Dynamic Quantum Circuit Compilation via Tensor Networks

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum circuit compilation is a crucial step in realizing quantum algorithms on near-term quantum hardware. Traditional compilation methods often employ static circuit representations, limiting their ability to adapt to the fluctuating coherence properties and connectivity constraints of actual quantum devices. This research introduces a novel approach utilizing tensor network representations for dynamic quantum circuit compilation. The core idea is to leverage the inherent entanglement and superposition present in quantum systems to create adaptive circuit layouts. Our method employs an iterative tensor network algorithm that continuously refines circuit representations, prioritizing coherence preservation and minimizing gate count, while incorporating real-time qubit state estimations. This dynamic adaptation directly addresses the challenges posed by the limitations of current quantum hardware, offering a pathway to more efficient and robust quantum algorithm execution. The key innovation lies in the application of tensor networks, a powerful mathematical tool, to the optimization problem of quantum circuit compilation.

DOI: 10.5281/zenodo.22292188

Related papers

Adaptive 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 gates executable on a specific quantum hardware. However, traditional compilation methods often struggle with the inherent complexities of quantum circuits, including their high dimensionality, susceptibility to noise, and the diverse characteristics of different quantum devices. This work proposes a novel approach to adaptive quantum circuit compilation that leverages the power of tensor networks (TNs) and reinforcement learning (RL). The core idea is to represent quantum circuits as TNs, dynamically adjusting the network's structure to capture the relevant circuit features while minimizing computational overhead and mitigating noise effects. A reinforcement learning agent is trained to iteratively construct and refine these TN representations, guided by feedback signals derived from circuit fidelity metrics and computational cost estimates. This dynamic adaptation allows for a more efficient and robust compilation process, potentially leading to improved performance and reduced error rates in quantum computations. The key contribution lies in the integration of TNs with RL for a self-optimizing compilation pipeline. This approach offers a promising direction for tackling the challenges inherent in quantum hardware limitations and diverse quantum device architectures.

Sep 5, 20260 citations

Adaptive Quantum Circuit Compilation for Hardware-Aware Optimization

Jincheng Zhang

Quantum circuit compilation is a critical step in realizing quantum algorithms on real quantum hardware. Traditional compilation methods often employ static optimization strategies, neglecting the unique constraints and characteristics of the target quantum device. This paper introduces a novel adaptive quantum circuit compilation framework designed to dynamically adjust circuit designs based on hardware-specific information, aiming to minimize gate count and, consequently, error rates. The core of the framework lies in integrating a hardware simulator directly within the compilation process. This allows the compiler to evaluate the performance of various circuit designs—considering factors like qubit connectivity, gate fidelity, and decoherence models—before committing to a final layout. The proposed approach represents a shift from static to adaptive optimization, providing a more nuanced and effective strategy for translating quantum algorithms into executable code on diverse quantum hardware platforms. This research demonstrates a significant potential improvement in quantum circuit efficiency and fidelity, addressing a key bottleneck in the advancement of quantum computing.

Sep 3, 20260 citations

Adaptive Quantum Circuit Compilation based on Quantum Coherence Analysis

Jincheng Zhang

Quantum circuit compilation is a crucial step in transforming a high-level quantum algorithm description into a sequence of gates executable on a specific quantum hardware platform. Traditional compilation methods predominantly focus on minimizing gate count and circuit depth, often overlooking the significant impact of quantum coherence on the overall fidelity of the computation. This paper introduces an adaptive quantum circuit compilation algorithm designed to mitigate this limitation. The algorithm dynamically analyzes the coherence properties of the quantum circuit during the compilation process, aiming to preserve coherence while simultaneously minimizing detrimental decoherence effects. This approach represents a fundamental shift in the compilation paradigm, directly addressing the critical challenge of coherence maintenance in quantum computing. The proposed methodology utilizes a novel metric for coherence analysis and employs an adaptive optimization strategy to achieve superior circuit fidelity compared to conventional compilation techniques. The effectiveness of the algorithm is demonstrated through theoretical analysis and a detailed explanation of its components, paving the way for more robust and accurate quantum circuit implementations.

Sep 2, 20260 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, 20260 citations

Adaptive Quantum Circuit Compilation with Error Mitigation

Jincheng Zhang

Quantum circuit compilation is a fundamental step in quantum computing, translating high-level quantum algorithms into sequences of gates executable on a specific quantum hardware. However, current compilation methods often employ static decompositions, neglecting the inherent noise characteristics of quantum devices, leading to suboptimal circuit designs and reduced fidelity. This work proposes a novel adaptive quantum circuit compilation strategy that dynamically adjusts circuit decompositions in real-time, based on continuous monitoring of hardware noise profiles. The core of this approach lies in integrating quantum error mitigation techniques with a feedback loop that iteratively refines the circuit design to minimize the detrimental effects of noise. We introduce a framework for evaluating the effectiveness of this adaptive compilation process, demonstrating potential improvements in circuit fidelity and execution time. This research directly addresses the critical need for real-time adaptive compilation, a cornerstone for the practical realization of fault-tolerant quantum computing. The presented methodology offers a promising path towards more efficient and robust quantum algorithms, ultimately contributing to the advancement of quantum information processing.

Sep 7, 20260 citations

Adaptive Quantum Circuit Compilation via Reinforcement Learning

Jincheng Zhang

Quantum circuit compilation is a critical step in realizing quantum algorithms on near-term quantum hardware. The process of translating high-level quantum algorithms into low-level gate sequences, while minimizing gate count and circuit depth, is often performed using deterministic algorithms that struggle to adapt to the diverse and often imperfect characteristics of available quantum devices. This paper proposes a novel approach to quantum circuit compilation utilizing reinforcement learning (RL). An RL agent is trained to dynamically adapt to hardware constraints, such as qubit connectivity and gate decomposition costs, optimizing circuit fidelity and execution time. The core claim is that RL can significantly improve quantum circuit compilation by introducing a dynamic and adaptive compilation process. The agent learns a policy that maps high-level quantum algorithms to optimized low-level gate sequences, effectively bridging the gap between theoretical algorithms and practical hardware execution. This work demonstrates the potential of RL to overcome the limitations of traditional deterministic compilation methods and paves the way for more efficient and robust quantum algorithm implementation.

Aug 28, 20260 citations