To develop a strategy to design VQC in an automated way, i.e. quantum architecture search (QAS), some researchers Considering the noise level limit, one crucial aspect have turned their attention to the classical Neural Architecture for quantum machine learning is to design a highperforming Search (NAS) framework. NAS focuses on automating variational quantum circuit architecture the design of neural network structures [Elsken et al., with small number of quantum gates. As the classical 2019], but often grapple with the challenge of evaluating a neural architecture search (NAS), quantum architecture vast number of possible network architectures. The Monte search methods (QAS) employ methods Carlo Tree Search (MCTS) algorithm addresses this issue by like reinforcement learning, evolutionary algorithms iteratively exploring and evaluating segments of the search and supernet optimization to improve the space, thereby identifying promising neural network structures search efficiency. In this paper, we propose a novel without exhaustive enumeration [Silver et al., 2016; qubit-wise architecture search (QWAS) method, Wang et al., 2020]. However, the efficiency of the search is which progressively search one-qubit configuration significantly influenced by the manually predefined action per stage, and combine with Monte Carlo Tree space before the tree construction. To address this issue, Search algorithm to find good quantum architectures [Wang et al., 2021] proposed an improved MCTS-based algorithm by partitioning the search space into several called Latent Action Neural Architecture Search good and bad subregions. The numerical experimental (LaNAS) that learns a latent action space that best fits the results indicate that our proposed method can problem to be solved.

Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is challenging -- the design space scales exponentially with circuit size, and there are few well-supported guiding principles for QML circuit design. Although recent Quantum Circuit Search (QCS) methods attempt to search for performant QML circuits that are also robust to hardware noise, they directly adopt designs from classical Neural Architecture Search (NAS) that are misaligned with the unique constraints of quantum hardware, resulting in high search overheads and severe performance bottlenecks. We present \'Eliv\'agar, a novel resource-efficient, noise-guided QCS framework. \'Eliv\'agar innovates in all three major aspects of QCS -- search space, search algorithm and candidate evaluation strategy -- to address the design flaws in current classically-inspired QCS methods. \'Eliv\'agar achieves hardware-efficiency and avoids an expensive circuit-mapping co-search via noise- and device topology-aware candidate generation. By introducing two cheap-to-compute predictors, Clifford noise resilience and Representational capacity, \'Eliv\'agar decouples the evaluation of noise robustness and performance, enabling early rejection of low-fidelity circuits and reducing circuit evaluation costs. Due to its resource-efficiency, \'Eliv\'agar can further search for data embeddings, significantly improving performance. Based on a comprehensive evaluation of \'Eliv\'agar on 12 real quantum devices and 9 QML applications, \'Eliv\'agar achieves 5.3% higher accuracy and a 271$\times$ speedup compared to state-of-the-art QCS methods.

Quantum computing continues to advance at a rapid pace, but one challenge that holds the field back is mitigating the noise that plagues quantum machines. This leads to much higher error rates compared to classical computers. This noise is often caused by imperfect control signals, interference from the environment, and unwanted interactions between qubits, which are the building blocks of a quantum computer. Performing computations on a quantum computer involves a "quantum circuit," which is a series of operations called quantum gates. These quantum gates, which are mapped to the individual qubits, change the quantum states of certain qubits, which then perform the calculations to solve a problem.

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse-level noise-adaptive compilation. However, limited research efforts have explored a higher level of optimization by making the quantum circuits themselves resilient to noise. We propose QuantumNAS, a comprehensive framework for noise-adaptive co-search of the variational circuit and qubit mapping. Variational quantum circuits are a promising approach for constructing QML and quantum simulation. However, finding the best variational circuit and its optimal parameters is challenging due to the large design space and parameter training cost. We propose to decouple the circuit search and parameter training by introducing a novel SuperCircuit. The SuperCircuit is constructed with multiple layers of pre-defined parameterized gates and trained by iteratively sampling and updating the parameter subsets (SubCircuits) of it. It provides an accurate estimation of SubCircuits performance trained from scratch. Then we perform an evolutionary co-search of SubCircuit and its qubit mapping. The SubCircuit performance is estimated with parameters inherited from SuperCircuit and simulated with real device noise models. Finally, we perform iterative gate pruning and finetuning to remove redundant gates. Extensively evaluated with 12 QML and VQE benchmarks on 14 quantum computers, QuantumNAS significantly outperforms baselines. For QML, QuantumNAS is the first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class classification accuracy on real QC. It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD. We also open-source TorchQuantum (https://github.com/mit-han-lab/torchquantum) for fast training of parameterized quantum circuits to facilitate future research.