arXiv subjects

Kamran Majid

Publications and source records attributed to Kamran Majid.

3 recordsLinked to original sources

arXiv2025

The Metaphysics of Protection: Emergence, Agency, and the Ontological Status of Logical Qubits

This paper argues that the practice of fault-tolerant quantum computation, specifically the mechanism of Quantum Error Correction (QEC), offers a profoundly new lens through which to examine foundational questions of ontology, emergence, and interpretation. We move beyond the standard debate on quantum speedup to ask: What is the nature of the entity--the logical qubit--that is being protected, and what does the active, goal-directed process of its protection reveal about physical reality? We argue that the logical qubit presents a unique case study in the metaphysics of identity, functioning as a quantifiable "Ship of Theseus" in Hilbert space. We introduce the concept of "engineered emergence" to describe the active, information-driven stabilization of the logical qubit, distinguishing it from passive forms of emergence and positioning it as a new category of causal structure. Finally, we demonstrate that the logical qubit serves as a powerful new testbed for major interpretations of quantum mechanics (including agent-centered, Many-Worlds, and Bohmian views), revealing novel strengths and challenges for each. We conclude that the technological imperative of fault-tolerance is not merely an engineering problem but a catalyst for deep philosophical insight, transforming abstract debates into concrete physical questions.

physics.hist-ph↗

arXiv2025

Deep Neural Network Emulation of the Quantum-Classical Transition via Learned Wigner Function Dynamics

The emergence of classical behavior from quantum mechanics as Planck's constant $\hbar$ approaches zero remains a fundamental challenge in physics [1-3]. This paper introduces a novel approach employing deep neural networks to directly learn the dynamical mapping from initial quantum state parameters (for Gaussian wave packets of the one-dimensional harmonic oscillator) and $\hbar$ to the parameters of the time-evolved Wigner function in phase space [4-6]. A comprehensive dataset of analytically derived time-evolved Wigner functions was generated, and a deep feedforward neural network with an enhanced architecture was successfully trained for this prediction task, achieving a final training loss of ~ 0.0390. The network demonstrates a significant and previously unrealized ability to accurately capture the underlying mapping of the Wigner function dynamics. This allows for a direct emulation of the quantum-classical transition by predicting the evolution of phase-space distributions as $\hbar$ is systematically varied. The implications of these findings for providing a new computational lens on the emergence of classicality are discussed, highlighting the potential of this direct phase-space learning approach for studying fundamental aspects of quantum mechanics. This work presents a significant advancement beyond previous efforts that focused on learning observable mappings [7], offering a direct route via the phase-space representation.

quant-ph↗

arXiv2025

Neural Network Emulation of the Classical Limit in Quantum Systems via Learned Observable Mappings

The classical limit of quantum mechanics, formally investigated through frameworks like strict deformation quantization, remains a profound area of inquiry in the philosophy of physics. This paper explores a computational approach employing a neural network to emulate the emergence of classical behavior from the quantum harmonic oscillator as Planck's constant $\hbar$ approaches zero. We develop and train a neural network architecture to learn the mapping from initial expectation values and $\hbar$ to the time evolution of the expectation value of position. By analyzing the network's predictions across different regimes of hbar, we aim to provide computational insights into the nature of the quantum-classical transition. This work demonstrates the potential of machine learning as a complementary tool for exploring foundational questions in quantum mechanics and its classical limit.

quant-ph↗