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

IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

Integrating Complex Adaptive Systems Theory with Whole-Systems Quantum Computing Approaches

Pravir Malik

Published Oct 24, 2024• 0 citations
Publisher

Abstract

This paper explores the intersection of Complex Adaptive Systems (CAS) and whole-systems quantum computing, focusing on how CAS principles can enhance quantum computing methods and vice versa. CAS, characterized by complexity, adaptability, self-organization, and emergence, amongst other properties, are prevalent in various domains, including ecology, economics, and social systems. Whole-systems quantum computing, particularly through quantum annealing, manipulates the global quantum state of a system, utilizing entanglement and superposition to solve optimization problems. The paper outlines a methodology for modeling complex adaptive problems using quantum annealing, alongside case studies illustrating CAS theory’s application in quantum computation. By examining representative quantum computing companies the paper investigates their management of entropy-a key summary metric in CAS-and correlations with quantum supremacy. The integration of CAS dynamics into quantum computing is posited to aid in designing more advanced Hamiltonians and quantum algorithms, enhancing the capability to tackle complex, real-world problems more efficiently. The paper calls for further research into several promising avenues, such as the development of sophisticated Hamiltonians that encode complex interdependencies, the validation of CAS models on quantum hardware, and the potential for systematizing quantum supremacy through a virtuous cycle of CAS-informed quantum algorithm development.

DOI
10.1109/iemcon62851.2024.11093529

Related papers

Adaptive Self-Organized Quantum Systems

Jincheng Zhang

This paper introduces a novel approach to designing adaptive self-organized quantum systems, leveraging quantum entanglement and quantum tunneling to enhance the system's adaptability and facilitate more complex and efficient quantum computations. Traditional quantum systems are often static, and this algorithm introduces self-organization capabilities to enable more flexible quantum computation. The core mechanism centers around the manipulation of quantum entanglement and tunneling to achieve this adaptability. This work presents a framework for dynamically adjusting the system's properties, offering a significant advancement over existing approaches. The resulting system demonstrates improved performance in specific quantum algorithms through enhanced adaptability. The study explores the potential of this approach in tackling complex computational problems and lays the foundation for future research into adaptive quantum systems.

•Aug 31, 2026•0 citations

Quantum State Adaptation: A Dynamic Approach to Enhanced Quantum Computation

Jincheng Zhang

Quantum computing promises revolutionary computational capabilities, offering the potential to solve complex problems currently intractable for classical computers. However, realizing this potential hinges significantly on the efficient manipulation and control of quantum states. This paper investigates a novel approach to quantum state adaptation, leveraging the inherent dynamic properties of quantum systems to dynamically adjust quantum states, thereby enhancing the performance of quantum computations. We propose a framework predicated on the principle that a quantum system's response to its environment – specifically, its inherent adaptability – can be leveraged to optimize its quantum state. This research explores the potential of this dynamic adaptation mechanism, examining its impact on various quantum algorithms and highlighting its advantages in terms of performance and robustness. The core claim of this study is that utilizing this adaptive approach can significantly improve quantum computation efficiency.

•Aug 29, 2026•0 citations

Quantum Computing and Its Implications for Complex System Analysis

Kiran Iqbal, Omar Ahmad, Arnes Yuli Vandika

Quantum computing has emerged as a transformative technology capable of solving complex problems beyond the reach of classical computing. Its unique properties, such as superposition and entanglement, enable efficient processing of vast datasets, making it especially valuable for analyzing complex systems. This research aims to explore the implications of quantum computing for complex system analysis, particularly in fields such as physics, biology, and finance. The goal is to identify how quantum algorithms can enhance the understanding and modeling of intricate systems. A systematic literature review was conducted, examining recent advancements in quantum algorithms and their applications to complex system analysis. Comparative analyses were performed between classical and quantum computing approaches, focusing on specific case studies to illustrate the advantages of quantum solutions. The findings indicate that quantum computing significantly accelerates certain computations, leading to improved accuracy and efficiency in modeling complex systems. Case studies in quantum simulations of molecular interactions and financial modeling demonstrate substantial performance gains over classical methods. Quantum computing holds great promise for advancing the analysis of complex systems across various disciplines. Continued research and development in this area are essential to fully harness the capabilities of quantum technologies, ultimately leading to breakthroughs in understanding and solving complex problems.

Research of Scientia Naturalis•Dec 27, 2024•0 citations

Core Claim: Design a self-adaptive topological quantum computing framework for predicting quantum system evolution, and provide more effective quantum algorithm design.

Jincheng Zhang

Quantum computing offers the potential for exponentially faster computations compared to classical computers, but the design and optimization of quantum algorithms remain a significant challenge. This research explores a novel approach to quantum system evolution prediction using a self-adaptive topological framework. We propose a dynamic modeling strategy that leverages topology to represent the evolution of quantum states, allowing for the prediction of complex quantum behavior. The framework incorporates a continuous, self-consistent evolution process, enabling the identification of key transition points and the generation of more effective quantum algorithms. This work represents a significant advancement in quantum system dynamics prediction, offering a robust and adaptable methodology for exploring and designing new quantum algorithms. The core of our approach lies in the creation of a self-adaptive topology that evolves alongside the quantum system, providing a dynamic and predictive tool. This dynamic evolution, coupled with our modeling approach, promises to significantly enhance the efficiency and versatility of quantum computation. Specifically, we demonstrate the framework's ability to accurately predict the behavior of various quantum systems, including those exhibiting complex entanglement dynamics, opening doors to the development of novel quantum algorithms.

•Aug 31, 2026•0 citations

Exploring Quantum Computing for Solving Complex Engineering Problems

Raghav Agarwal

Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.

International Journal of Research in Modern Engineering & Emerging Technology•Dec 1, 2022•1 citation

Adaptive Topology & Quantum Computing Fusion

Jincheng Zhang

This paper investigates the synergistic integration of adaptive topology and quantum computing, presenting a novel framework for complex system modeling and optimization. Traditional methods struggle to efficiently handle intricate topological structures, offering a significant advantage through quantum computation's precision. We propose a method leveraging the strengths of both techniques, focusing on leveraging quantum computing's inherent capabilities for high-fidelity simulation and optimization of complex topological landscapes. The core mechanism centers around a hybrid approach wherein topology optimization algorithms are augmented with quantum-enhanced simulation, ultimately leading to improved performance in various application domains. This research explores the potential of this fusion to unlock new capabilities in complex system design and analysis.

•Aug 31, 2026•0 citations
This paper explores the intersection of Complex Adaptive Systems (CAS) and whole-systems quantum computing, focusing on how CAS principles can enhance quantum computing methods and vice versa. CAS, characterized by complexity, adaptability, self-organization, and emergence, amongst other properties, are prevalent in various domains, including ecology, economics, and social systems. Whole-systems quantum computing, particularly through quantum annealing, manipulates the global quantum state of a system, utilizing entanglement and superposition to solve optimization problems. The paper outlines a methodology for modeling complex adaptive problems using quantum annealing, alongside case studies illustrating CAS theory’s application in quantum computation. By examining representative quantum computing companies the paper investigates their management of entropy-a key summary metric in CAS-and correlations with quantum supremacy. The integration of CAS dynamics into quantum computing is posited to aid in designing more advanced Hamiltonians and quantum algorithms, enhancing the capability to tackle complex, real-world problems more efficiently. The paper calls for further research into several promising avenues, such as the development of sophisticated Hamiltonians that encode complex interdependencies, the validation of CAS models on quantum hardware, and the potential for systematizing quantum supremacy through a virtuous cycle of CAS-informed quantum algorithm development.
This paper introduces a novel approach to designing adaptive self-organized quantum systems, leveraging quantum entanglement and quantum tunneling to enhance the system's adaptability and facilitate more complex and efficient quantum computations. Traditional quantum systems are often static, and this algorithm introduces self-organization capabilities to enable more flexible quantum computation. The core mechanism centers around the manipulation of quantum entanglement and tunneling to achieve this adaptability. This work presents a framework for dynamically adjusting the system's properties, offering a significant advancement over existing approaches. The resulting system demonstrates improved performance in specific quantum algorithms through enhanced adaptability. The study explores the potential of this approach in tackling complex computational problems and lays the foundation for future research into adaptive quantum systems.
Quantum computing promises revolutionary computational capabilities, offering the potential to solve complex problems currently intractable for classical computers. However, realizing this potential hinges significantly on the efficient manipulation and control of quantum states. This paper investigates a novel approach to quantum state adaptation, leveraging the inherent dynamic properties of quantum systems to dynamically adjust quantum states, thereby enhancing the performance of quantum computations. We propose a framework predicated on the principle that a quantum system's response to its environment – specifically, its inherent adaptability – can be leveraged to optimize its quantum state. This research explores the potential of this dynamic adaptation mechanism, examining its impact on various quantum algorithms and highlighting its advantages in terms of performance and robustness. The core claim of this study is that utilizing this adaptive approach can significantly improve quantum computation efficiency.
Quantum computing has emerged as a transformative technology capable of solving complex problems beyond the reach of classical computing. Its unique properties, such as superposition and entanglement, enable efficient processing of vast datasets, making it especially valuable for analyzing complex systems. This research aims to explore the implications of quantum computing for complex system analysis, particularly in fields such as physics, biology, and finance. The goal is to identify how quantum algorithms can enhance the understanding and modeling of intricate systems. A systematic literature review was conducted, examining recent advancements in quantum algorithms and their applications to complex system analysis. Comparative analyses were performed between classical and quantum computing approaches, focusing on specific case studies to illustrate the advantages of quantum solutions. The findings indicate that quantum computing significantly accelerates certain computations, leading to improved accuracy and efficiency in modeling complex systems. Case studies in quantum simulations of molecular interactions and financial modeling demonstrate substantial performance gains over classical methods. Quantum computing holds great promise for advancing the analysis of complex systems across various disciplines. Continued research and development in this area are essential to fully harness the capabilities of quantum technologies, ultimately leading to breakthroughs in understanding and solving complex problems.
Quantum computing offers the potential for exponentially faster computations compared to classical computers, but the design and optimization of quantum algorithms remain a significant challenge. This research explores a novel approach to quantum system evolution prediction using a self-adaptive topological framework. We propose a dynamic modeling strategy that leverages topology to represent the evolution of quantum states, allowing for the prediction of complex quantum behavior. The framework incorporates a continuous, self-consistent evolution process, enabling the identification of key transition points and the generation of more effective quantum algorithms. This work represents a significant advancement in quantum system dynamics prediction, offering a robust and adaptable methodology for exploring and designing new quantum algorithms. The core of our approach lies in the creation of a self-adaptive topology that evolves alongside the quantum system, providing a dynamic and predictive tool. This dynamic evolution, coupled with our modeling approach, promises to significantly enhance the efficiency and versatility of quantum computation. Specifically, we demonstrate the framework's ability to accurately predict the behavior of various quantum systems, including those exhibiting complex entanglement dynamics, opening doors to the development of novel quantum algorithms.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.
This paper investigates the synergistic integration of adaptive topology and quantum computing, presenting a novel framework for complex system modeling and optimization. Traditional methods struggle to efficiently handle intricate topological structures, offering a significant advantage through quantum computation's precision. We propose a method leveraging the strengths of both techniques, focusing on leveraging quantum computing's inherent capabilities for high-fidelity simulation and optimization of complex topological landscapes. The core mechanism centers around a hybrid approach wherein topology optimization algorithms are augmented with quantum-enhanced simulation, ultimately leading to improved performance in various application domains. This research explores the potential of this fusion to unlock new capabilities in complex system design and analysis.