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

Geometric Amplification in Quantum Networks: The MPCT-τ Protocol for Selective State Enhancement

lichao

Published Jan 15, 2026• 0 citations
Publisher

Abstract

This paper introduces the MPCT-τ (Multi-Path Closure with adaptive Time-constant) protocol, a novel geometric approach for selective amplification of tar-get states in quantum networks. By leveraging geometric phase closure relationscombined with adaptive time-scale feedback and weak marking perturbations, weachieve significant amplification of specific quantum states. Numerical simulationsdemonstrate an average amplification factor of 7.44× for target states in 12-nodenetworks, substantially outperforming pure geometric evolution (0.94×). The sys-tem exhibits controllable entanglement entropy (ranging from 0.000 to 0.690) andshows promising scaling behavior with network size. The protocol operates throughthree interconnected mechanisms: (1) geometric phase closure enhancement viatriangular loops, (2) adaptive τ feedback based on closure errors, and (3) weaksymmetry-breaking marking. Our results provide a new paradigm for geomet-ric approaches to quantum state manipulation and offer potential applications inquantum-enhanced computation, state preparation, and quantum sensing.

DOI
10.5281/zenodo.18257641

Related papers

MPCT-τ: Geometric Quantum State Control via Multi-Path Closure Time Theory

lichao

We present a novel quantum state control method based on Multi-PathClosure Time (MPCT) theory that achieves significant quantum stateamplification through purely geometric dynamics without externalHamiltonian driving. The MPCT-τ framework demonstrates: (1) Average 2.50×(maximum 3.55×) local state probability enhancement via weak periodicmarking, (2) Observation of quantized peak spacing following n² patternswith n²=4 corresponding to n=2, (3) Natural formation of multi-peaksuperposition states with average 3.4 significant peaks in geometric mode,and (4) Non-zero entanglement entropy averaging 0.44, confirming genuinequantum correlations. The core mechanism involves emergent time scalesfrom MPCT phase closures, geometric vibrations for coherence maintenance,and weak dissipation implementing the Principle of Existence Cost (PEC). Thiswork establishes a new paradigm for low-energy geometric quantum controlwith only three core parameters, potentially revolutionizing quantumcomputation approaches.Keywords: Quantum state control, Geometric quantum computation, Multipath closure, Emergent time, Quantum amplification, n² quantization

•Jan 16, 2026•0 citations

MPCT-τ : Geometric Quantum State Control via Multi-Path Closure Time Theory

lichao

This paper presents a novel quantum state control method basedon Multi-Path Closure Time (MPCT) theory. Through purely ge-ometric dynamical processes without external Hamiltonian driving,we achieve: (1) Significant quantum state amplification with aver-age 3.29× (maximum 3.70×) local state probability enhancement viaweak marking; (2) Observation of quantized peak spacing followingn2 patterns, specifically n2 = 4 corresponding to n = 2; (3) Naturalformation of multi-peak superposition states with average 3.2 signifi-cant peaks in geometric mode. The core mechanism involves MPCTclosure phase driving for temporal emergence, geometric vibration sta-bilization for coherence maintenance, and weak dissipation balancingimplementing the Principle of Existence Cost (PEC). This work es-tablishes a new paradigm for low-energy geometric quantum control,potentially revolutionizing quantum computation approaches.

•Jan 16, 2026•0 citations

Adaptive Topology Control for Quantum Entanglement Enhancement

Jincheng Zhang

Quantum entanglement is a fundamental resource for quantum computation, offering potential advantages in secure communication and quantum algorithms. However, achieving high-fidelity entanglement generation and manipulation remains a significant challenge. This paper explores the application of adaptive topology control to enhance quantum entanglement, aiming to improve both computational accuracy and efficiency. We propose a novel control scheme that dynamically adjusts the topology of the entangled states, leveraging the inherent properties of quantum geometry to mitigate errors and accelerate computation. The core mechanism involves a feedback loop that continuously refines the topological structure, optimizing for reduced noise and increased entanglement fidelity. This approach offers a promising pathway towards practical quantum computation by addressing limitations of traditional methods.

•Sep 3, 2026•0 citations

Core Claim: Researching adaptive geometric transformations of quantum entanglement, leveraging the entanglement of quantum states to perform complex topological transformations and explore its applications in quantum computing and quantum information processing.

Jincheng Zhang

Quantum entanglement, a cornerstone of quantum mechanics, presents a unique opportunity for manipulating complex systems. This research explores the potential of adaptive geometric transformations of quantum entanglement, capitalizing on the inherent properties of entangled states to achieve intricate topological transformations. Traditional geometric transformations often struggle with the inherent complexity of topological phenomena, while our approach leverages entanglement to circumvent these limitations, offering a promising avenue for advancements in quantum computation and information processing. Specifically, we investigate a novel algorithm that dynamically adjusts the state of entangled qubits to induce desired topological transformations. This involves creating a feedback loop that continuously refines the entangled state, allowing for the implementation of sophisticated geometric operations. The core mechanism centers around the precise manipulation of topological fields through entanglement, enabling the generation of complex, non-intuitive topological structures. This work aims to demonstrate the practical viability of this approach, providing a foundation for the development of novel quantum algorithms and computational paradigms.

•Sep 6, 2026•0 citations

Quantum Topology Adaptive Geometry Learning

Jincheng Zhang

Quantum topology, a rapidly developing field, seeks to leverage the unique properties of quantum mechanics to create novel computational paradigms. One promising approach is the manipulation of quantum systems through geometric transformations, offering potential for enhanced functionality and control. This paper explores the concept of adaptive geometry learning, a novel framework designed to dynamically adjust the geometric structure of quantum systems in response to input data. We propose a learning mechanism that iteratively refines the system's geometry, optimizing performance through a closed-loop process. The core claim of this work is that a quantum geometry learning framework can dramatically enhance the performance of quantum systems by intelligently adapting to the data. This is a significant departure from traditional static geometry learning, which limits the system's adaptability and potential for complex behavior. We demonstrate the framework's effectiveness through simulations, showcasing its ability to learn complex geometric patterns and achieve improved outcomes compared to baseline techniques. The paper concludes with a discussion of future research directions and potential applications.

•Aug 29, 2026•0 citations

Nonlinear Quantum Topology: Adaptive Mapping via Geometric Path Optimization

Jincheng Zhang

Quantum topology, a fascinating field, offers a unique perspective on the properties of space and time. This paper explores the application of non-linear quantum topology to adaptive mapping, specifically through the use of geometric path optimization. We introduce a novel algorithm that leverages the dynamic evolution of quantum topology represented by geometric paths to achieve precise parameter control and optimization. The core mechanism centers around constructing a quantum path representation, modeling its evolution, and employing optimization techniques tailored to these evolving paths. The proposed framework offers a fundamentally new approach to controlling quantum systems, promising advancements in quantum information processing and manipulation. This work provides a conceptual foundation for exploring complex quantum systems through a novel mapping strategy.

•Aug 31, 2026•0 citations
This paper introduces the MPCT-τ (Multi-Path Closure with adaptive Time-constant) protocol, a novel geometric approach for selective amplification of tar-get states in quantum networks. By leveraging geometric phase closure relationscombined with adaptive time-scale feedback and weak marking perturbations, weachieve significant amplification of specific quantum states. Numerical simulationsdemonstrate an average amplification factor of 7.44× for target states in 12-nodenetworks, substantially outperforming pure geometric evolution (0.94×). The sys-tem exhibits controllable entanglement entropy (ranging from 0.000 to 0.690) andshows promising scaling behavior with network size. The protocol operates throughthree interconnected mechanisms: (1) geometric phase closure enhancement viatriangular loops, (2) adaptive τ feedback based on closure errors, and (3) weaksymmetry-breaking marking. Our results provide a new paradigm for geomet-ric approaches to quantum state manipulation and offer potential applications inquantum-enhanced computation, state preparation, and quantum sensing.
We present a novel quantum state control method based on Multi-PathClosure Time (MPCT) theory that achieves significant quantum stateamplification through purely geometric dynamics without externalHamiltonian driving. The MPCT-τ framework demonstrates: (1) Average 2.50×(maximum 3.55×) local state probability enhancement via weak periodicmarking, (2) Observation of quantized peak spacing following n² patternswith n²=4 corresponding to n=2, (3) Natural formation of multi-peaksuperposition states with average 3.4 significant peaks in geometric mode,and (4) Non-zero entanglement entropy averaging 0.44, confirming genuinequantum correlations. The core mechanism involves emergent time scalesfrom MPCT phase closures, geometric vibrations for coherence maintenance,and weak dissipation implementing the Principle of Existence Cost (PEC). Thiswork establishes a new paradigm for low-energy geometric quantum controlwith only three core parameters, potentially revolutionizing quantumcomputation approaches.Keywords: Quantum state control, Geometric quantum computation, Multipath closure, Emergent time, Quantum amplification, n² quantization
This paper presents a novel quantum state control method basedon Multi-Path Closure Time (MPCT) theory. Through purely ge-ometric dynamical processes without external Hamiltonian driving,we achieve: (1) Significant quantum state amplification with aver-age 3.29× (maximum 3.70×) local state probability enhancement viaweak marking; (2) Observation of quantized peak spacing followingn2 patterns, specifically n2 = 4 corresponding to n = 2; (3) Naturalformation of multi-peak superposition states with average 3.2 signifi-cant peaks in geometric mode. The core mechanism involves MPCTclosure phase driving for temporal emergence, geometric vibration sta-bilization for coherence maintenance, and weak dissipation balancingimplementing the Principle of Existence Cost (PEC). This work es-tablishes a new paradigm for low-energy geometric quantum control,potentially revolutionizing quantum computation approaches.
Quantum entanglement is a fundamental resource for quantum computation, offering potential advantages in secure communication and quantum algorithms. However, achieving high-fidelity entanglement generation and manipulation remains a significant challenge. This paper explores the application of adaptive topology control to enhance quantum entanglement, aiming to improve both computational accuracy and efficiency. We propose a novel control scheme that dynamically adjusts the topology of the entangled states, leveraging the inherent properties of quantum geometry to mitigate errors and accelerate computation. The core mechanism involves a feedback loop that continuously refines the topological structure, optimizing for reduced noise and increased entanglement fidelity. This approach offers a promising pathway towards practical quantum computation by addressing limitations of traditional methods.
Quantum entanglement, a cornerstone of quantum mechanics, presents a unique opportunity for manipulating complex systems. This research explores the potential of adaptive geometric transformations of quantum entanglement, capitalizing on the inherent properties of entangled states to achieve intricate topological transformations. Traditional geometric transformations often struggle with the inherent complexity of topological phenomena, while our approach leverages entanglement to circumvent these limitations, offering a promising avenue for advancements in quantum computation and information processing. Specifically, we investigate a novel algorithm that dynamically adjusts the state of entangled qubits to induce desired topological transformations. This involves creating a feedback loop that continuously refines the entangled state, allowing for the implementation of sophisticated geometric operations. The core mechanism centers around the precise manipulation of topological fields through entanglement, enabling the generation of complex, non-intuitive topological structures. This work aims to demonstrate the practical viability of this approach, providing a foundation for the development of novel quantum algorithms and computational paradigms.
Quantum topology, a rapidly developing field, seeks to leverage the unique properties of quantum mechanics to create novel computational paradigms. One promising approach is the manipulation of quantum systems through geometric transformations, offering potential for enhanced functionality and control. This paper explores the concept of adaptive geometry learning, a novel framework designed to dynamically adjust the geometric structure of quantum systems in response to input data. We propose a learning mechanism that iteratively refines the system's geometry, optimizing performance through a closed-loop process. The core claim of this work is that a quantum geometry learning framework can dramatically enhance the performance of quantum systems by intelligently adapting to the data. This is a significant departure from traditional static geometry learning, which limits the system's adaptability and potential for complex behavior. We demonstrate the framework's effectiveness through simulations, showcasing its ability to learn complex geometric patterns and achieve improved outcomes compared to baseline techniques. The paper concludes with a discussion of future research directions and potential applications.
Quantum topology, a fascinating field, offers a unique perspective on the properties of space and time. This paper explores the application of non-linear quantum topology to adaptive mapping, specifically through the use of geometric path optimization. We introduce a novel algorithm that leverages the dynamic evolution of quantum topology represented by geometric paths to achieve precise parameter control and optimization. The core mechanism centers around constructing a quantum path representation, modeling its evolution, and employing optimization techniques tailored to these evolving paths. The proposed framework offers a fundamentally new approach to controlling quantum systems, promising advancements in quantum information processing and manipulation. This work provides a conceptual foundation for exploring complex quantum systems through a novel mapping strategy.