Although quantum computers will not necessarily transform ML in general, they hold immense potential for specific uses, such as dealing with high-dimensional data and complex correlations that cannot be effectively modeled using classical systems. It is, however, difficult to design efficient quantum circuits, especially with the existing Noisy Intermediate-Scale Quantum (NISQ) hardware that has constraints including decoherence, a limited number of qubits, and gate fidelity limitations. The authors propose an Adaptive Quantum Circuit Architecture Search (AQCAS) framework whose structure and parameters are optimized dynamically to improve the performance of the task-specific ML. The framework integrates quantum circuit encoding, a hybrid search strategy that is based on reinforcement learning and evolutionary algorithms, and a task-specific adaptivity mechanism. Benchmark datasets were used for experimental evaluations of both simulation using Qiskit Aer and classical optimization using the Python frameworks TensorFlow and PennyLane. Fixed and standard variational quantum circuits were compared with performance measurements like the classification accuracy, circuit depth, convergence speed, and computational efficiency. The results show that the accuracy of AQCAS can be raised to 94.1%, and the circuit depth is lowered at the same time. It converged rapidly and is computationally efficient. Ablation studies show that both hybrid search and adaptivity mechanisms help to enhance generalization and stability under NISQ constraints. The results demonstrate the potential of AQCAS as a practical approach to using adaptive quantum circuits in specialized ML applications, and lay the groundwork for future development of scaling to larger devices, implementing noise-aware optimization, and integrating with hybrid quantum-classical ML pipelines.
Although quantum computers will not necessarily transform ML in general, they hold immense potential for specific uses, such as dealing with high-dimensional data and complex correlations that cannot be effectively modeled using classical systems. It is, however, difficult to design efficient quantum circuits, especially with the existing Noisy Intermediate-Scale Quantum (NISQ) hardware that has constraints including decoherence, a limited number of qubits, and gate fidelity limitations. The authors propose an Adaptive Quantum Circuit Architecture Search (AQCAS) framework whose structure and parameters are optimized dynamically to improve the performance of the task-specific ML. The framework integrates quantum circuit encoding, a hybrid search strategy that is based on reinforcement learning and evolutionary algorithms, and a task-specific adaptivity mechanism. Benchmark datasets were used for experimental evaluations of both simulation using Qiskit Aer and classical optimization using the Python frameworks TensorFlow and PennyLane. Fixed and standard variational quantum circuits were compared with performance measurements like the classification accuracy, circuit depth, convergence speed, and computational efficiency. The results show that the accuracy of AQCAS can be raised to 94.1%, and the circuit depth is lowered at the same time. It converged rapidly and is computationally efficient. Ablation studies show that both hybrid search and adaptivity mechanisms help to enhance generalization and stability under NISQ constraints. The results demonstrate the potential of AQCAS as a practical approach to using adaptive quantum circuits in specialized ML applications, and lay the groundwork for future development of scaling to larger devices, implementing noise-aware optimization, and integrating with hybrid quantum-classical ML pipelines.
Quantum Machine Learning (QML) utilizes the principles of quantum computations to improve the performance of learning by the use of parameterized quantum circuits and variational algorithms. But despite the qubit number, circuit depth, and patterns of entanglement, creating the optimal quantum circuit architectures for particular machine learning applications remains an important problem. Fixed circuits, or classical Neural Architecture Search (NAS), are not enough to transfer knowledge to other tasks and/or to search the quantum search space efficiently. In this context, this paper presents an Adaptive Quantum Circuit Architecture Search (AQ CAS) framework that dynamically generates and optimizes quantum circuit architectures using a hybrid search algorithm that integrates reinforcement learning, evolutionary operators, and evaluation without gradient. AQ CAS involves a task-specific feedback mechanism to eliminate those candidates that perform poorly and concentrate the search on those candidates with good performance. The framework is evaluated on benchmark data sets such as Iris, synthetic classical ML tasks, and hybrid quantum-classical tasks, and compared to fixed circuits, existing quantum NAS methods, and classical machine learning models. Experimental results indicate that AQ CAS can achieve higher accuracy for the task, using fewer qubits, fewer layers, and less training time, which further shows the need for adaptivity and task specialization. These results inform us about efficient hybrid classical-quantum pipelines, and give a glimpse of the directions that adaptive architecture search might take in enabling scalable and practical QML deployment.
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.
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.
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.
A significant hurdle in the noisy intermediate-scale quantum (NISQ) era is identifying functional quantum circuits. These circuits must also adhere to the constraints imposed by current quantum hardware limitations. Variational quantum algorithms (VQAs), a class of quantum-classical optimization algorithms, were developed to address these challenges in the currently available quantum devices. However, the overall performance of VQAs depends on the initialization strategy of the variational circuit, the structure of the circuit (also known as ansatz), and the configuration of the cost function. Focusing on the structure of the circuit, in this thesis, we improve the performance of VQAs by automating the search for an optimal structure for the variational circuits using reinforcement learning (RL). Within the thesis, the optimality of a circuit is determined by evaluating its depth, the overall count of gates and parameters, and its accuracy in solving the given problem. The task of automating the search for optimal quantum circuits is known as quantum architecture search (QAS). The majority of research in QAS is primarily focused on a noiseless scenario. Yet, the impact of noise on the QAS remains inadequately explored. In this thesis, we tackle the issue by introducing a tensor-based quantum circuit encoding, restrictions on environment dynamics to explore the search space of possible circuits efficiently, an episode halting scheme to steer the agent to find shorter circuits, a double deep Q-network (DDQN) with an ε-greedy policy for better stability. The numerical experiments on noiseless and noisy quantum hardware show that in dealing with various VQAs, our RL-based QAS outperforms existing QAS. Meanwhile, the methods we propose in the thesis can be readily adapted to address a wide range of other VQAs.
Quantum computing has emerged as one of the most influential paradigms capable of transforming computational processes that struggle under classical limitations. In recent years, hybrid quantum–classical algorithms have gained significant attention for their ability to leverage the strengths of both architectures, especially within the Noisy Intermediate-Scale Quantum (NISQ) era. This research paper explores a quantum-enhanced optimization framework designed to improve machine learning model performance through parameterized quantum circuits (PQCs). The goal of this study is to provide a practical, scalable approach that can be integrated into existing machine learning workflows without requiring fully developed fault-tolerant quantum hardware. The proposed framework uses classical preprocessing to encode data into quantum states, followed by quantum transformations that explore richer feature spaces due to quantum superposition and entanglement. A classical optimizer then updates the quantum circuit parameters based on measurement results, forming an end-to-end feedback loop. This hybrid model aims to demonstrate improved optimization efficiency, reduction in loss-function plateaus, and better generalization for specific ML tasks involving small to medium-sized datasets. This paper provides a comprehensive investigation of methodology, literature background, and experimental reasoning with statistical analysis, along with conceptual diagrams illustrating system architecture and circuit layouts. The outcomes further strengthen the viability of hybrid quantum–classical computation as a promising direction for advanced machine learning systems.