Noisy Intermediate-Scale Quantum (NISQ) devices are constrained by short coherence times and imperfect control, limiting the practical performance of near-term quantum algorithms. While Quantum Architecture Search (QAS) automates circuit design using reinforcement learning, most existing approaches optimize architectures under ideal or fixed average-case noise models. As a result, the discovered circuits often exhibit poor robustness when deployed on realistic hardware subject to noise variability. In this paper, we propose a coevolutionary framework for noise-resistant quantum architecture search. A circuit-designing agent, referred to as the Architect, is trained against an adaptive Hardware-Aware Saboteur (HAS). The HAS models worst-case, yet physically plausible, operating conditions by amplifying backend-derived noise components under a constrained noise amplification budget. The Architect is implemented as a Proximal Policy Optimization agent with a discrete gate alphabet, while the HAS is a separate reinforcement learning agent that selects budget-constrained amplification over single-qubit, two-qubit, and measurement noise channels. Their interaction is mediated by an adversarial environment that stabilizes training via an annealed mixture of nominal and stress-tested performance and a curriculum over the amplification budget. The framework is evaluated on Greenberger–Horne–Zeilinger (GHZ) state preparation for three to five qubits and on small variational quantum eigensolver benchmarks. Compared to a reinforcement learning-based QAS baseline and a hardware-efficient ansatz baseline, the coevolutionary Architect achieves significantly improved robustness under backend-aligned noise amplification and demonstrates stronger generalization to unseen noise families. These results indicate that explicitly training against adaptive, hardware-aligned worst-case noise is an effective strategy for discovering noise-resilient quantum circuits suitable for NISQ-era execution.
Noisy Intermediate-Scale Quantum (NISQ) devices are constrained by short coherence times and imperfect control, limiting the practical performance of near-term quantum algorithms. While Quantum Architecture Search (QAS) automates circuit design using reinforcement learning, most existing approaches optimize architectures under ideal or fixed average-case noise models. As a result, the discovered circuits often exhibit poor robustness when deployed on realistic hardware subject to noise variability. In this paper, we propose a coevolutionary framework for noise-resistant quantum architecture search. A circuit-designing agent, referred to as the Architect, is trained against an adaptive Hardware-Aware Saboteur (HAS). The HAS models worst-case, yet physically plausible, operating conditions by amplifying backend-derived noise components under a constrained noise amplification budget. The Architect is implemented as a Proximal Policy Optimization agent with a discrete gate alphabet, while the HAS is a separate reinforcement learning agent that selects budget-constrained amplification over single-qubit, two-qubit, and measurement noise channels. Their interaction is mediated by an adversarial environment that stabilizes training via an annealed mixture of nominal and stress-tested performance and a curriculum over the amplification budget. The framework is evaluated on Greenberger–Horne–Zeilinger (GHZ) state preparation for three to five qubits and on small variational quantum eigensolver benchmarks. Compared to a reinforcement learning-based QAS baseline and a hardware-efficient ansatz baseline, the coevolutionary Architect achieves significantly improved robustness under backend-aligned noise amplification and demonstrates stronger generalization to unseen noise families. These results indicate that explicitly training against adaptive, hardware-aligned worst-case noise is an effective strategy for discovering noise-resilient quantum circuits suitable for NISQ-era execution.
The key challenge in the noisy intermediate-scale quantum era is finding useful circuits compatible with current device limitations. Variational quantum algorithms (VQAs) offer a potential solution by fixing the circuit architecture and optimizing individual gate parameters in an external loop. However, parameter optimization can become intractable, and the overall performance of the algorithm depends heavily on the initially chosen circuit architecture. Several quantum architecture search (QAS) algorithms have been developed to design useful circuit architectures automatically. In the case of parameter optimization alone, noise effects have been observed to dramatically influence the performance of the optimizer and final outcomes, which is a key line of study. However, the effects of noise on the architecture search, which could be just as critical, are poorly understood. This work addresses this gap by introducing a curriculum-based reinforcement learning QAS (CRLQAS) algorithm designed to tackle challenges in realistic VQA deployment. The algorithm incorporates (i) a 3D architecture encoding and restrictions on environment dynamics to explore the search space of possible circuits efficiently, (ii) an episode halting scheme to steer the agent to find shorter circuits, and (iii) a novel variant of simultaneous perturbation stochastic approximation as an optimizer for faster convergence. To facilitate studies, we developed an optimized simulator for our algorithm, significantly improving computational efficiency in simulating noisy quantum circuits by employing the Pauli-transfer matrix formalism in the Pauli-Liouville basis. Numerical experiments focusing on quantum chemistry tasks demonstrate that CRLQAS outperforms existing QAS algorithms across several metrics in both noiseless and noisy environments.
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 architecture search (QAS) has emerged to automate the design of high-performance quantum circuits under specific tasks and hardware constraints. We propose a noise-aware quantum architecture search (NA-QAS) framework based on variational quantum circuit design. By incorporating a noise model into the training of parameterized quantum circuits (PQCs) , the proposed framework identifies the noise-robust architectures. We introduce a hybrid Hamiltonian ε -greedy strategy to optimize evaluation costs and circumvent local optima. Furthermore, an enhanced variable-depth NSGA-II algorithm is employed to navigate the vast search space, enabling an automated trade-off between architectural expressibility and quantum hardware overhead. The effectiveness of the framework is validated through binary classification and iris multi-classification tasks under a noisy condition. Compared to existing approaches, our framework can search for quantum architectures with superior performance and greater resource efficiency under a noisy condition.
Abstract Noisy Intermediate-Scale Quantum (NISQ) devices are near-term quantum computing devices that inherently suffer from imperfect gate implementation, decoherence, and measurement noise. The main type of variational quantum algorithms(VQAs) is based on Parameterized Quantum Circuits (PQCs), which, however, face practical challenges due to optimization instability of optimization caused by noise and reduced performance. In this paper, we suggest a comprehensive noise-resilient optimization framework for PQCs, which explicitly incorporates noise-resilient circuit architecture design, incorporating noise-aware training strategies into the variational optimization loop. The framework uses circuit structures that are efficient in depth and are hardware compatible in addition to adaptive optimizer choice, stochastic optimization, and averaging as a part of repeated measurement to reduce the impacts of realistic noise. Large-scale experiments are performed with different amounts of qubits, depths of the circuit and noise strengths with realistic noise models. The findings indicate that the convergence rate is faster, the stability is increased, the variability of run-to-run variability is minimized, and other robustness is better than the noise-unaware optimization techniques. Moreover, systematic studies determine optimal operating regions that optimally trade-off expressibility and noise tolerance to offer useful design advice on trusted implementation of VQAs with near-term quantum hardware.
Quantum computing leverages quantum mechanics to address computational problems in ways that differ fundamentally from classical approaches. While current quantum hardware remains error-prone and limited in scale, Variational Quantum Circuits offer a noise-resilient framework suitable for today's devices. The performance of these circuits strongly depends on the underlying architecture of their parameterized quantum components. Identifying efficient, hardware-compatible quantum circuit architectures -- known as Quantum Architecture Search (QAS) -- is therefore essential. Manual QAS is complex and error-prone, motivating efforts to automate it. Among various automated strategies, Reinforcement Learning (RL) remains underexplored, particularly in Quantum Machine Learning contexts. This work introduces RL-QAS, a framework that applies RL to discover effective circuit architectures for classification tasks. We evaluate RL-QAS using the Iris and binary MNIST datasets. The agent autonomously discovers low-complexity circuit designs that achieve high test accuracy. Our results show that RL is a viable approach for automated architecture search in quantum machine learning. However, applying RL-QAS to more complex tasks will require further refinement of the search strategy and performance evaluation mechanisms.
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.