This work introduces Algorithmic Idealism a framework that reinterprets quantum mechanics as a computational process governed by algorithmic probability informational simplicity and utility optimization Reality is modeled as an agentenvironment feedback loop where agents maximize utility by predicting selfstate transitions Core quantum phenomena such as measurement entanglement and probabilities are explained through informational constructs The paper presents a set of quantum mechanics postulates inspired by this framework and explains their mathematical meaning in detail These postulates include measurement as Bayesian updating entanglement as joint utility optimization and quantum probabilities as utilityweighted predictions Physical laws are shown to emerge as informational regularities from algorithmic constraints By defining identity through decisionmaking consistency and treating simulated and base realities as indistinguishable the framework also resolves philosophical questions about identity and reality This approach unifies quantum mechanics with computational theory offering a novel perspective on foundational physics and its connection to information and computation
This work introduces Algorithmic Idealism a framework that reinterprets quantum mechanics as a computational process governed by algorithmic probability informational simplicity and utility optimization Reality is modeled as an agentenvironment feedback loop where agents maximize utility by predicting selfstate transitions Core quantum phenomena such as measurement entanglement and probabilities are explained through informational constructs The paper presents a set of quantum mechanics postulates inspired by this framework and explains their mathematical meaning in detail These postulates include measurement as Bayesian updating entanglement as joint utility optimization and quantum probabilities as utilityweighted predictions Physical laws are shown to emerge as informational regularities from algorithmic constraints By defining identity through decisionmaking consistency and treating simulated and base realities as indistinguishable the framework also resolves philosophical questions about identity and reality This approach unifies quantum mechanics with computational theory offering a novel perspective on foundational physics and its connection to information and computation
Algorithmic idealism represents a transformative approach to understanding reality, emphasizing the informational structure of self-states and their algorithmic transitions over traditional notions of an external, objective universe. Rooted in algorithmic information theory, it redefines reality as a sequence of self-state transitions governed by principles such as Solomonoff induction. This framework offers a unified solution to longstanding challenges in quantum mechanics, cosmology, and metaphysics, addressing issues like the measurement problem, the Boltzmann brain paradox, and the simulation hypothesis. Algorithmic idealism shifts the focus from describing an independent external world to understanding first-person experiences, providing epistemic interpretations of physical theories and dissolving metaphysical divides between "real" and simulated realities. Beyond resolving these conceptual challenges, it raises profound ethical questions regarding the continuity, duplication, and termination of informational entities, reshaping discussions on identity, consciousness, and existence in the digital and quantum age. By offering a mathematically rigorous yet philosophically innovative framework, algorithmic idealism invites a rethinking of reality as an emergent property of informational dynamics rather than a static external construct.
This work presents a comprehensive and rigorous examination of quantum computing, spanning its mathematical foundations, physical principles, algorithmic developments, hardware architectures, and philosophical implications. Beginning with linear algebra over Hilbert spaces and the postulates of quantum mechanics, the manuscript develops the formal structure of quantum information theory, including qubits, entanglement, quantum channels, and noise models. The work provides in-depth treatments of the quantum circuit model, alternative computation paradigms such as measurement-based, adiabatic, and topological quantum computation, and the theoretical framework of quantum error correction, including stabilizer and surface codes. Foundational and modern quantum algorithms are analysed in detail, including Shor’s factoring algorithm, Grover’s search, variational quantum eigensolvers, QAOA, and emerging quantum machine learning approaches. Beyond algorithms and implementations, the manuscript addresses scalability challenges, fault tolerance, computational complexity limits, and the implications of quantum computing for cryptography and the extended Church–Turing thesis. The concluding sections explore philosophical questions concerning interpretation, measurement, and the relationship between computation and physical reality. This work is intended as both a reference text and a research-level synthesis for advanced students, researchers, and practitioners in quantum computing, theoretical computer science, and quantum physics.
Quantum state collapse is a fundamental aspect of quantum mechanics, often described as a spontaneous transition from superposition to a definite state. This paper explores the potential of computational modeling as a framework to understand this phenomenon, suggesting that collapse is not merely a discrete event but a complex, iterative process driven by a "collapse operator." We propose a detailed model incorporating feedback loops and entanglement, aiming to simulate the probabilistic evolution of quantum states. This research seeks to provide a novel approach to investigating collapse, potentially offering insights into the emergence of quantum phenomena and offering a new perspective on the nature of reality.
[Author’s Note (April 2026)]Supersession Notice: This 2025 paper explores the early KB-Flux framework. It has been structurally overhauled and formally superseded by “The Light & Flux (L&F) Framework (2026)”. While the 2025 version relies on cognitive metaphors—treating the wave function as a “generative model” —the 2026 L&F framework provides a rigorous mathematical proof deriving complex Hilbert space from finite-inference constraints. It replaces “phenomenal grammar” with the Simplex Projection (𐍀), grounded in metabolic and thermodynamic mandates. Researchers seeking the formal Non-Derivability Principle and a stratified three-tier ontology should refer to the 2026 version. Original AbstractReaders are advised to treat the present paper strictly as a historical record of the framework’s initial development. For the current, accurate formulation, please refer to the new publication.The measurement problem—why quantum superpositions yield definite outcomes upon observation—has persisted as the central interpretative challenge of quantum mechanics for nearly a century. This paper proposes that the problem dissolves when viewed through the joint lens of Kantian epistemology and predictive processing (Bayesian brain) theory. Drawing on Kant’s distinction between phenomena (experience) and noumena (reality-in-itself), we argue that the quantum wave function (Ψ) does not represent a physical field in the noumenal world, but rather the generative model of a prediction-generating cognitive agent. In this framework, "superposition" represents a state of uninitialized probabilistic expectation (Latent Space), while "collapse" is reinterpreted as a Bayesian belief update—the discrete transition from a probability distribution to a definite value, necessitated by the information-processing constraints of the observer. By treating the observer not as a passive witness but as an active inference engine, this framework: Naturalizes the collapse postulate: Measurement is the update of an internal model, not a physical discontinuity in nature. Resolves nested-observer paradoxes: Wigner's Friend is resolved by treating reality as a distributed system with eventual consistency, where distinct agents hold distinct local cache states. Grounds the Born Rule: We argue that the squared-amplitude probability rule (P=∣Ψ∣2) serves as a transcendental constraint (akin to Gleason’s Theorem) required for the internal consistency of any probabilistic agent. This interpretation suggests that the "weirdness" of quantum mechanics reflects the necessary architectural constraints of any finite mind attempting to model an inaccessible reality, effectively treating physics as the "User Interface" of reality.
Abstract I argue for an approach to the Foundations of Physics that puts the question in the title center stage, rather than asking “what is the case in the world?”. This approach, algorithmic idealism, attempts to give a mathematically rigorous in-principle -answer to this question both in the usual empirical regime of physics and in some more exotic regimes within cosmology, philosophy, and science fiction (but soon perhaps real) technology. I begin by arguing that quantum theory, in its actual practice and in some interpretations, should be understood as telling an agent what they should expect to observe next (rather than what is the case), and that the difficulty of answering this former question from the usual “external” perspective is at the heart of persistent enigmas such as the Boltzmann brain problem, extended Wigner’s friend scenarios, Parfit’s teletransportation paradox, or our understanding of the simulation hypothesis. Algorithmic idealism is a conceptual framework, based on two postulates that admit several possible mathematical formalizations, cast in the language of algorithmic information theory. Here I give a non-technical description of this view and show how it dissolves the aforementioned enigmas: for example, it claims that you should never bet on being a Boltzmann brain regardless of how many there are, that shutting down computer simulations does not generally terminate its inhabitants, and it predicts the apparent embedding into an objective external world as an approximate description.
We propose the Generative Reality Hypothesis (GRH), a novel interpretation of quantum mechanics that reconceptualizes physical reality as an information-processing system analogous to generative artificial intelligence models. Under this framework, quantum superposition represents the latent space of possible states, wave function collapse corresponds to sampling from probability distributions during information queries (measurements), and the Born rule emerges as the fundamental sampling algorithm of reality. We present rigorous mathematical foundations establishing formal equivalences between quantum dynamics and specific generative models. Our framework recasts the measurement problem as a question of computational process, explains non-local correlations as the enforcement of consistency within a shared generative model, and provides a mechanistic explanation for wave-particle duality as a transition between two computational modes: continuous latent evolution (wave) and discrete sampling (particle). We propose five specific, falsifiable experimental tests using current quantum technologies that can distinguish GRH from standard quantum mechanics through quantitative predictions about complexity-dependent decoherence, measurement dynamics, and information flow. Statistical criteria for falsification are established with required experimental precision specified. The framework connects quantum mechanics to information theory and computational complexity, offering a computationally grounded foundation for quantum phenomena while making testable predictions that differ measurably from existing interpretations.