In order to address the central question at the heart of the concluding debate at the AAAI 07 Spring quantum interaction symposium, where it was proposed that other probabilistic and nondeterministic theories might replace quantum mechanics, even in relation to quantum physics, and in further explanation of our AAAI Spring 2007 paper ‘How Intelligence Evolved?’, we outline the derivation from first principles of what we call the universal rewrite system. For, once derived, this system has all the appearances of being nature’s semantic computational machine order code, readily identifiable with a new version of quantum mechanics, and powerful predictive capabilities. These, fully detailed in the book Zero to Infinity, now published by World Scientific (Rowlands, 2007), especially in chapter 20, which is a joint contribution by the authors of this paper, are in total agreement with experiment so far, in particular in elementary particle physics, magnetic nuclear spectroscopy and MRI, and the DNA / RNA genetic code molecular biology. Indeed, in the earlier paper, a further identifiable version of this universal rewrite system in recognition of its descriptive quantum physical nature, is that of Dirac’s bra / ket quantum mechanical formalism, where bra and ket will serve as the two creation and conserve universal rewrite system rules / productions, so showing it to describe, in the case of bosons, a Bargmann-Fock quantum field of creation / annihilation operators. It is our view, therefore, that this quantum physical universal computational rewrite system, where the nilpotent generalization of Dirac’s famous quantum mechanical equation specifies the computational machine order code, describes the quantum physical system of process at the heart of nature, and that recognition of this is a necessary step in creating an artificial intelligence which has semantic, as well as syntactic features.
In order to address the central question at the heart of the concluding debate at the AAAI 07 Spring quantum interaction symposium, where it was proposed that other probabilistic and nondeterministic theories might replace quantum mechanics, even in relation to quantum physics, and in further explanation of our AAAI Spring 2007 paper ‘How Intelligence Evolved?’, we outline the derivation from first principles of what we call the universal rewrite system. For, once derived, this system has all the appearances of being nature’s semantic computational machine order code, readily identifiable with a new version of quantum mechanics, and powerful predictive capabilities. These, fully detailed in the book Zero to Infinity, now published by World Scientific (Rowlands, 2007), especially in chapter 20, which is a joint contribution by the authors of this paper, are in total agreement with experiment so far, in particular in elementary particle physics, magnetic nuclear spectroscopy and MRI, and the DNA / RNA genetic code molecular biology. Indeed, in the earlier paper, a further identifiable version of this universal rewrite system in recognition of its descriptive quantum physical nature, is that of Dirac’s bra / ket quantum mechanical formalism, where bra and ket will serve as the two creation and conserve universal rewrite system rules / productions, so showing it to describe, in the case of bosons, a Bargmann-Fock quantum field of creation / annihilation operators. It is our view, therefore, that this quantum physical universal computational rewrite system, where the nilpotent generalization of Dirac’s famous quantum mechanical equation specifies the computational machine order code, describes the quantum physical system of process at the heart of nature, and that recognition of this is a necessary step in creating an artificial intelligence which has semantic, as well as syntactic features.
Further evidence is presented in favour of the computational paradigm, conceived and constructed by Rowlands and Diaz, as detailed in Rowlands’ book Zero to Infinity (2007) [2], and in particular the authors’ paper ‘The Grammatical Universe: the Laws of Thermodynamics and Quantum Entanglement’ [1]. The paradigm, which has isomorphic group and algebraic quantum mechanical language interpretations, not only predicts the well‐established facts of quantum physics, the periodic table, chemistry / valence and of molecular biology, whose understanding it extends; it also provides an elegant, simple solution to the unresolved quantum measurement problem. In this fundamental paradigm, all the computational constructs / predictions that emerge, follow from the simple fact, that, as in quantum mechanics, the wave function is defined only up to an arbitrary fixed phase. This fixed phase provides a simple physical understanding of the quantum vacuum in quantum field theory, where only relative phases, known to be able to encode 3+1 relativistic space‐time geometries, can be measured. It is the arbitrary fixed measurement standard, against which everything that follows is to be measured, even though the standard itself cannot be, since nothing exists against which to measure it. The standard, as an arbitrary fixed reference phase, functions as the holographic basis for a self‐organized universal quantum process of emergent novel fermion states of matter where, following each emergence, the arbitrary standard is re‐fixed anew so as to provide a complete history / holographic record or hologram of the current fixed past, advancing an unending irreversible evolution, such as is the evidence of our senses. The fermion states, in accord with the Pauli exclusion principle, each correspond to a unique nilpotent symbol in the infinite alphabet (which specifies the grammar in this nilpotent universal computational rewrite system (NUCRS) paradigm); and the alphabet, as Hill and Rowlands hypothesize on substantial evidence [26], includes that of the RNA / DNA genetic code and, as holographic phase encodings / holograms, the 4D geometries of all living systems as self‐organised grammatical computational rewrite machines / machinery. Human brains, natural grammatical (written symbol) languages, 4D geometric self‐awareness and a totally new emergent property of matter, human consciousness, can thus with some measure of confidence be postulated as further genetic consequences which follow from this self‐organizing fundamental rewrite NUCRS construction. For it, like natural language, possesses a semantics and not just a syntax, where the initial symbol, i.e. the arbitrary fixed phase measurement standard, is able to function as the template for the blueprints of the emergent 4D relativistic real and virtual geometries to come, in a ‘from the Self Creation to the creation of the human self’ computational rewrite process evolution.
Information is a part of the physical universe as matter and energy. Today first small quantum computers begin to appear. A quantum computer represents information by qubits. In quantum computation there are two principles (algorithms) that speed up the computation, Fourier Transform and Grover's amplification algorithm. Quantum artificial intelligence is introduced by linear algebra-based quantum machine learning and symbolical quantum artificial intelligence. The initial motivation for relating quantum theory to consciousness was related to free free will in a deterministic world, since the quantum world is nondeterministic. The dualism of mind and matter involves the unresolved problem of how the mind and brain could interact. The relationship of union between the mind and the body of a person is resolved by quantum measurement. Sir Roger Penrose argues that elementary acts of consciousness cannot be described by a Turing machine. Such non-countable elementary acts of conscious ness are only present in the wave function collapse (measurement). Penrose assumes that the wave function collapse is not random, it represents an objective reduction.
This study seeks to establish a consistent theoretical foundation to address enduring open questions in physics—such as the origins of life, free will, the subjective experience of time, consciousness, and biological intelligence—by exploring the connection between the stochastic quantum hydrodynamic model (SQHM) and the order generation in systems far from thermodynamic equilibrium, all converging in the manifestation of physical reality. It offers novel insights into the emergence of order, biological systems, and associated functions such as biological intelligence, free will, consciousness, and the behavior of social structures. These insights are grounded in the assumption of a discrete spacetime structure, enabling an analogy of the universe as a running discrete computation, where emergent physical laws arise from the computation’s intrinsic problem-solving goals. This perspective carries profound implications for physical evolution, suggesting that everyday reality, the origin of life, social interactions, and consciousness itself are intrinsic features of the universal physics. It introduces the idea that free will may emerge as a functional mechanism guiding the universe’s progression toward increasingly efficient and organized states, states in which order is preserved to the greatest extent possible. This view embodies a form of bounded probabilism, standing in sharp contrast to the concept of total, unconstrained randomness, which reduces the universe to a mere cosmic game of dice. It also offers a novel perspective through which artificial intelligence can be framed and its limitations, as well as its differences from biological intelligence, can be better understood. This view highlights how the quantum foundations of the universe contribute to the expression of consciousness, outlining potential avenues for advancing AI toward a more faithful emulation of conscious experience.
The simulation of quantum matter with classical hardware plays a central role in the discovery and development of quantum many-body systems, with far-reaching implications in condensed matter physics and quantum technologies. In general, efficient and sophisticated algorithms are required to overcome the severe challenge posed by the exponential scaling of the Hilbert space of quantum systems. In contrast, hardware built with quantum bits of information are inherently capable of efficiently finding solutions of quantum many-body problems. While a universal and scalable quantum computer is still beyond the horizon, recent advances in qubit manufacturing and coherent control of synthetic quantum matter are leading to a new generation of intermediate scale quantum hardware. \n \nThe complexity underlying quantum many-body systems closely resembles the one encountered in many problems in the world of information and technology. In both contexts, the complexity stems from a large number of interacting degrees of freedom. A powerful strategy in the latter scenario is machine learning, a subfield of artificial intelligence where large amounts of data are used to extract relevant features and patterns. In particular, artificial neural networks have been demonstrated to be capable of discovering low-dimensional representations of complex objects from high-dimensional dataset, leading to the profound technological revolution we all witness in our daily life. \n \nIn this Thesis, we envision a new paradigm for scientific discovery in quantum physics. On the one hand, we have the essentially unlimited data generated with the increasing amount of highly controllable quantum hardware. On the other hand, we have a set of powerful algorithms that efficiently capture non-trivial correlations from high-dimensional data. Therefore, we fully embrace this data-driven approach to quantum mechanics, and anticipate new exciting possibilities in the field of quantum many-body physics and quantum information science. We revive a powerful stochastic neural network called a restricted Boltzmann machine, which slowly moved out of fashion after playing a central role in the machine learning revolution of the early 2010s. We introduce a neural-network representation of quantum states based on this generative model. We propose a set of algorithms to reconstruct unknown quantum states from measurement data and numerically demonstrate their potential, with important implications for current experiments. These include the reconstruction of experimentally inaccessible properties, such as entanglement, and diagnostics to determine sources of noise. Furthermore, we introduce a machine learning framework for quantum error correction, where a neural network learns the best decoding strategy directly from data. We expect that the full integration between quantum hardware and artificial intelligence will become the gold standard, and will drive the world into the era of fault-tolerant quantum computing and large-scale quantum simulations.
An outlined subquantum theory that might be considered a possible extension of Bohmian mechanics is further discussed. In this theory a fundamental physical system is modelled as a particle endowed with a methodological probabilistic classical Turing machine, which characterizes systems as data processing devices, making of information a crucial physical concept in the determination of the system dynamics. The main idea of this theory resides in proposing a Darwinian evolutionary mechanism—therefore based on natural selection—as the central element that would determine the emergence of quantum mechanics from an evolutionarily stable strategy (ESS) whose essentials are supposedly captured in three regulating principles. The Darwinian character of these regulating principles is explored.
Unique in its field, this book uses a methodology that is entirely new, creating the simplest and most abstract foundations for physics to date. The author proposes a fundamental description of process in a universal computational rewrite system, leading to an irreducible form of relativistic quantum mechanics from a single operator. This is not only simpler, and more fundamental, but also seemingly more powerful than any other quantum mechanics formalism available. The methodology finds immediate applications in particle physics, theoretical physics and theoretical computing. In addition, tak