Title: Quantum State Collapse as a Directed Evolution Algorithm

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum state collapse is a fundamental process in quantum physics, representing the transition from a definite state to a probabilistic state. This paper proposes a novel evolutionary algorithm, leveraging this collapse mechanism, to guide the refinement of quantum state designs, potentially uncovering novel, highly efficient quantum states. The algorithm employs a quantum process to iteratively "collapse" the state of a quantum system, subtly altering its properties based on a guided evolutionary process. This approach offers a unique pathway to discovering new and optimized quantum states, moving beyond traditional optimization techniques. The core mechanism centers around a quantum evolution process where the algorithm iteratively adjusts parameters to influence the collapse, leading to a refined quantum state.

DOI: 10.5281/zenodo.22525672

Related papers

Title: Quantum State Collapse as a Recursive Algorithm for Optimization

Jincheng Zhang

Quantum State Collapse as a Recursive Algorithm for Optimization Quantum State Collapse, a phenomenon described by quantum mechanics, represents a dramatic transition where a system loses its coherence and becomes in a probabilistic state. This concept has gained increasing attention in fields like information theory and quantum computing, but its direct application to optimization problems, particularly complex systems, remains largely unexplored. This paper proposes a novel algorithm inspired by quantum state collapse, iteratively refining a system's state to achieve a desired objective, utilizing a feedback loop based on a probability distribution. We hypothesize that this approach, by mimicking the collapse process, can uncover solutions that are difficult to reach through traditional optimization methods. The algorithm employs a recursive process, wherein the system's state is updated based on a probability distribution representing the potential solutions. This distribution is dynamically adjusted based on the system's current state, encouraging exploration and refinement. We demonstrate the efficacy of this approach through a series of numerical simulations, showcasing its potential to outperform existing optimization techniques in certain benchmark problems. This work explores the possibility of leveraging the unique properties of quantum mechanics to address complex optimization challenges.

Sep 1, 20260 citations

Title: Recursive Quantum State Collapse with Adaptive Thresholds

Jincheng Zhang

Abstract: Quantum state collapse is a fundamental process in quantum mechanics, where a system transitions from a superposition of states to a definite state. This paper explores a novel recursive approach to this process, extending the concept of collapse to a continuous refinement of a system's state. We introduce a mechanism for calculating an "effective" collapse threshold based on the system's internal dynamics and dynamically adjusts the collapse rate to optimize the state's evolution. This design offers a departure from traditional static collapse theories, potentially leading to a more robust and dynamic understanding of quantum systems. The paper details the core mechanism, its mathematical underpinnings, and presents preliminary results demonstrating the effectiveness of this approach in a simplified quantum simulation.

Sep 5, 20260 citations

Title: Quantum State Collapse as a Computational Tool

Jincheng Zhang

Quantum state collapse, a fundamental process in quantum mechanics, presents a unique challenge and opportunity for computational science. This paper explores the potential of utilizing quantum state collapse as a computational tool to efficiently analyze complex systems, specifically focusing on identifying critical points and transitions. We propose a novel methodology centered around applying quantum measurements to 'collapse' a system's state, followed by rigorous analysis of the resulting states to determine system properties and behavior. The core mechanism involves systematically manipulating quantum states to induce collapse, creating a controlled environment for investigation. This approach offers a potentially transformative method for complex system modeling and analysis, moving beyond traditional computational methods and directly leveraging the inherent nature of quantum mechanics. The study will detail the framework, initial results, and potential future directions for this computational technique.

Aug 31, 20260 citations

Title: Quantum State Collapse with Feedback Loops

Jincheng Zhang

Quantum state collapse is a fundamental process in quantum mechanics, describing the irreversible transition from superposition to a definite state. This phenomenon, while seemingly random, is intimately linked to the observer and the very nature of reality. This paper explores a novel quantum state collapse protocol utilizing feedback loops, aiming to dynamically adjust collapse probabilities based on the observed system states. The core mechanism involves analyzing the collapsing process itself, adjusting the collapse probability to maintain a desired state or achieve a specific outcome, offering a potential pathway to controlled manipulation of quantum collapse. Current collapse protocols often rely on reactive observation, hindering precise control. This research proposes a fundamentally different approach, leveraging feedback to guide the collapse process towards a predetermined state or outcome, potentially offering a more insightful and controllable mechanism for quantum phenomena. The paper details the algorithm's architecture, demonstrates its effectiveness through simulated examples, and discusses future research directions.

Sep 7, 20260 citations

Title: Scale-Invariant Quantum State Collapse Dynamics

Jincheng Zhang

Quantum state collapse is a fundamental process in quantum mechanics, representing the irreversible transition from a superposition of states to a definite state. This paper explores a novel model for this dynamic process, incorporating the observer's influence and the environment, aiming to quantify the 'scale-invariant' nature of collapse. We propose a method to analyze and predict collapse probabilities across various scales, offering a significant advancement beyond traditional collapse models. The core mechanism involves a dynamic state evolution equation that accounts for both the quantum system's inherent properties and external perturbations. This approach offers a pathway towards more robust and predictable quantum computations.

Sep 1, 20260 citations

Title: Dynamic Topology Optimization via Quantum Topology

Jincheng Zhang

Quantum topology offers a novel approach to algorithm optimization, leveraging the unique properties of quantum geometry to dynamically adjust circuit topologies. This paper introduces a dynamic topology optimization algorithm based on quantum principles, aiming to enhance the performance of quantum algorithms. We present a framework that utilizes quantum circuits to evolve and refine their topology, resulting in optimized solutions. The core mechanism centers around the application of quantum-induced topology changes to guide the evolution of quantum states, ultimately improving the accuracy and efficiency of quantum computations. The algorithm's design focuses on maximizing the convergence speed and minimizing computational resources, addressing limitations of traditional optimization techniques. This research explores the potential of quantum topology to revolutionize the design and implementation of quantum algorithms.

Aug 31, 20260 citations