Abstract The fidelity of quantum circuits (QC) is influenced by several factors, including hardware characteristics, calibration status, and the transpilation process, all of which impact their susceptibility to noise. However, existing methods struggle to estimate and compare the noise performance of different circuit layouts due to fluctuating error rates and the absence of a standardized fidelity metric. In this work, Q‐fid is introduced, a Long Short‐Term Memory (LSTM) based fidelity prediction system accompanied by a novel metric designed to quantify the fidelity of quantum circuits. Q‐fid provides an intuitive way to predict the noise performance of Noisy Intermediate‐Scale Quantum (NISQ) circuits. This approach frames fidelity prediction as a Time Series Forecasting problem to analyze the tokenized circuits, capturing the causal dependence of the gate sequences and their impact on overall fidelity. Additionally, the model is capable of dynamically adapting to changes in hardware characteristics, ensuring accurate fidelity predictions under varying conditions. Q‐fid achieves a high prediction accuracy with an average RMSE of , up to more accurate than the Qiskit transpile tool mapomatic . By offering a reliable method for fidelity prediction, Q‐fid empowers developers to optimize transpilation strategies, leading to more efficient and noise‐resilient quantum circuit implementations.
Abstract The fidelity of quantum circuits (QC) is influenced by several factors, including hardware characteristics, calibration status, and the transpilation process, all of which impact their susceptibility to noise. However, existing methods struggle to estimate and compare the noise performance of different circuit layouts due to fluctuating error rates and the absence of a standardized fidelity metric. In this work, Q‐fid is introduced, a Long Short‐Term Memory (LSTM) based fidelity prediction system accompanied by a novel metric designed to quantify the fidelity of quantum circuits. Q‐fid provides an intuitive way to predict the noise performance of Noisy Intermediate‐Scale Quantum (NISQ) circuits. This approach frames fidelity prediction as a Time Series Forecasting problem to analyze the tokenized circuits, capturing the causal dependence of the gate sequences and their impact on overall fidelity. Additionally, the model is capable of dynamically adapting to changes in hardware characteristics, ensuring accurate fidelity predictions under varying conditions. Q‐fid achieves a high prediction accuracy with an average RMSE of , up to more accurate than the Qiskit transpile tool mapomatic . By offering a reliable method for fidelity prediction, Q‐fid empowers developers to optimize transpilation strategies, leading to more efficient and noise‐resilient quantum circuit implementations.
ABSTRACT Executing quantum circuits (QCs) on noisy intermediate‐scale quantum (NISQ) devices faces significant challenges due to quantum noise that impacts computational reliability. Layout selection presents a promising approach to enhance execution quality by mapping logical qubits to high‐fidelity physical qubits. Nevertheless, traditional approaches struggle to find high‐fidelity layouts mainly because of the difficulties in modeling noise characteristics. In this work, a machine learning‐driven layout optimization framework is introduced. By developing a novel feature set that considers information of circuit structures, backend calibration data and related factors, the trained machine learning (ML) model can accurately predict the fidelity of QCs under different candidate layouts, facilitating selecting high‐fidelity layouts. By evaluating proposed framework on tianyan176‐2 quantum computer using 6486 random QCs and 10 real‐world quantum programs, the results provide three key insights: first, the random forest model demonstrates superior noise modeling capabilities compared to the traditional product model and other ML models; second, the proposed framework significantly improves QC execution fidelity compared with random layout selection and Mapomatic ; furthermore, the SHapley Additive exPlanations (SHAP) analysis is conducted, which reveals strong correlations between features likes liveness and depth with QC execution fidelity, thereby facilitating understanding the random forest model's decision‐making mechanism in layout optimization.
Abstract Fidelity is one of the most valuable and commonly used metrics for assessing the performance of quantum circuits on error-prone quantum processors. Several approaches have been proposed to estimate circuit fidelity without executing it on quantum hardware, but they often face limitations in scalability or accuracy. In this work, we present a comprehensive theoretical framework to predict the fidelity of quantum circuits under depolarizing noise. Building on theoretical results, we propose an efficient fidelity estimation algorithm based on device calibration data. The method is thoroughly validated through simulation and execution on real hardware, demonstrating improved accuracy compared to state-of-the-art alternatives, with enhancements in prediction R 2 ranging from 4.96% to 213.54%. The proposed approach provides a scalable and practical tool for benchmarking quantum hardware, comparing quantum software techniques such as compilation methods, obtaining computation bounds for quantum systems, and guiding hardware design decisions, making it a critical resource for developing and evaluating quantum computing technologies.
Quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era face significant challenges due to inherent noise and limited qubit coherence. Accurate fidelity evaluation of quantum states necessitates multiple repeated measurements to obtain statistical results. But determining the optimal number of measurements remains an open problem due to the dynamic, device-dependent nature of quantum noise. Existing methods either assume prior knowledge of the noise model or inherently employ a fixed measurement strategy, which limits their applicability in practical deployment scenarios. This paper presents AutoFid, an adaptive and noise-aware fidelity measurement framework that automatically determines the number of required tests based on circuit structure and hardware feedback. AutoFid models quantum circuits as Directed Acyclic Graphs and estimates structural complexity via random walks, enabling estimation of measurement effort. It further incorporates transpilation-aware features such as gate fidelity, depth inflation, and crosstalk to refine iteration budgets. During runtime, AutoFid dynamically samples fidelity results and employs an early stopping strategy based on confidence intervals to reduce redundant measurements while preserving accuracy guarantees. We evaluate AutoFid on 18 quantum benchmarks executed on real IBMQ hardware platforms. Experimental results show that AutoFid reduces measurement costs by more than 50% compared to both fixed-shot and learning-based baselines, while consistently maintaining fidelity bias below 0.01. Additional analysis using classical software testing metrics and ablation studies demonstrate its effectiveness, robustness, and adaptability across a wide range of quantum workloads.
The current advancement in quantum computers has been focusing on increasing the number of qubits and enhancing their fidelity. However, the available quantum devices, known as intermediate scale quantum (NISQ) computers, still suffer from different sources of noise that impact their reliability. Thus, practical noise modeling is of great importance in the development of quantum error mitigation approaches. In this article, we propose a machine learning (ML)-based scheme to predict the output fidelity of the quantum circuit executed on NISQ devices. We show the benefit of using graph neural network (GNN)-based models compared to traditional ML-based models in capturing the quantum circuit structure in addition to its gates’ features, which enable characterizing unpredicted quantum circuit errors. We use different metrics to measure the fidelity of the quantum circuit output. Our experimental results using different quantum algorithms executed on IBM Q Guadalupe quantum computer show the high prediction accuracy of our ML reliability models. Our results also show that our models can guide the single-qubit gate rescheduling to improve the output fidelity of the quantum circuit without the need for prior execution of dedicated calibration circuits.
Noisy Intermediate-Scale Quantum (NISQ) devices are promising platforms for practical quantum computing, but their performance is significantly affected by limited qubit availability, gate errors, decoherence, circuit depth, and hardware-dependent noise. Conventional quantum circuits are generally designed and optimized before execution and may not respond effectively to changing hardware conditions during computation. This research proposes a Self-Adaptive Quantum Circuit framework that dynamically modifies quantum circuits according to the current computational resources and noise characteristics of NISQ devices. The proposed framework monitors parameters such as qubit fidelity, gate error rates, circuit depth, execution time, and resource availability, and automatically selects suitable circuit configurations to improve computational performance. Adaptive mechanisms can be employed to reduce unnecessary gates, optimize qubit utilization, and mitigate the impact of hardware noise while maintaining the required computational accuracy. The framework will be evaluated using simulation platforms and available NISQ quantum hardware, comparing adaptive circuits with conventional static circuits using metrics such as accuracy, fidelity, circuit depth, gate count, execution time, and error rate. The expected outcome is a resource-efficient and noise-aware quantum circuit optimization approach capable of improving the reliability and performance of quantum applications on near-term quantum hardware. The proposed research aims to contribute toward more practical, flexible, and hardware-aware quantum computing systems for NISQ-era applications. Keywords: NISQ Computing, Self-Adaptive Quantum Circuits, Quantum Circuit Optimization, Quantum Error Mitigation, Quantum Noise, Quantum Computing.
In the era of noisy intermediate-scale quantum (NISQ) computing, the synergistic collaboration between quantum and classical computing models has emerged as a promising solution for tackling complex computational challenges. Long short-term memory (LSTM), as a popular network for modeling sequential data, has been widely acknowledged for its effectiveness. However, with the increasing demand for data and spatial feature extraction, the training cost of LSTM exhibits exponential growth. In this study, we propose the quantum convolutional long short-term memory (QConvLSTM) model. By ingeniously integrating classical convolutional LSTM (ConvLSTM) networks and quantum variational algorithms, we leverage the variational quantum properties and the accelerating characteristics of quantum states to optimize the model training process. Experimental validation demonstrates that, compared to various LSTM variants, our proposed QConvLSTM model outperforms in terms of performance. Additionally, we adopt a hierarchical tree-like circuit design philosophy to enhance the model’s parallel computing capabilities while reducing dependence on quantum bit counts and circuit depth. Moreover, the inherent noise resilience in variational quantum algorithms makes this model more suitable for spatiotemporal sequence modeling tasks on NISQ devices.