Quantum computers, currently in the noisy intermediate-scale quantum (NISQ) era, have started to provide scientists with a novel tool to explore quantum physics and chemistry. While several electronic systems have been extensively studied, Frenkel excitons, as prototypical optical excitations, remain among the less-explored applications. Here, we first use variational quantum deflation to calculate the eigenstates of the Frenkel Hamiltonian and evaluate the observables based on the oscillator strength for each eigenstate. Furthermore, using NISQ quantum computers requires performing error mitigation techniques alongside simulations. To deal with noisy qubits, we developed a deep-learning-based framework combined with a post-selection technique to learn the noise pattern and mitigate the error. Our mitigation methods work well and outperform the conventional post-selection and remain valid on real hardware.
Quantum computers, currently in the noisy intermediate-scale quantum (NISQ) era, have started to provide scientists with a novel tool to explore quantum physics and chemistry. While several electronic systems have been extensively studied, Frenkel excitons, as prototypical optical excitations, remain among the less-explored applications. Here, we first use variational quantum deflation to calculate the eigenstates of the Frenkel Hamiltonian and evaluate the observables based on the oscillator strength for each eigenstate. Furthermore, using NISQ quantum computers requires performing error mitigation techniques alongside simulations. To deal with noisy qubits, we developed a deep-learning-based framework combined with a post-selection technique to learn the noise pattern and mitigate the error. Our mitigation methods work well and outperform the conventional post-selection and remain valid on real hardware.
Quantum computing represents one of the most transformative technological developments of the twenty-first century. However, the practical realization of its potential on current Noisy Intermediate-Scale Quantum (NISQ) hardware remains severely constrained by noise and computational errors that arise from the inherent fragility of quantum states. This paper presents a systematic comparative analysis of three prominent quantum error mitigation techniques, partial quantum error correction (QEC), zero-noise extrapolation (ZNE), and deep learning-based noise prediction, and develops a predictive assessment of a proposed hybrid framework that integrates all three into a unified system. Through a thorough review of twelve peer-reviewed publications spanning 2017 to 2026, we establish what each technique has accomplished individually and to what degree of accuracy. We identify a critical and previously unaddressed gap: no existing work has systematically combined these three complementary approaches. Drawing on documented performance trends and theoretical complementarity, we predict that a well-designed hybrid framework could achieve classification accuracies of 85 to 90 percent on standard quantum machine learning benchmarks, compared to only 60 to 65 percent without error mitigation. These predictions are grounded in peer-reviewed experimental data. Our analysis provides a clear evidence-based roadmap for future experimental validation and establishes a strong foundation for the development of practical, resource-efficient quantum machine learning systems on near-term hardware. CCS Concepts: Hardware → Quantum computation; • Computing methodologies → Machine learning; • Theory of computation → Quantum complexity theory
At the intersection of two promising technologies, Machine Learning (ML) and Quantum Computing (QC), Quantum Machine Learning (QML) emerges. However, while QC is still in an early stage of development, QML is even more so. In this study, the Auto-MPG dataset was selected for its dimension, making it suitable for QML tasks within the current limitations of quantum hardware, especially regarding qubit requirements. Unlike classification problems that dominate the literature, regression problems have only been explored theoretically. This study aims to bridge that gap by applying QML techniques to a practical regression task. To this end, a preliminary analysis was conducted using a classical model as a reference point for subsequent evaluation. The quantum experimentation was performed in two phases: first, the model was trained in an ideal simulator to determine the model's best hyperparameters. Then, these best models were run in a noisy simulator using quantum error mitigation techniques. The results show how the Variational Quantum Algorithms (VQA) outperforms classical Extreme Gradient Boosting and the methods used to mitigate errors in quantum hardware are effective, as they achieve comparable results between the noisy simulator and the perfect simulator. This brings closer the possible applications in NISQ current devices.
Minimizing the effect of noise is essential for quantum computers. The conventional method to protect qubits against noise is through quantum error correction. However, for current quantum hardware in the so-called noisy intermediate-scale quantum (NISQ) era, noise presents in these systems and is too high for error correction to be beneficial. Quantum error mitigation is a set of alternative methods for minimizing errors, including error extrapolation, probabilistic error cancellation, measurement error mitigation, subspace expansion, symmetry verification, virtual distillation, etc. The requirement for these methods is usually less demanding than error correction. Quantum error mitigation is a promising way of reducing errors on NISQ quantum computers. This paper gives a comprehensive introduction to quantum error mitigation. The state-of-art error mitigation methods are covered and formulated in a general form, which provides a basis for comparing, combining and optimizing different methods in future work.
This work focuses on the design and evaluation of Hybrid Quantum Neural Networks (HQNNs) that integrate the computational advantages of quantum circuits with the learning flexibility of classical deep neural networks to enhance machine learning performance in the Noisy Intermediate-Scale Quantum (NISQ) era.The study investigates two prominent HQNN architectures, namely Quantum Convolutional Neural Networks (QCNNs) and Quanvolutional Neural Networks (QuanNNs), with an emphasis on their robustness under realistic quantum noise conditions.It was evaluated using image classification tasks.Under various quantum noise channels, including Bit Flip, Phase Flip, Phase Damping, Amplitude Damping, and Depolarizing Noise, were introduced to reflect realistic hardware conditions.To reduce noise-induced performance loss, error mitigation techniques such as Zero-Noise Extrapolation, Probabilistic Error Cancellation, and Measurement Error Mitigation were applied.In addition, an AI-assisted adaptive framework is proposed to automatically recommend suitable mitigation strategies based on observed noise behavior and circuit characteristics.Experimental results indicate that QuanNN exhibits strong robustness under lowto-moderate noise levels and maintains stable performance even under high bit-flip noise, while QCNN demonstrates occasional performance gains under specific noisy configurations but degrades more rapidly as noise intensity increases.The proposed AI-guided mitigation mechanism further enhances model stability across varying noise regimes.This study provides practical insights into architecture selection, noise resilience, and intelligent error mitigation integration, offering a scalable framework for deploying HQNNs in real-world NISQ-era quantum machine learning applications.
In the past few years, the first commercially available quantum computers have emerged, in an early stage of development, the so-called Noisy Intermediate-Scale Quantum (NISQ) era. Although these devices are still very prone to errors of different natures, they also have shown to deal successfully with small computational problems. Nowadays, one of the challenges in quantum computation is exactly to be able to show that quantum computers are useful, whereby mitigating the effects of the faulty hardware is pivotal. Recently, a wide range of quantum error mitigation techniques have been proposed and successfully implemented, alternative to quantum error correction codes. Herein, we discuss several types of noise in a quantum computer and techniques available to mitigate them, as well as their limitations and conditions of applicability. We also suggest an hierarchy for them, towards the conception of a layered software framework of error mitigation techniques, and implement some of them in a quantum simulation of the Heisenberg model on an IBM quantum computer, improving the fidelity of the simulation by 2.8x.
Quantum computing offers exponential advantages for problems in cryptography, optimization, and quantum simulation, yet the practicality of current Noisy Intermediate-Scale Quantum (NISQ) devices is severely constrained by decoherence, gate imperfections, and measurement noise. Conventional quantum error correction (QEC) codes such as Steane and surface codes impose large qubit overheads and require high-fidelity logical operations, making them unsuitable for near-term hardware. To address this gap, this work introduces ML-QEM, an adaptive quantum error mitigation framework that employs machine learning to identify and correct dominant Pauli error patterns in real time using only 3–5 ancillary qubits. By integrating Qiskit Aer noise modeling with a lightweight TensorFlow-based classifier, ML-QEM learns device-specific noise characteristics and applies targeted recovery operations with minimal latency. Evaluations across depolarizing, amplitude damping, and thermal relaxation noise models demonstrate a 28.4% average fidelity improvement, 60–84% lower overhead than Steane and surface codes, and a classical processing delay of only 21.7 ms. ML-QEM thus provides an effective and resource-efficient pathway toward enhancing quantum computation fidelity in the NISQ era.