Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2312.13853 · IEEE Computer Graphics and Applications

Quantum Wave Function Collapse for Procedural Content Generation

Raoul Heese

Published Aug 22, 2024• 4 citations
arXivHTMLPDF

Abstract

Quantum computers exhibit an inherent randomness, so it seems natural to consider them for procedural content generation. In this work, a quantum version of the famous (classical) wave function collapse algorithm is proposed. This quantum wave function collapse algorithm is based on the idea that a quantum circuit can be prepared in such a way that it acts as a special-purpose random generator for content of a desired form. The proposed method is presented theoretically and investigated experimentally on simulators and IBM Quantum devices.

DOI
10.1109/mcg.2024.3447775

Related papers

Quantum Random Number Generation

Jincheng Zhang

Quantum random number generation (QRNG) offers a fundamentally different approach to randomness compared to classical methods. This paper explores the design and principles of a QRNG based on harnessing the inherent uncertainty in quantum measurements. The core claim is the development of a system capable of generating truly random quantum bit sequences. This is achieved by exploiting the probabilistic nature of quantum measurement, specifically the collapse of the wave function upon observation, which yields unpredictable outcomes. The generated sequences are expected to exhibit statistical independence, a crucial property for various quantum computing applications. This work lays the groundwork for a detailed exploration of QRNG techniques, highlighting their potential impact on secure quantum communication and simulation. We will detail the theoretical underpinnings, focusing on the mathematical formulation of the process and the statistical properties of the generated random numbers.

•Aug 31, 2026•0 citations

Title: Quantum-Inspired Wave Function Collapse (QWWC) for Machine Learning

Jincheng Zhang

Quantum-Inspired Wave Function Collapse (QWWC) is a novel approach to machine learning algorithm optimization that seeks to enhance model accuracy and generalization through a modified wave function collapse process. Traditional methods often struggle with the inherent randomness of classical algorithms, leading to suboptimal performance. This paper proposes a new method that introduces quantum-inspired noise and entanglement into the wave function collapse, aiming to mitigate these limitations and improve model reliability. We explore the potential benefits of this approach in various machine learning tasks, focusing on [Specific Machine Learning Task - e.g., Deep Neural Network Classification] and demonstrating its effectiveness through simulations and preliminary experiments. The core of this work lies in the design of a novel noise distribution and entanglement strategy that minimizes the effects of classical randomness while maximizing the impact of quantum-inspired effects. This research highlights the potential for a paradigm shift in machine learning optimization, moving beyond approximations towards a more robust and precise approach.

•Sep 4, 2026•0 citations

Procedural Generation and Games at the Dawn of Fault Tolerant Quantum Computing

Daniel Bultrini, James R. Wootton

Vision paper Abstract-Quantum computers have long been more of a toy for researchers than a tool for solving complex problems. However, recent advances in the field make exploiting the advantages of fault-tolerant quantum computers feasible in the next 5 to 10 years. It is now time to begin imagining how such devices could be used in practice for game development and deployment. In this work we identify procedural content generation as a very promising area of application and exploration. We examine a selection of algorithmic approaches used in classical procedural content generation and propose promising quantum algorithms that could provide an alternative approach or a computational advantage. We then end with a hypothetical game that exploits a recent quantum algorithm for computing the Jones polynomial exponentially faster than classical computers could.

IEEE Conference on Games (CoG)•Aug 19, 2025•0 citations

Title: Fractal Wave Function Collapse for Quantum Simulation

Jincheng Zhang

Quantum simulation offers unprecedented capabilities in understanding and modeling quantum systems, but current methods are hampered by computational complexity. This paper investigates a novel approach to quantum simulation—Fractal Wave Function Collapse (FWFC)—which leverages fractal geometry to represent and collapse quantum wave function states, enabling the creation of highly complex simulations. The core claim is that this method can overcome computational limitations, facilitating the simulation of intricate quantum phenomena with significantly reduced computational demands. We present a theoretical framework and preliminary implementation demonstrating the potential of FWC to generate novel quantum states and simulations.

•Sep 4, 2026•0 citations

Algorithmic Self-Organization via Wave Function Collapse

Jincheng Zhang

This paper explores the application of wave function collapse as a novel algorithmic framework for promoting self-organization within complex systems. Traditional approaches often involve explicit control and manipulation, while this work proposes a method that leverages the inherent probabilistic nature of wave function collapse to guide system evolution towards stable states. The core mechanism involves continuous collapse of the system's wave function, triggering a cascade of transformations and ultimately driving the system towards a predetermined configuration. We present a preliminary implementation demonstrating the effectiveness of this approach in a simplified, yet illustrative, example. The paper discusses the theoretical underpinnings of wave function collapse, its potential applicability to various systems, and future directions for research.

•Sep 1, 2026•0 citations

Title: Quantum-Enhanced Wave Function Collapse for Algorithm Optimization

Jincheng Zhang

This paper explores the potential of quantum mechanics to revolutionize algorithm optimization, specifically focusing on the quantum collapse of wave functions. We propose a novel approach utilizing a quantum annealing algorithm to guide the collapse of wave functions during optimization, aiming to significantly accelerate convergence and enhance solution quality compared to classical methods. The core of this investigation centers on the fundamental properties of superposition and entanglement, demonstrating how these quantum phenomena can be harnessed to overcome computational bottlenecks inherent in optimization processes. We present preliminary results showcasing the efficacy of this method, highlighting its potential to unlock new levels of algorithm efficiency.

•Aug 31, 2026•0 citations
Quantum computers exhibit an inherent randomness, so it seems natural to consider them for procedural content generation. In this work, a quantum version of the famous (classical) wave function collapse algorithm is proposed. This quantum wave function collapse algorithm is based on the idea that a quantum circuit can be prepared in such a way that it acts as a special-purpose random generator for content of a desired form. The proposed method is presented theoretically and investigated experimentally on simulators and IBM Quantum devices.
Quantum random number generation (QRNG) offers a fundamentally different approach to randomness compared to classical methods. This paper explores the design and principles of a QRNG based on harnessing the inherent uncertainty in quantum measurements. The core claim is the development of a system capable of generating truly random quantum bit sequences. This is achieved by exploiting the probabilistic nature of quantum measurement, specifically the collapse of the wave function upon observation, which yields unpredictable outcomes. The generated sequences are expected to exhibit statistical independence, a crucial property for various quantum computing applications. This work lays the groundwork for a detailed exploration of QRNG techniques, highlighting their potential impact on secure quantum communication and simulation. We will detail the theoretical underpinnings, focusing on the mathematical formulation of the process and the statistical properties of the generated random numbers.
Quantum-Inspired Wave Function Collapse (QWWC) is a novel approach to machine learning algorithm optimization that seeks to enhance model accuracy and generalization through a modified wave function collapse process. Traditional methods often struggle with the inherent randomness of classical algorithms, leading to suboptimal performance. This paper proposes a new method that introduces quantum-inspired noise and entanglement into the wave function collapse, aiming to mitigate these limitations and improve model reliability. We explore the potential benefits of this approach in various machine learning tasks, focusing on [Specific Machine Learning Task - e.g., Deep Neural Network Classification] and demonstrating its effectiveness through simulations and preliminary experiments. The core of this work lies in the design of a novel noise distribution and entanglement strategy that minimizes the effects of classical randomness while maximizing the impact of quantum-inspired effects. This research highlights the potential for a paradigm shift in machine learning optimization, moving beyond approximations towards a more robust and precise approach.
Vision paper Abstract-Quantum computers have long been more of a toy for researchers than a tool for solving complex problems. However, recent advances in the field make exploiting the advantages of fault-tolerant quantum computers feasible in the next 5 to 10 years. It is now time to begin imagining how such devices could be used in practice for game development and deployment. In this work we identify procedural content generation as a very promising area of application and exploration. We examine a selection of algorithmic approaches used in classical procedural content generation and propose promising quantum algorithms that could provide an alternative approach or a computational advantage. We then end with a hypothetical game that exploits a recent quantum algorithm for computing the Jones polynomial exponentially faster than classical computers could.
Quantum simulation offers unprecedented capabilities in understanding and modeling quantum systems, but current methods are hampered by computational complexity. This paper investigates a novel approach to quantum simulation—Fractal Wave Function Collapse (FWFC)—which leverages fractal geometry to represent and collapse quantum wave function states, enabling the creation of highly complex simulations. The core claim is that this method can overcome computational limitations, facilitating the simulation of intricate quantum phenomena with significantly reduced computational demands. We present a theoretical framework and preliminary implementation demonstrating the potential of FWC to generate novel quantum states and simulations.
This paper explores the application of wave function collapse as a novel algorithmic framework for promoting self-organization within complex systems. Traditional approaches often involve explicit control and manipulation, while this work proposes a method that leverages the inherent probabilistic nature of wave function collapse to guide system evolution towards stable states. The core mechanism involves continuous collapse of the system's wave function, triggering a cascade of transformations and ultimately driving the system towards a predetermined configuration. We present a preliminary implementation demonstrating the effectiveness of this approach in a simplified, yet illustrative, example. The paper discusses the theoretical underpinnings of wave function collapse, its potential applicability to various systems, and future directions for research.
This paper explores the potential of quantum mechanics to revolutionize algorithm optimization, specifically focusing on the quantum collapse of wave functions. We propose a novel approach utilizing a quantum annealing algorithm to guide the collapse of wave functions during optimization, aiming to significantly accelerate convergence and enhance solution quality compared to classical methods. The core of this investigation centers on the fundamental properties of superposition and entanglement, demonstrating how these quantum phenomena can be harnessed to overcome computational bottlenecks inherent in optimization processes. We present preliminary results showcasing the efficacy of this method, highlighting its potential to unlock new levels of algorithm efficiency.