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

Formal Modeling of Quantum Circuits with Tensor Networks

Jincheng Zhang

Published Aug 28, 2026• 0 citations
Publisher

Abstract

Quantum circuits, fundamental to quantum computation, represent quantum states through a sequence of unitary transformations. The exponential growth in the Hilbert space dimension with increasing circuit size presents a significant challenge to simulation and analysis. This work introduces a novel tensor network approach to formally model and manipulate quantum circuits, specifically designed to mitigate this exponential scaling issue. The core idea is to represent the circuit's state vector as a tensor network, leveraging techniques for efficient compression and expansion. We explore the application of variational tensor networks (VTNs) to approximate the dynamics of quantum circuits, offering a pathway towards more scalable simulations. The effectiveness of this method is demonstrated through theoretical analysis and conceptual design, focusing on key aspects of tensor network construction and manipulation tailored to quantum circuit representations. This approach aims to provide a more scalable representation compared to existing methods.

DOI
10.5281/zenodo.22137276

Related papers

Symbolic Simulation of Quantum Circuits with Tensor Networks

Jincheng Zhang

The accurate simulation of quantum circuits remains a significant challenge due to the exponential scaling of computational resources with circuit size. This work explores the application of tensor network methods, specifically Matrix Product States (MPS), to provide a scalable approach for simulating quantum circuits. We demonstrate how tensor networks can efficiently represent and manipulate the states of quantum circuits, reducing the computational burden associated with traditional methods. The core claim of this research is that tensor networks offer a viable path to accurately simulating larger and more complex quantum systems. This research introduces a novel framework for symbolic simulation, leveraging the power of tensor network representations to overcome the limitations of conventional simulation techniques. The effectiveness of this approach is illustrated through theoretical analysis and conceptual examples.

•Sep 5, 2026•0 citations

Relational Quantum Circuit Compilation via Tensor Networks

Jincheng Zhang

Quantum circuit compilation is a critical step in realizing quantum computation, aiming to translate high-level quantum algorithms into a sequence of low-level gate operations executable on a quantum hardware. Traditional compilation methods often suffer from exponential scaling in the representation of the quantum state space, leading to prohibitive computational costs. This work introduces a novel approach to quantum circuit compilation utilizing tensor networks. We represent quantum circuits and their resulting states as higher-order tensor networks, enabling efficient manipulation and contraction of these representations. Our algorithm leverages tensor contraction and permutation techniques specifically tailored to the inherent structure of quantum circuits, minimizing the rank of the tensor networks and, consequently, reducing computational overhead. This approach offers the potential for significant speedups compared to conventional compilation techniques, addressing the core challenge of exponential scaling in quantum state representation. The method's effectiveness is demonstrated through theoretical analysis and a novel compilation algorithm.

•Sep 3, 2026•0 citations

Relational Quantum Circuit Synthesis via Tensor Network Optimization

Jincheng Zhang

The synthesis of efficient quantum circuits for complex computations remains a significant challenge in quantum computing. Traditional methods often struggle with the exponential growth of the solution space, particularly as the number of qubits increases. This work introduces a novel approach to quantum circuit synthesis leveraging tensor network optimization. We represent quantum circuits as tensor networks and employ techniques such as Tensor Train and Product Tensor Decomposition to efficiently approximate the optimal circuit topology. A quantum-inspired probabilistic sampling strategy is then integrated to explore the solution space effectively. The core claim is that optimizing quantum circuit synthesis for complex systems is intrinsically intractable using conventional methods. This approach offers a potentially scalable alternative, providing a pathway toward designing practical quantum circuits for a wider range of applications. The method is presented with a focus on the mathematical formalism and optimization techniques involved.

•Aug 28, 2026•0 citations

Distributed Quantum Simulation via Tensor Networks

Jincheng Zhang

This paper explores the potential of tensor networks for enabling distributed quantum simulation, a crucial step towards scaling up quantum computations. Traditional quantum simulation faces limitations due to the exponential growth of state vector sizes required for complex systems. Distributed quantum simulation aims to overcome this by partitioning the simulation across multiple quantum processors. However, efficient representation and manipulation of the resulting distributed quantum state remain a significant challenge. This work proposes utilizing tensor networks as a framework for representing and processing these distributed quantum states. Tensor networks offer an inherent structure that facilitates efficient parallel computation and minimizes communication overhead between quantum processors. We outline a theoretical framework, detailing the application of tensor network contractions and disentanglement techniques specifically tailored for distributed quantum simulation scenarios. The core claim of this work is that scaling up quantum simulations requires efficient methods for representing and manipulating quantum states, and tensor networks provide a promising avenue for achieving this goal. We present a method to reduce the computational complexity associated with simulating large quantum systems by leveraging the properties of tensor networks. The approach allows for a more scalable simulation strategy, moving beyond the limitations of traditional state vector representations. Further research will explore specific tensor network architectures and optimization strategies for different simulation problems.

•Sep 7, 2026•0 citations

Formal Specification of Quantum Algorithms via Tensor Networks

Jincheng Zhang

Quantum algorithms offer the potential for exponential speedups over classical algorithms for specific computational tasks. However, the inherent complexity and probabilistic nature of quantum mechanics pose significant challenges in verifying and understanding these algorithms. Traditional approaches, relying on simulations and theoretical arguments, often struggle to provide definitive proof of correctness and to guide the optimization of quantum circuits. This paper proposes a novel approach to formally specifying quantum algorithms by representing them using tensor networks. Tensor networks provide a powerful mathematical framework for describing multi-dimensional quantum states and operations, allowing for a precise and rigorous description of the algorithm's evolution. We demonstrate how tensor network techniques can be used to capture the unitary transformations that constitute a quantum algorithm, providing a foundation for automated verification and optimization strategies. The key contribution of this work is the application of tensor networks to the formal specification of quantum algorithms, addressing a critical gap in the field and paving the way for more robust and reliable quantum computation. The formal specification allows for the definition of the algorithm's state space and the evolution operator, enabling the application of mathematical techniques to analyze and improve the algorithm's performance.

•Aug 31, 2026•0 citations

Scalable Quantum Simulation of Molecular Dynamics using Tensor Networks

Jincheng Zhang

Quantum simulation offers a pathway to accurately model and understand complex molecular systems, a task often intractable using classical computational methods. However, the computational cost of simulating quantum systems, particularly those involving many particles and complex interactions, remains a significant barrier. This work presents a novel approach leveraging tensor network methods to achieve scalable quantum simulation of molecular dynamics. We demonstrate how tensor networks can efficiently represent the quantum state of a molecular system, allowing for the accurate calculation of its dynamics. The core idea is to utilize the tensor network representation to reduce the computational complexity associated with traditional quantum simulation algorithms. The presented method offers a promising route toward simulating larger and more realistic molecular systems, opening up new possibilities in fields such as drug discovery, materials science, and fundamental physics. This paper details the theoretical framework and provides a conceptual outline, emphasizing the scalability of the approach.

•Aug 28, 2026•0 citations
Quantum circuits, fundamental to quantum computation, represent quantum states through a sequence of unitary transformations. The exponential growth in the Hilbert space dimension with increasing circuit size presents a significant challenge to simulation and analysis. This work introduces a novel tensor network approach to formally model and manipulate quantum circuits, specifically designed to mitigate this exponential scaling issue. The core idea is to represent the circuit's state vector as a tensor network, leveraging techniques for efficient compression and expansion. We explore the application of variational tensor networks (VTNs) to approximate the dynamics of quantum circuits, offering a pathway towards more scalable simulations. The effectiveness of this method is demonstrated through theoretical analysis and conceptual design, focusing on key aspects of tensor network construction and manipulation tailored to quantum circuit representations. This approach aims to provide a more scalable representation compared to existing methods.
The accurate simulation of quantum circuits remains a significant challenge due to the exponential scaling of computational resources with circuit size. This work explores the application of tensor network methods, specifically Matrix Product States (MPS), to provide a scalable approach for simulating quantum circuits. We demonstrate how tensor networks can efficiently represent and manipulate the states of quantum circuits, reducing the computational burden associated with traditional methods. The core claim of this research is that tensor networks offer a viable path to accurately simulating larger and more complex quantum systems. This research introduces a novel framework for symbolic simulation, leveraging the power of tensor network representations to overcome the limitations of conventional simulation techniques. The effectiveness of this approach is illustrated through theoretical analysis and conceptual examples.
Quantum circuit compilation is a critical step in realizing quantum computation, aiming to translate high-level quantum algorithms into a sequence of low-level gate operations executable on a quantum hardware. Traditional compilation methods often suffer from exponential scaling in the representation of the quantum state space, leading to prohibitive computational costs. This work introduces a novel approach to quantum circuit compilation utilizing tensor networks. We represent quantum circuits and their resulting states as higher-order tensor networks, enabling efficient manipulation and contraction of these representations. Our algorithm leverages tensor contraction and permutation techniques specifically tailored to the inherent structure of quantum circuits, minimizing the rank of the tensor networks and, consequently, reducing computational overhead. This approach offers the potential for significant speedups compared to conventional compilation techniques, addressing the core challenge of exponential scaling in quantum state representation. The method's effectiveness is demonstrated through theoretical analysis and a novel compilation algorithm.
The synthesis of efficient quantum circuits for complex computations remains a significant challenge in quantum computing. Traditional methods often struggle with the exponential growth of the solution space, particularly as the number of qubits increases. This work introduces a novel approach to quantum circuit synthesis leveraging tensor network optimization. We represent quantum circuits as tensor networks and employ techniques such as Tensor Train and Product Tensor Decomposition to efficiently approximate the optimal circuit topology. A quantum-inspired probabilistic sampling strategy is then integrated to explore the solution space effectively. The core claim is that optimizing quantum circuit synthesis for complex systems is intrinsically intractable using conventional methods. This approach offers a potentially scalable alternative, providing a pathway toward designing practical quantum circuits for a wider range of applications. The method is presented with a focus on the mathematical formalism and optimization techniques involved.
This paper explores the potential of tensor networks for enabling distributed quantum simulation, a crucial step towards scaling up quantum computations. Traditional quantum simulation faces limitations due to the exponential growth of state vector sizes required for complex systems. Distributed quantum simulation aims to overcome this by partitioning the simulation across multiple quantum processors. However, efficient representation and manipulation of the resulting distributed quantum state remain a significant challenge. This work proposes utilizing tensor networks as a framework for representing and processing these distributed quantum states. Tensor networks offer an inherent structure that facilitates efficient parallel computation and minimizes communication overhead between quantum processors. We outline a theoretical framework, detailing the application of tensor network contractions and disentanglement techniques specifically tailored for distributed quantum simulation scenarios. The core claim of this work is that scaling up quantum simulations requires efficient methods for representing and manipulating quantum states, and tensor networks provide a promising avenue for achieving this goal. We present a method to reduce the computational complexity associated with simulating large quantum systems by leveraging the properties of tensor networks. The approach allows for a more scalable simulation strategy, moving beyond the limitations of traditional state vector representations. Further research will explore specific tensor network architectures and optimization strategies for different simulation problems.
Quantum algorithms offer the potential for exponential speedups over classical algorithms for specific computational tasks. However, the inherent complexity and probabilistic nature of quantum mechanics pose significant challenges in verifying and understanding these algorithms. Traditional approaches, relying on simulations and theoretical arguments, often struggle to provide definitive proof of correctness and to guide the optimization of quantum circuits. This paper proposes a novel approach to formally specifying quantum algorithms by representing them using tensor networks. Tensor networks provide a powerful mathematical framework for describing multi-dimensional quantum states and operations, allowing for a precise and rigorous description of the algorithm's evolution. We demonstrate how tensor network techniques can be used to capture the unitary transformations that constitute a quantum algorithm, providing a foundation for automated verification and optimization strategies. The key contribution of this work is the application of tensor networks to the formal specification of quantum algorithms, addressing a critical gap in the field and paving the way for more robust and reliable quantum computation. The formal specification allows for the definition of the algorithm's state space and the evolution operator, enabling the application of mathematical techniques to analyze and improve the algorithm's performance.
Quantum simulation offers a pathway to accurately model and understand complex molecular systems, a task often intractable using classical computational methods. However, the computational cost of simulating quantum systems, particularly those involving many particles and complex interactions, remains a significant barrier. This work presents a novel approach leveraging tensor network methods to achieve scalable quantum simulation of molecular dynamics. We demonstrate how tensor networks can efficiently represent the quantum state of a molecular system, allowing for the accurate calculation of its dynamics. The core idea is to utilize the tensor network representation to reduce the computational complexity associated with traditional quantum simulation algorithms. The presented method offers a promising route toward simulating larger and more realistic molecular systems, opening up new possibilities in fields such as drug discovery, materials science, and fundamental physics. This paper details the theoretical framework and provides a conceptual outline, emphasizing the scalability of the approach.