Quantum neural networks are promising for a wide range of applications in the Noisy Intermediate-Scale Quantum era. As such, there is an increasing demand for automatic quantum neural architecture search. We tackle this challenge by designing a quantum circuits metric for Bayesian optimization with Gaussian process. To this goal, we propose a new quantum gates distance that characterizes the gates' action over every quantum state and provide a theoretical perspective on its geometrical properties. Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems including training a quantum generative adversarial network, solving combinatorial optimization in the MaxCut problem, and simulating quantum Fourier transform. Our method can be extended to characterize behaviors of various quantum machine learning models.
Quantum neural networks are promising for a wide range of applications in the Noisy Intermediate-Scale Quantum era. As such, there is an increasing demand for automatic quantum neural architecture search. We tackle this challenge by designing a quantum circuits metric for Bayesian optimization with Gaussian process. To this goal, we propose a new quantum gates distance that characterizes the gates' action over every quantum state and provide a theoretical perspective on its geometrical properties. Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems including training a quantum generative adversarial network, solving combinatorial optimization in the MaxCut problem, and simulating quantum Fourier transform. Our method can be extended to characterize behaviors of various quantum machine learning models.
In the era of Noisy Intermediate-Scale Quantum (NISQ) computing, traditional quantum algorithms face the challenges of limited number of qubits, noise and decoherence. In order to address these issues, we propose a Quantum Architecture Search (QAS) method driven by Bayesian Optimization (BO), which is applied to variational quantum algorithms. In this work, QAS is regarded as a fixed-scale sampling problem. We innovatively propose a quantum gate pool and use a parameterized probabilistic model to dynamically determine the optimal quantum gate for each position in the quantum circuit, thus optimizing the circuit structure. Through using a gradient-free BO method based on radial basis function, we adaptively design end-to-end quantum circuits, significantly reducing circuit depths and improving computational accuracy. We conducted experiments on ground state energy estimation in quantum chemistry and combinatorial optimization problem. The experimental results show that our method is significantly superior to traditional methods and other meta-heuristic search methods in accuracy and efficiency. Our method not only reduces the depth of quantum circuits by up to 85% under a certain accuracy, but also improves the accuracy rate to nearly 100% in combinatorial optimization problem. This provides a powerful and efficient tool for designing optimal quantum circuits and promotes the practical application of quantum algorithms in the NISQ era.
There is increasing interest in the area of quantum computing, but using and developing new quantum algorithms, often represented as quantum circuits, is arduous and requires designers with a wide area of experience. The exploration of techniques for the automatic design of quantum algorithms, i.e., quantum architecture search, is therefore of high importance. This work explores the use of neural networks for quantum architecture search, since neural networks have proven quite successful in various complex search domains, and current approaches in quantum architecture search make very restricted use of them. There are many different domains in which quantum architecture search can be applied. They can be used to compile unitaries into quantum circuits, develop ansätze for variational quantum circuits, or develop new quantum algorithms by applying it to input output pairs of one task one wishes to solve. The focus in this thesis is on the task of quantum state preparation, a very important task in the field of quantum computing, as it is a subroutine in a large number of quantum algorithms like quantum machine learning and Hamiltonian simulation. This thesis approaches the task of quantum architecture search first by establishing the importance of the structure of quantum circuits. This is done with a large-scale study of the influence different circuit structure parameters, such as qubits and depth, have on the performance on variational quantum machine learning. During this research, four guidelines for designing circuits for variational quantum machine learning were established. DeepQPrep, a neural network-augmented deductive search for the automatic design of quantum circuits for quantum state preparation, was proposed. It is able to create non-parameterized quantum circuits to solve 76.9% of a restrictive set of quantum states with quantum circuits that were much shallower than the ones found in the ground-truth data. The model was trained on a dataset created in this thesis as none were available at the time. During the exploration of extensions of DeepQPrep to enable it to solve more arbitrary quantum state preparation problems without the need for labeled data, different challenges became apparent. A study on the difficulty of training neural networks to create parameterized quantum circuits using reinforcement learning found that training is hampered by the barren plateau problem of quantum computing. It shows that the variance of the gradient of the quantum circuit output with respect to its parameters goes exponentially to zero. A study showed that this problem makes the application of neural networks to encode quantum circuits highly challenging when no intermediate quantum state information is used. This culminated in DeepQPrepGrad, which combines parameterized circuits, the inductive search of DeepQPrep, and the reinforcement learning-based approach with more compact representations. By combining these parts with the gradients of quantum circuits, an approach that is able to create parameterized quantum circuits for arbitrary quantum states was obtained. The approach was evaluated on 20000 arbitrary 3-qubit quantum states without the need for any labeled data, resulting in a mean minimal similarity of 0.97 alleviated the found challenges to a large degree. This thesis explored key challenges faced when training neural networks to encode quantum circuits. These challenges are of paramount importance as they potentially affect a wide range of tasks in the field of quantum computing to which neural networks could be applied. Lastly, DeepQPrepGrad provides a significant step forward for the creation of quantum circuits using neural networks. DeepQPrepGrad makes great progress in solving the challenges highlighted above and has the potential to be applicable in a wide range of quantum architecture search tasks, such as variational eigensolver and variational quantum machine learning.
The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both fields. Among QML approaches, variational quantum circuits (VQCs), also known as quantum neural networks (QNNs), have shown promise both empirically and theoretically. However, their broader adoption is hindered by reliance on quantum hardware during inference. Hardware imperfections and limited access to quantum devices pose practical challenges. To address this, the Quantum-Train (QT) framework leverages the exponential scaling of quantum amplitudes to generate classical neural network parameters, enabling inference without quantum hardware and achieving significant parameter compression. Yet, designing effective quantum circuit architectures for such quantum-enhanced neural programmers remains non-trivial and often requires expertise in quantum information science. In this paper, we propose an automated solution using differentiable optimization. Our method jointly optimizes both conventional circuit parameters and architectural parameters in an end-to-end manner via automatic differentiation. We evaluate the proposed framework on classification, time-series prediction, and reinforcement learning tasks. Simulation results show that our method matches or outperforms manually designed QNN architectures. This work offers a scalable and automated pathway for designing QNNs that can generate classical neural network parameters across diverse applications.
Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS) on Noisy Intermediate-Scale Quantum (NISQ) devices. QAS is designed to optimize quantum circuits for Variational Quantum Algorithms (VQAs). Most QAS algorithms tightly couple the search space and search algorithm, typically requiring the evaluation of numerous quantum circuits, resulting in high computational costs and limiting scalability to larger quantum circuits. Predictor-based QAS algorithms mitigate this issue by estimating circuit performance based on structure or embedding. However, these methods often demand time-intensive labeling to optimize gate parameters across many circuits, which is crucial for training accurate predictors. Inspired by the classical neural architecture search algorithm A r c h 2 v e c , we investigate the potential of unsupervised representation learning for QAS without relying on predictors. Our framework decouples unsupervised architecture representation learning from the search process, enabling the learned representations to be applied across various downstream tasks. Additionally, it integrates an improved quantum circuit graph encoding scheme, addressing the limitations of existing representations and enhancing search efficiency. This predictor-free approach removes the need for large labeled datasets. During the search, we employ REINFORCE and Bayesian Optimization to explore the latent representation space and compare their performance against baseline methods. We further validate our approach by executing the best-discovered MaxCut circuits on IBM's ibm_sherbrooke quantum processor, confirming that the architectures retain optimal performance even under real hardware noise. Our results demonstrate that the framework efficiently identifies high-performing quantum circuits with fewer search iterations.
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data. Simultaneously, quantum computing (QC) promises to address classically intractable problems via quantum parallelism, motivating research in quantum machine learning (QML). Among QML techniques, quantum autoencoders show promise for compressing high-dimensional quantum and classical data. However, designing effective quantum circuit architectures for quantum autoencoders remains challenging due to the complexity of selecting gates, arranging circuit layers, and tuning parameters.
This paper proposes a neural architecture search (NAS) framework that automates the design of quantum autoencoders using a genetic algorithm (GA). By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders for data reconstruction without becoming trapped in local minima. We demonstrate effectiveness on image datasets, highlighting the potential of quantum autoencoders for efficient feature extraction within a noise-prone, near-term quantum era. Our approach lays a foundation for broader application of genetic algorithms to quantum architecture search, aiming for a robust, automated method that can adapt to varied data and hardware constraints.
The use of Variational Quantum Circuits (VQCs), which are an important type of model in Quantum Machine Learning (QML) that will be used with Basic Quantum Computers during their Noisy intermediate-scale quantum (NISQ) era, is very dependent on the properties of the circuits themselves (architectures) and how they are trained (training settings). In fact, this model is so dependent on the knowledge of the human designer that creating these models takes significant time and energy to develop properly through qualitatively high amounts of quantum knowledge. In this work, we propose a new method for automatically building and training quantum machine learning models using an AutoML-Assisted approach to optimize VQCs. The overall goal of the AutoML-Assisted approach is to treat the design of quantum circuits as a learning problem and utilize Neural Architecture Search ( NAS ) to search end to end on the architecture, variational parameters, and hyperparameters automatically. Our experimental results on classification tasks show that the automatically learned VQCs not only give better accuracy but also give faster convergence and generally are more robust to quantum noise than warehouse VQCs developed by humans. This work makes a direct contribution to increasing the automation and scalability by which quantum machine learning can be run on NISQ computers.