Quantum Circuit Learning (QCL) is one approach to leveraging Noisy-Intermediate-Scale Quantum (NISQ) devices, which have a limited number of qubits and are unable to correct physical errors. While QCL was initially expected to be tolerant of overfitting, intrinsic noise in these devices limits the performance of QCL applications. To address this issue, we propose a circuit configuration that combines data re-uploading with dynamic circuit techniques. We developed a QCL circuit with dynamic circuit integration to mitigate the effects of noise on both the learning and estimation phases. To assess the effectiveness of the proposed method, we implemented the circuit on a classical simulator with realistic noise models and trained it on various toy classification problems. Our results show that the proposed circuit estimates the correct labels of test data more accurately than the original data re-uploading circuit. We also explore possible extensions of the proposed method.
Quantum Circuit Learning (QCL) is one approach to leveraging Noisy-Intermediate-Scale Quantum (NISQ) devices, which have a limited number of qubits and are unable to correct physical errors. While QCL was initially expected to be tolerant of overfitting, intrinsic noise in these devices limits the performance of QCL applications. To address this issue, we propose a circuit configuration that combines data re-uploading with dynamic circuit techniques. We developed a QCL circuit with dynamic circuit integration to mitigate the effects of noise on both the learning and estimation phases. To assess the effectiveness of the proposed method, we implemented the circuit on a classical simulator with realistic noise models and trained it on various toy classification problems. Our results show that the proposed circuit estimates the correct labels of test data more accurately than the original data re-uploading circuit. We also explore possible extensions of the proposed method.
We propose a noise-mitigation quantum simulation strategy for near-term quantum devices based on Quantum Circuit Learning (QCL), which is in particular effective for integrable quantum spin chains. The method trains a shallow variational circuit to approximate a deeper time-evolution circuit by learning the conserved charges and only a small amount of dynamical information in the system. Under realistic noise models, the learned circuit maintains both conserved quantities and dynamical observables significantly closer to their true values than the noisy simulation of the original circuit. We demonstrate, on small-scale prototypes, that QCL can act as an effective, physics-informed error mitigation strategy, producing shorter, more robust circuits without exponential sampling overhead.
Quantum circuit learning (QCL) is a hybrid quantum-classical machine learning technique designed for noisy intermediate-scale quantum (NISQ) devices. In QCL, classical data are encoded into quantum states and processed through parameterized quantum gates, and the resulting quantum measurement outcomes are used to optimize circuit parameters via classical optimization algorithms for machine learning. Sparse identification of nonlinear dynamics (SINDy) is a method for estimating classical nonlinear dynamical systems from time series data. In SINDy, system dynamics is approximately represented as a linear combination of a predefined set of basis functions, with their coefficients sparsely estimated from the time-series data. In this study, we propose sparse identification of quantum Hamiltonian dynamics (SIQHDy), a SINDy-inspired quantum circuit learning algorithm for estimating quantum Hamiltonian dynamics from time-series data. In SIQHDy, quantum Hamiltonian dynamics is approximately represented as a product of basis quantum circuits, with their parameters sparsely estimated from time-series data of quantum measurement outcomes. We numerically demonstrate that SIQHDy can successfully estimate the dynamics of single-, two-, and five-qubit systems and that the advantage of sparsity becomes evident when using time-series data with measurement noise.
While Quantum Machine Learning (QML) holds great potential, its practical realization on Noisy Intermediate-Scale Quantum (NISQ) hardware has been hindered by the limitations of variational quantum circuits (VQCs). Recent evidence suggests that VQCs suffer from severe trainability and noise-related issues, leading to growing skepticism about their long-term viability. However, the possibility of implementing learning models directly at the pulse-control level remains comparatively unexplored and could offer a promising alternative. In this work, we formulate a pulse-based variant of data re-uploading, embedding trainable parameters directly into the native system's dynamics. We benchmark our approach on a simulated superconducting transmon processor with realistic noise profiles. The pulse-based model consistently outperforms its gate-based counterpart, exhibiting higher test accuracy and improved generalization under equivalent noise conditions. Moreover, by systematically increasing noise strength, we show that pulse-level implementations retain higher fidelity for longer, demonstrating enhanced resilience to decoherence and control errors. These results suggest that pulse-native architectures, though less explored, may offer a viable and hardware-aligned path forward for practical QML in the NISQ era.
Hybridizing machine learning (ML) with quantum computing has become an attractive strategy to increase computational power on Noisy Intermediate Scale Quantum (NISQ) devices. Although these can be potentially quantum advantage devices, their qubit numbers and noise are bounded and there are challenges when it comes to practical quantum circuit realization. This work concentrates on the creation of optimized quantum circuits via ML-based strategies, to enhance the performance of the circuits, to reduce the error rate and to accommodate the particular noise profile of the hardware of NISQ devices. The resulting suboptimal mappings with little or no consideration of noise and hardware topology in traditional quantum compilers can ruin the fidelity of quantum algorithm. Leveraging machine learning, and specifically reinforcement and supervised learning methods, our method learns to optimize compilation strategy for specific quantum hardware architecture. The paper proposes a framework that uses machine learning models to predict qubit mappings, gate decompositions, and transpilation paths that minimize gate depth and error accumulation. We also investigate transfer learning methods for propagating the learned compilation strategies to novel NISQ machines. Such methods are empirically validated on both simulated and real quantum hardware backends, providing evidence of circuit depth, execution time and error reductions. The results show that ML-assisted compilation can achieve better success probabilities for benchmark quantum algorithms like VQE and QAOA than conventional compilers. Furthermore, the work examines the interpretability of ML models providing hardware-aware optimization recommendations and understanding the noise patterns and methods of error correction. Our work highlights the key role of ML for transferring between theoretical quantum algorithms and their practical NISQ implementations. This integration boosts the immediate applicability of existing quantum devices and provides a basis for adaptive and scalable likelihood-based compilation for the era of fault-tolerant quantum computation. Our results motivate further investigation of hybrid classical-quantum workflows, in which ML iteratively improves compilation in accordance with changing hardware noise and capabilities. By improving quantum circuit compilation onNISQ computers, this work is taking steps toward the realization of quantum advantage in application scenarios such as optimization, cryptography, and quantum simulation without needing to wait for fully fault-tolerant large-scale quantum computers.
This paper presents Adaptive Quantum-Enhanced Learning (AQEL), a novel hybrid quantum-classical machine learning framework designed to address critical challenges in the Noisy Intermediate-Scale Quantum (NISQ) era. AQEL integrates four adaptive components: Adaptive Quantum Feature Mapping (AQFM), a Noise-Aware Variational Quantum Circuit (NAVQC), a Classical Meta-Optimizer with Quantum Feedback (CMOQF), and a Secure Scalable Data Interface (SSDI). Unlike static hybrid approaches, AQEL is designed to dynamically adapt its quantum architecture based on performance metrics and gradient behavior. We evaluate the framework's effectiveness on a benchmark binary classification task via high-fidelity simulation. The results demonstrate high classification accuracy, with the model automatically adapting its circuit depth during training to navigate the expressibility-trainability trade-off. Comprehensive ablation studies and a simulated noise resilience analysis validate the potential of AQEL's adaptive components, indicating its promise for developing robust QML applications.
Quantum Machine Learning (QML) is an exciting frontier applying the philosophy of quantum computation to classical machine learning for improved learning performance of complicated tasks. However, QML is greatly challenged by realistic issues like noisy quantum measurements and limited labeled quantum data caused by the high cost and technological limitations. This paper explores the novel transfer-learning mechanisms to tackle these issues, which allow the reuse of the knowledge from related quantum tasks for improving model performance and generalization in resource-limited scenarios. Transfer learning, one of the great and successful concepts of classical machine learning, is to transfer the learned parameters or features from the source domain (or task) to the target domain (or task). We further develop and investigate, in quantum, a methodology where parameterized quantum circuits (PQCs) learnt on a source quantum data set have certain parts partially re-used or fine-tuned for a target task, given limited amount of target data. This strategy also avoids large quantum training runs that are not scalable on state-of-the-art noisy intermediate-scale quantum (NISQ) processors. The suggested methods are layerwise quantum adaptation, quantum kernel reuse and hybrid quantum-classical feature transfer. We investigate the robustness of these mechanisms in presence of noise by simulating quantum circuits using depolarizing and readout noise models. Moreover, we show that pretraining of quantum models on synthetic (or simulated) data sets (which are generated using larger, less noisy systems) can be instrumental in learning on real, small-sized quantum devices. Experiment results in quantum classification tasks demonstrate the effectiveness of transfer learning in boosting the accuracy, accelerating the convergence, and lowering the quantum circuit depth of successfully trained model. It is interesting to mention that transfer using layer-wise models became very resistant to quantum noise, as its performance did not degrade significantly for noisy inputs. These results demonstrate that transfer learning can overcome the scarce quantum data and suppress the harmful impact of measurement noise. This work presents a potential venue for enhancing the applicability of QML in the NISQ era. By transferring knowledge from theoretical quantum learning models to actual quantum hardware constraints, transfer learning allows quantum algorithms to learn faster and generalize better across tasks.