Variational quantum algorithm is a leading approach for quantum chemistry and many-body physics on noisy intermediate-scale quantum devices. The performance depends strongly on the structure of the parameterized quantum circuits. Quantum architecture search (QAS) can automate ansatz design. However, it requires repeated training and evaluation of many candidate circuits, leading to high evaluation cost. In this work, we propose a noise-aware quantum architecture search framework based on ZX-calculus topological reuse (ZX-QAS). The framework encodes the search space with a ternary Gray-code mapping and integrates a noise-aware quantum neural network with a ZX-calculus topological reuse mechanism. The effectiveness of the framework is validated through ground-state energy estimation tasks and one-dimensional transverse-field Ising model tasks under noisy conditions. The results show that the ZX-QAS exhibits stable convergence and remarkably reduces the cost of expensive evaluations under noisy conditions.
Variational quantum algorithm is a leading approach for quantum chemistry and many-body physics on noisy intermediate-scale quantum devices. The performance depends strongly on the structure of the parameterized quantum circuits. Quantum architecture search (QAS) can automate ansatz design. However, it requires repeated training and evaluation of many candidate circuits, leading to high evaluation cost. In this work, we propose a noise-aware quantum architecture search framework based on ZX-calculus topological reuse (ZX-QAS). The framework encodes the search space with a ternary Gray-code mapping and integrates a noise-aware quantum neural network with a ZX-calculus topological reuse mechanism. The effectiveness of the framework is validated through ground-state energy estimation tasks and one-dimensional transverse-field Ising model tasks under noisy conditions. The results show that the ZX-QAS exhibits stable convergence and remarkably reduces the cost of expensive evaluations under noisy conditions.
Abstract With the ongoing advances in noisy intermediate-scale quantum hardware, variational quantum algorithms have demonstrated significant potential in a range of quantum applications. However, obtaining high-performance, shallow-parameterized quantum circuits typically requires repeated optimization of the gate parameters over a large set of candidate circuits, resulting in prohibitively high evaluation costs. To address this challenge, this study proposes a novel predictor-based quantum architecture search (PQAS-ZX) method that leverages ZX-calculus. In this approach, a quantum circuit is first represented as a ZX diagram that supports multi-step equivalent simplifications at the diagram level. By applying these equivalence transformations, multiple circuit variants that share the same performance metric are generated, thereby significantly expanding the training dataset and enhancing the ability of the predictor to manage diverse circuit structures. ZX diagrams offer more flexible characterizations of multi-qubit entanglement and phase interactions, as well as higher-level equivalent transformations, compared with the state-of-the-art predictor-based quantum architecture search with graph measures (PQAS-GM). Numerical simulations of three variational quantum eigensolver tasks, namely the transverse-field Ising, Heisenberg, and BeH2 molecular models, demonstrated that PQAS-ZX required only approximately 80.9%, 82.9%, and 76.1% of the queries required by PQAS-GM, respectively, to achieve the same probability of reaching the target ground-state energy. These results highlight the advantage of using ZX diagrams to identify high-quality circuits efficiently and alleviate the evaluation burden of quantum architecture searches.
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.
Designing efficient variational quantum algorithms (VQAs) is crucial for transforming the theoretical advantages of quantum algorithms into practical applications. In this context, quantum architecture search (QAS) has been introduced to automate the search and design of VQAs. However, current mainstream QAS algorithms typically perform both global and local searches simultaneously, which can result in high search space complexity and optimization challenges. In this paper, we propose a hierarchical quantum architecture search framework based on a two-stage search structure. In the first stage, global exploration of the overall quantum circuit structure is performed, while in the second stage, local optimization of quantum gate selection is carried out. We provide a numerical analysis of the theoretical advantages of the proposed framework in reducing the search space. To evaluate practical performance, we conduct experiments on quantum chemistry tasks with different algorithm combinations integrated into the framework. The results demonstrate the effectiveness of the hierarchical search structure in automating quantum circuit design.
Abstract Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS cannot only alleviate the influence of quantum noise and barren plateaus but also outperforms VQAs with pre-selected ansatze.
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.
Variational Quantum Algorithms (VQAs) are central to the utility of Noisy Intermediate-Scale Quantum (NISQ) devices, but their performance is critically dependent on the design of the parameterized quantum circuit (PQC) ansatz. Manual design of these circuits is a complex, non-trivial task requiring significant expertise. To address this, Quantum Architecture Search (QAS) has emerged to automate the discovery of optimal circuit structures. However, many QAS methods incur substantial computational costs by requiring full training of candidate circuits. In this paper, we introduce QAS via iterative elimination, a novel framework that efficiently identifies high-performing circuits. Our method begins with a large, randomly generated population of circuits and iteratively prunes this set based on a composite score that evaluates both immediate performance and trainability. We introduce a dynamic weighting scheme that initially prioritizes trainability to avoid barren plateaus and gradually shifts focus to performance as the search progresses. We validate our approach on Variational Quantum Eigensolver (VQE) tasks for the 6-qubit Transverse Field Ising Model and the 5-qubit Heisenberg model. Experimental results show that our method, particularly with the dynamic weighting, discovers circuits that are competitive with those found by state-of-the-art training-free QAS methods, demonstrating a powerful balance between search efficiency and solution quality.