organizations and governments must take to prepare for this new era. The work emphasizes the need for early adoption of quantum-resistant encryption methods and cross-disciplinary collaboration to develop secure frameworks capable of safeguarding sensitive data in a quantum future. (shrink)

The recent debate on hypercomputation has raised new questions both on the computational abilities of quantum systems and the Church-Turing Thesis role in Physics.We propose here the idea of “effective physical process” as the essentially physical notion of computation. By using the Bohm and Hiley active information concept we analyze the differences between the standard form (quantum gates) and the non-standard one (adiabatic and morphogenetic) of Quantum Computing, and we point out how its Super-Turing potentialities derive (...) from an incomputable information source in accordance with Bell’s constraints. On condition that we give up the formal concept of “universality”, the possibility to realize quantum oracles is reachable. In this way computation is led back to the logic of physical world. (shrink)

High-Performance Computing (HPC) has become a cornerstone for enabling breakthroughs in artificial intelligence (AI) by offering the computational resources necessary to process vast datasets and optimize complex algorithms. As AI models continue to grow in complexity, traditional HPC systems, reliant on central processing units (CPUs), face limitations in scalability, efficiency, and speed. Emerging technologies like quantum computing and specialized hardware such as Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and Field Programmable Gate Arrays (FPGAs) are poised (...) to address these challenges. This research paper explores various HPC techniques used to optimize and accelerate AI algorithms, focusing on quantum computing’s potential for parallelism and specialized hardware's capabilities in delivering faster computation and energy efficiency. It delves into current advancements, comparative analyses of different HPC methods, and the integration of hybrid quantum-classical approaches to further enhance AI optimization. The study also examines the challenges of implementing these technologies at scale, with an eye toward the future of AI acceleration and the role of HPC in maintaining energy efficiency while meeting computational demands. Through this investigation, we aim to provide a comprehensive overview of how quantum computing and specialized hardware are reshaping the landscape of AI, paving the way for more advanced, efficient, and sustainable AI solutions. (shrink)

-/- The Integration of Angelito Malicse’s Universal Formula with Quantum Computer Design, AGI Algorithmic Design, and Education -/- In the pursuit of developing intelligent systems, the realms of quantum computing, artificial general intelligence (AGI), and educational frameworks face the significant challenge of balancing complex feedback mechanisms, ethical decision-making, and system stability. The universal formula developed by Angelito Malicse provides a pioneering approach to understanding free will, human behavior, and decision-making. His three laws, deeply rooted in the concept (...) of natural balance, offer valuable insights into addressing problems related to technological systems, such as quantum computers and AGI. This essay explores how Malicse’s universal formula can be integrated into quantum computer design, AGI algorithmic development, and educational models, emphasizing the importance of feedback loops, balance, and ethical decision-making as critical components in these fields. -/- The Universal Formula: An Overview -/- Angelito Malicse’s universal formula consists of three key laws: -/- 1. The Law of Karma (Cause and Effect): This law posits that every action leads to a consequence. It emphasizes that systems—whether natural or man-made—must function without defects to ensure they operate effectively and safely. Any imbalance or disruption in the system causes dysfunction, whether the system is biological, mechanical, or computational. -/- 2. The Homeostasis Principle: This principle asserts that systems—be they biological, technological, or social—must maintain equilibrium to function correctly. When an imbalance occurs, the system seeks to restore balance. This is a critical process in all functional systems, from the human body to technological networks. -/- 3. The Ethical Decision-Making Law: This law underscores the importance of ethical decision-making within systems. It suggests that choices must promote balance and avoid disrupting the natural harmony of the system. If decisions violate this balance, they result in negative consequences, reflecting the idea that imbalance leads to dysfunction in both societal and technological systems. -/- These principles, while addressing human behavior and societal functioning, also have profound implications for the development of quantum computing and AGI, areas that are critical to future advancements in technology. -/- Quantum Computing and the Universal Formula -/- Quantum computing represents a revolutionary leap in computational power, utilizing quantum mechanics principles—such as superposition, entanglement, and interference—to solve problems in parallel. Quantum systems, however, are highly sensitive to errors and disturbances, which could affect their ability to function reliably. Thus, understanding and applying the universal formula to quantum computer design is essential. -/- Quantum Causal Inference and the Law of Karma -/- Quantum computing is built on probabilistic models where each quantum state evolves based on prior interactions, an approach that mirrors the concept of cause and effect. Quantum systems follow causal inference, meaning that each quantum operation is influenced by the previous one. This aligns directly with the Law of Karma—every operation in a quantum computer has consequences, which can either lead to a stable state or introduce error. If the system is defective or disrupted, it results in instability, just as a defective system in nature or society fails to function correctly. -/- Quantum algorithms, such as those used in quantum machine learning, rely heavily on cause-effect relationships to guide the computation. The delicate balance between quantum states and operations mirrors the need for balance in natural systems: if a quantum system is not free from errors, its performance will degrade, reflecting Malicse’s principle that systems must be free of defects to operate properly. -/- Quantum Neural Networks (QNNs) and Feedback Mechanisms -/- Quantum Neural Networks (QNNs) aim to replicate the neural network structure of the human brain using quantum bits (qubits) and quantum gates. These networks utilize feedback loops, where the system continuously adapts and updates based on prior outputs, enhancing its learning process. This closely aligns with Malicse’s feedback loop principle, which emphasizes the importance of continuous adjustment based on environmental feedback. -/- QNNs utilize quantum feedback to optimize learning. However, just like biological systems, they require feedback that operates in balance, ensuring that each decision made by the system aligns with previous learnings and the larger goal of the system. For a quantum system to achieve reliable performance, the feedback mechanism must be balanced and precise, aligning with Malicse’s view that all systems—whether biological or technological—must act to restore balance when disrupted. -/- Homeostasis in Quantum Systems -/- The ability of quantum systems to maintain homeostasis—to return to a balanced state after a disturbance—is another vital aspect of quantum computing. Quantum error correction algorithms play a central role in ensuring that quantum states do not deviate significantly from their intended positions due to noise or errors. These mechanisms allow quantum systems to maintain internal equilibrium despite external interference, mirroring the concept of homeostasis in biological systems. -/- Malicse’s Homeostasis Principle is directly applicable here: just as biological systems restore balance to ensure survival, quantum systems require constant error-correction and recalibration to maintain balance. This principle ensures that quantum computations are accurate and that imbalances (such as errors or instabilities) are corrected swiftly, maintaining the overall stability of the quantum system. -/- AGI and the Universal Formula -/- The field of AGI seeks to develop machines capable of generalizing knowledge and solving complex problems across multiple domains. As AGI systems evolve, the challenge lies in ensuring they adhere to ethical principles and maintain balance within their decision-making processes. The universal formula offers guidance on how AGI can be structured to make decisions that reflect the principles of natural balance. -/- Feedback Mechanisms and Ethical Decision-Making -/- One of the core challenges of AGI is ensuring that it can make decisions ethically, even when faced with complex, ambiguous situations. AGI systems learn from feedback, adapting their decisions based on both the data they receive and the ethics embedded within their algorithms. The feedback loop in AGI is similar to that of human decision-making—data inputs (environmental feedback) are processed, and decisions are refined to optimize outcomes. -/- Malicse’s Ethical Decision-Making Law can be applied to AGI systems to ensure that their actions promote balance and avoid harm. For example, an AGI system designed for healthcare must balance patient needs with resource availability, ensuring that decisions benefit society while maintaining fairness and equity. If the AGI violates this balance—say, by favoring one group over another—it could result in negative consequences, such as social inequality or resource misallocation. -/- Homeostasis in AGI Systems -/- Much like quantum systems, AGI must strive for homeostasis to maintain long-term operational stability. AGI should be able to detect imbalances in its decision-making process and correct them accordingly. For instance, an AGI operating in a financial system must adjust its strategies based on market feedback, ensuring that it consistently contributes to the stability of the market, without causing disruptive cycles. -/- Integrating Malicse’s Homeostasis Principle in AGI ensures that these systems are capable of continuous adaptation, balancing inputs and outputs in a way that prevents harmful outcomes. In decision-making, this means the AGI will always strive for balance, ensuring that no decision undermines the overall health of the system, whether it’s societal, economic, or environmental. -/- The Role of Education in Promoting Natural Balance -/- Educational systems are fundamental to shaping the leaders and innovators of the future. By incorporating the principles of natural balance, ethical decision-making, and feedback-based learning, we can produce individuals capable of understanding the complexities of quantum systems and AGI. -/- Malicse’s universal formula should be integrated into the educational curriculum at all levels, fostering a generation of thinkers who can balance cause and effect, understand feedback loops, and navigate the ethical challenges of technology. Through this holistic education, students will be better equipped to understand and contribute to the design of future technologies, ensuring that they maintain harmony with natural systems and societal needs. -/- Conclusion -/- Angelito Malicse’s universal formula offers a profound framework for understanding the fundamental principles of decision-making, both in human and computational systems. By integrating these principles into the design of quantum computers and AGI systems, we can create technologies that adhere to the natural laws of balance and ethics, preventing harmful outcomes and ensuring stable, sustainable progress. -/- In the realms of quantum computing and AGI, the application of the Law of Karma, Homeostasis, and Ethical Decision-Making will ensure that these advanced systems work in harmony with their environments, just as natural systems do. Further, by embedding these principles into educational curricula, we can train future generations to harness the power of these technologies in a way that benefits society, promotes fairness, and sustains the natural balance of the universe. -/- By applying Malicse’s universal formula across these domains, we can foster a future where technology and humanity work together in a sustainable, ethical, and balanced way. -/- . (shrink)

This essay proposes a speculative, multi-stage research framework for investigating the possibility of backward time travel through automation of the Gateway Process using advanced neural interfaces, artificial intelligence, and quantum computing. While backward time travel remains unproven and controversial, the framework is presented as a thought experiment integrating neuroscience, brain–computer interfaces, quantum measurement, and the many-worlds interpretation of quantum mechanics. The proposed steps emphasize automation, objectivity, repeatability, and iterative review. Rather than asserting empirical confirmation, the model (...) aims to formalize how such extraordinary claims might be evaluated under controlled, interdisciplinary conditions. (shrink)

Marshall Barnes, an independent research and development engineer, has developed a theoretical framework for time travel that relies on parallel universes and the participatory universe concept, drawing from quantum mechanics. This approach avoids traditional paradoxes by instantiating new parallel copies of past eras through information manipulation rather than direct spacetime traversal. This essay explores how quantum computing could expedite and enhance the effectiveness of Barnes’ proposed time travel machine. By leveraging quantum superposition, entanglement, and simulation capabilities, (...) quantum computers can accelerate the complex calculations required for quantum branching and retrocausality. Theoretical foundations, potential implementation strategies, and implications are discussed, supported by existing quantum technologies and Barnes’ experimental insights. (shrink)

We present a comprehensive investigation into the minimal axioms required to derive quantum structure from classical computation. Through systematic analysis of multiple computational models—SK combinatory logic, reversible logic gates (Toffoli, Fredkin), reversible cellular automata, and lambda calculus—we establish that no form of computation, whether irreversible or reversible, can generate quantum structure. Our main results are: 1. The No-Go Theorem: Reversible n-bit gates are 2^n × 2^n permutation matrices that embed into the classical symplectic group Sp(2·2^n, R), not the (...) unitary group U(2^n). This theorem has been formally verified in Coq. 2. Axiom Identification: Using Generalized Probabilistic Theories (GPTs), we identify A1 (state space extension/superposition) as the unique primitive axiom that cannot be derived from computation. 3. Universality: Results hold across all Turing-complete computation models tested, establishing computation-model independence. These findings definitively answer the question "Can quantum structure be derived from computation?" with: No, without the superposition axiom A1. The gap between classical and quantum is precisely A1—and this gap is mathematically unbridgeable by computation alone. (shrink)

Computable Wavefunction Realism (CWFR) is a finite-information ontology for quantum mechanics that preserves Everettian explanatory structure while rejecting a literal continuum as fundamental ontology. The motivation is semantic: if real-valued quantities are treated as ontic, then finite-precision readouts must denote under a stable interface, and the theory’s operations must be semantically closed on the admissible domain. Continuum ontology violates these requirements in two independent ways. On the domain side, lawful continuum evolution can force dependence on unbounded collections of independent (...) fine-scale contingencies, producing non-denoting magnitudes and loss of closure. On the range side, the continuum admits distinctions among real magnitudes that no bounded-input-dependent procedure can stably track; such distinctions enlarge the ontology without enlarging the space of physically meaningful effects. CWFR enforces semantic stability and finite-information closure via three structural postulates: (I) a Lorentz-invariant spectral band-limit bounding independent ultraviolet degrees of freedom; (II) amplitudes restricted to computable complex numbers under admissible representations; and (III) evolution by finitely specified, oracle-free successor maps that are total on the resulting countable configuration space. Within this setting, decoherence induces stable, finitely witnessable macroscopic predicates that organize histories into a finitely branching epistemic-dynamic refinement structure. Classical alternatives are defined diachronically as refined histories rather than synchronically as components of the instantaneous quantum state, rendering the preferred basis problem ill-posed within the ontology. Interference persists within refinement blocks while branching occurs through the certification of new stability predicates; Born weights are defined over finite refinement partitions, and classical worlds arise as maximal refinement threads. (shrink)

Following our previous work establishing that complex structure does not automatically emerge from SK combinatory logic, we investigate whether reversible computation provides the missing ingredient for quantum structure. Through systematic analysis of four computational models—reversible logic gates (Toffoli, Fredkin), continuous-time quantum walks, reversible cellular automata, and the non-commutativity of SK operators—we establish a hierarchy of quantum-like behaviors: Level 0 (Irreversible): SK computation—classical, deterministic Level 1 (Discrete Reversible): Toffoli/Fredkin gates, RCA—classical, embeddable in Sp(2N,R) where N = 2^n for (...) n-bit gates Level 2 (Continuous-Time): U(t) = exp(-iHt) evolution—interference emerges (note: i is introduced by hand) Level 3 (Quantum): Quantum circuits—superposition, entanglement Our main finding is that reversibility alone is insufficient for quantum behavior: discrete reversible gates on n bits are 2^n × 2^n permutation matrices that embed into the classical symplectic group Sp(2·2^n,R), not the unitary group U(2^n). While continuous-time evolution introduces interference patterns, it does not generate genuine superposition. We conclude that quantum structure requires additional axioms beyond any form of computation, discrete or continuous. (shrink)

The central motivating idea behind the development of this work is the concept of prespace, a hypothetical structure that is postulated by some physicists to underlie the fabric of space or space-time. I consider how such a structure could relate to space and space-time, and the rest of reality as we know it, and the implications of the existence of this structure for quantum theory. Understanding how this structure could relate to space and to the rest of reality requires, (...) I believe, that we consider how space itself relates to reality, and how other so-called "spaces" used in physics relate to reality. In chapter 2, I compare space and space-time to other spaces used in physics, such as configuration space, phase space and Hilbert space. I support what is known as the "property view" of space, opposing both the traditional views of space and space-time, substantivalism and relationism. I argue that all these spaces are property spaces. After examining the relationships of these spaces to causality, I argue that configuration space has, due to its role in quantum mechanics, a special status in the microscopic world similar to the status of position space in the macroscopic world. In chapter 3, prespace itself is considered. One way of approaching this structure is through the comparison of the prespace structure with a computational system, in particular to a cellular automaton, in which space or space-time and all other physical quantities are broken down into discrete units. I suggest that one way open for a prespace metaphysics can be found if physics is made fully discrete in this way. I suggest as a heuristic principle that the physical laws of our world are such that the computational cost of implementing those laws on an arbitrary computational system is minimized, adapting a heuristic principle of this type proposed by Feynman. In chapter 4, some of the ideas of the previous chapters are applied in an examination of the physics and metaphysics of quantum theory. I first discuss the "measurement problem" of quantum mechanics: this problem and its proposed solution are the primary subjects of chapter 4. It turns out that considering how quantum theory could be made fully discrete leads naturally to a suggestion of how standard linear quantum mechanics could be modified to give rise to a solution to the measurement problem. The computational heuristic principle reinforces the same solution. I call the modified quantum mechanics Critical Complexity Quantum Mechanics (CCQM). I compare CCQM with some of the other proposed solutions to the measurement problem, in particular the spontaneous localization model of Ghirardi, Rimini and Weber. Finally, in chapters 5 and 6, I argue that the measure of complexity of quantum mechanical states I introduce in CCQM also provides a new definition of entropy for quantum mechanics, and suggests a solution to the problem of providing an objective foundation for statistical mechanics, thermodynamics, and the arrow of time. (shrink)

In this paper, the issues of computability and constructivity in the mathematics of physics are discussed. The sorts of questions to be addressed are those which might be expressed, roughly, as: Are the mathematical foundations of our current theories unavoidably non-constructive: or, Are the laws of physics computable?

Standard Everettian quantum mechanics is formulated on a continuum Hilbert space with unbounded spectral support and arbitrary real or complex amplitudes. Under explanatory realism, such a continuum ontology is semantically unstable. A realist theory requires that physical magnitudes denote under admissible semantics and that evolution be closed on its ontic domain. Continuum ontology violates these requirements in two ways: continuum dynamics can force domain non-closure through unbounded aggregation of fine-scale contingencies, and continuum magnitudes permit distinctions whose resolution would itself (...) require unbounded input dependence, rendering that structure explanatorily inert. We develop Computable Wavefunction Realism (CWFR), a finite-information Everettian ontology that enforces semantic stability through three postulates: (I) a Lorentz-invariant spectral band-limit; (II) Type-2 computable amplitudes, with physical predicates restricted to strict inequalities admitting finite witnesses; and (III) evolution by finitely specified, oracle-free successor maps that are total on the admissible domain. These constraints define a countable configuration space and restrict physical law to semantically total dynamics. Within this setting, macroscopic structure is organized by stability predicates and successor-driven refinement. Classical worlds correspond to refinement threads rather than components of an instantaneous state decomposition, and branching is therefore diachronic rather than basis-dependent, rendering the preferred basis problem ill-posed within the ontology. Decoherence yields stable macroscopic distinctions, spacetime structure emerges from correlational geometry, and Born weights are assigned over finite partitions, preserving the empirical content of quantum theory. We construct explicit admissible dynamics, including interacting, Poincaré-equivariant successor maps, and show that interaction, decoherence, and genuine Everettian branching occur within the finite-information domain. (shrink)

Quantum mechanics and general relativity provide incompatible descriptions of time. In QM, states evolve unitarily with respect to an external time parameter; in GR, time is a coordinate in a dynamical spacetime geometry. We propose that both quantum and relativistic time emerge from a pre-geometric structure we call Prototime (PT): represented by an orthomodular lattice of consistency relations with zero von Neumann entropy at the fundamental level. PT has no background time, space, or metric—only algebraic relations of compatibility (...) and orthogonality. We formalize PT using an orthomodular lattice Lequipped with a foliation functor F: I →Ctx(L) that maps orderedi ndices to Boolean sublattices (classical contexts). A spectral redundancy functional Φs measures coherence preservation across embeddings. We show that: 1. Stable foliations satisfying Piron-Solèr conditions admit Hilbert-space representa- tions, recovering standard QM with Schrödinger dynamics via Stone’s theorem; 2. The partial order on decoherence events across foliations, together with a natural volume measure, reconstructs Lorentzian spacetime structure; 3. Time—both quantum (parameter along unitary flows) and relativistic (causal or- der)—supervenes on Φs-monotone decoherence gradients. Critically, we provide a computationally tractable implementation of Φs using spectral graph theory on mutual information networks (Bailey and Schneider, forthcoming). Testing this on simulated multi-agent systems—including combinatorial threshold-linear networks (CTLNs) that model graph-constrained neural dynamics—we demonstrate that Φs exhibits characteristic signatures during basin formation, attractor convergence, and coherence-to-classicality transitions. These results provide the first quantitative bridge between abstract PT formalism and testable physical predictions. We identify concrete empirical tests via quantum biology (photosynthetic complexes), quantum decoherence exper- iments, and neural ensemble dynamics. We also resolve the Wheeler delayed-choice experiment within PT, where apparent retrocausality dissolves because decoherence times vary across system components, creating asymmetric basin formation that only appears backward-causal when projected onto classical time. PT thus provides a kine- matical unification of QM and GR with testable predictions, offering a pre-geometric foundation for both fundamental physics and, potentially, theories of consciousness grounded in informational irreducibility. -/- (This piece is the formalism for our “Superpsychism” piece for our forthcoming target paper at The Journal of Consciousness Studies and our paper on Spectral Phi. Comments are very welcome.). (shrink)

The problem of emergence in physical theories makes necessary to build a general theory of the relationships between the observed system and the observing system. It can be shown that there exists a correspondence between classical systems and computational dynamics according to the Shannon-Turing model. A classical system is an informational closed system with respect to the observer; this characterizes the emergent processes in classical physics as phenomenological emergence. In quantum systems, the analysis based on the computation theory fails. (...) It is here shown that a quantum system is an informational open system with respect to the observer and able to exhibit processes of observational, radical emergence. Finally, we take into consideration the role of computation in describing the physical world. (shrink)

Practical quantum computing devices and their applications to AI in particular are presently mostly speculative. Nevertheless, questions about whether this future technology, if achieved, presents any special ethical issues are beginning to take shape. As with any novel technology, one can be reasonably confident that the challenges presented by "quantum AI" will be a mixture of something new and something old. Other commentators (Sevilla & Moreno 2019), have emphasized continuity, arguing that quantum computing does not (...) substantially affect approaches to value alignment methods for AI, although they allow that further questions arise concerning governance and verification of quantum AI applications. In this brief paper, we turn our attention to the problem of identifying as-yet-unknown discontinuities that might result from quantum AI applications. Wise development, introduction, and use of any new technology depends on successfully anticipating new modes of failure for that technology. This requires rigorous efforts to break systems in protected sandboxes, and it must be conducted at all stages of technology design, development, and deployment. Such testing must also be informed by technical expertise but cannot be left solely to experts in the technology because of the history of failures to predict how non-experts will use or adapt to new technologies. This interplay between experts and non-experts may be particularly acute for quantum AI because quantum mechanics is notoriously difficult to understand. (As Richard Feynman quipped, "Anyone who claims to understand quantum mechanics is either lying or crazy.") We will discuss the extent to which the difficulties in understanding the physics underlying quantum computing challenges attempts to anticipate new failure modes that might be introduced in AI applications intended for unsupervised operation in the public sphere. (shrink)

With the emergence of quantum technologies such as quantum computing, quantum communications, and quantum sensing, new potential has emerged for smart manufacturing and Industry 4.0. These technologies, however, present ethical concerns that must be addressed in order to ensure they are developed and used responsibly. This article outlines some of the ethical challenges that quantum technologies may raise for Industry 4.0 and presents the value sensitive design methodology as a strategy for ethics-by-design of (...) class='Hi'>quantum computing in Industry 4.0. This research further investigates the potential ethical difficulties that may come from the use of quantum technologies in Industry 4.0, such as concerns about privacy, security, accountability, and the influence of these technologies on workers and society as a whole. The article argues, based on current literature, that these issues necessitate a proactive and comprehensive approach to ethics-by-design. (shrink)

The Computational Model of Mind (CMM) conceptualizes cognition as computational processes, modeling mental operations through algorithmic manipulations of symbolic or distributed representations. This framework bridges psychology, neuroscience, philosophy, and computer science, providing a unified lens for understanding the mind. Its symbiotic relationship with artificial intelligence (AI) has accelerated advances in cognitive science and the development of intelligent systems, from neural networks to autonomous agents. This article offers a comprehensive analysis of CMM, tracing its historical evolution from Turing's foundational ideas to (...) modern deep learning paradigms. It delineates CMM's theoretical underpinnings, operational mechanisms, and strengths in simulating complex cognitive functions, while critically addressing limitations, such as its struggles with consciousness, embodiment, and ecological validity. The paper explores CMM's intersections with AI, highlighting applications in cognitive modeling, natural language processing, robotics, and neurotechnology. It also examines ethical challenges, including algorithmic bias, privacy concerns, and the implications of machine autonomy. Looking ahead, I propose future directions, emphasizing interdisciplinary collaboration, neurobiologically inspired algorithms, and emerging technologies like quantum computing. This synthesis affirms CMM's transformative role in unraveling the nature of mind and machine intelligence, and an innovative research at the nexus of cognition and technology. (shrink)

The viewpoint that consciousness, including feeling, could be fully expressed by a computational device is known as strong artificial intelligence or strong AI. Here I offer a defense of strong AI based on machine-state functionalism at the quantum level, or quantum-state functionalism. I consider arguments against strong AI, then summarize some counterarguments I find compelling, including Torkel Franzén’s work which challenges Roger Penrose’s claim, based on Gödel incompleteness, that mathematicians have nonalgorithmic levels of “certainty.” Some consequences of strong (...) AI are then considered. A resolution is offered of some problems including John Searle’s Chinese Room problem and the problem of consciousness propagation under isomorphism. (shrink)