Current quantum computers are noisy and error-prone.As devices grow in size, we need scalable methods for predicting the performance of a given device.In this work, we explore the potential of neural networks to play this role.We demonstrate a convolutional neural network's ability to predict circuit success rates under both simulated and experimental error models; in each case outperforming non-neural network models that are based on per gate error rates.We also experimentally investigate how a convolutional network's ability to predict success rates varies as a function of dataset size and measurement accuracy, achieving noticeable improvements in performance on simulated data as measurement precision increases.Finally, we present proof-of-concept work detailing a convolutional neural network's ability to handle non-Markovian noise on wide circuits (greater than 25 qubits), a regime in which traditional techniques become inaccurate and highly expensive.
Current quantum computers are noisy and error-prone.As devices grow in size, we need scalable methods for predicting the performance of a given device.In this work, we explore the potential of neural networks to play this role.We demonstrate a convolutional neural network's ability to predict circuit success rates under both simulated and experimental error models; in each case outperforming non-neural network models that are based on per gate error rates.We also experimentally investigate how a convolutional network's ability to predict success rates varies as a function of dataset size and measurement accuracy, achieving noticeable improvements in performance on simulated data as measurement precision increases.Finally, we present proof-of-concept work detailing a convolutional neural network's ability to handle non-Markovian noise on wide circuits (greater than 25 qubits), a regime in which traditional techniques become inaccurate and highly expensive.
Current quantum computers are noisy and error-prone.As devices grow in size, we need scalable methods for predicting the performance of a given device.In this work, we explore the potential of neural networks to play this role.We demonstrate a convolutional neural network's ability to predict circuit success rates under both simulated and experimental error models; in each case outperforming non-neural network models that are based on per gate error rates.We also experimentally investigate how a convolutional network's ability to predict success rates varies as a function of dataset size and measurement accuracy, achieving noticeable improvements in performance on simulated data as measurement precision increases.Finally, we present proof-of-concept work detailing a convolutional neural network's ability to handle non-Markovian noise on wide circuits (greater than 25 qubits), a regime in which traditional techniques become inaccurate and highly expensive.
Accurately predicting a quantum computer's capability—which circuits it can run and how well it can run them—is a foundational goal of quantum characterization and benchmarking. As modern quantum computers become increasingly hard to simulate, we must develop accurate and scalable predictive capability models to help researchers and stakeholders decide which quantum computers to build and use. In this work, we propose a hardware-agnostic method to efficiently construct scalable predictive models of a quantum computer's capability for almost any class of circuits, and demonstrate our method using convolutional neural networks (CNNs). Our CNN-based approach works by efficiently representing a circuit as a three-dimensional tensor and then using a CNN to predict its success rate. Our CNN capability models obtain approximately a 1% average absolute prediction error when modeling processors experiencing both Markovian and non-Markovian stochastic Pauli errors. We also apply our CNNs to model the capabilities of cloud-access quantum computing systems, obtaining moderate prediction accuracy (average absolute error around2−5%), and we highlight the challenges to building better neural network capability models.
Abstract Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. Deep convolutional neural networks (CNNs) are trained to predict single-qubit and two-qubit expectation values using databases of classically simulated circuits. These circuits are built using either a universal gate set or a continuous set of rotations plus an entangling gate, and they are represented via properly designed encodings of these gates. The prediction accuracy for previously unseen circuits is analyzed, also making comparisons with small-scale quantum computers available from the free IBM Quantum program. The CNNs often outperform these quantum devices, depending on the circuit depth, on the network depth, and on the training set size. Notably, our CNNs are designed to be scalable. This allows us exploiting transfer learning and performing extrapolations to circuits larger than those included in the training set. These CNNs also demonstrate remarkable resilience against noise, namely, they remain accurate even when trained on (simulated) expectation values averaged over very few measurements.
Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter several challenges, including limitations in accuracy improvement, computational efficiency, explain ability, and generalization. As data dimensionality or model complexity increases, CNNs often become more difficult to train efficiently, leading to slower processing times. To overcome these challenges, Quantum Convolutional Neural Networks (QCNNs) offer a promising quantum-based approach to enhance existing models, particularly by improving training efficiency and reducing processing times. This paper provides a comparative analysis of classical CNNs and a novel quantum circuit architecture tailored for image-based tasks. The analysis emphasizes the adaptability and potential of quantum circuits to enhance feature extraction, recognition accuracy, and processing efficiency.We applied various QCNN architectures to the Chinese-MNIST dataset and compared their performance with traditional CNN models. Although QCNNs achieved slightly lower accuracy, they demonstrated significantly greater stability. These findings high- light QCNNs’ potential to overcome the computational limitations of conventional deep learning, offering a promising pathway for quantum-enhanced solutions in computer vision. Future work will focus on refining quantum circuit designs, developing hybrid quantum-classical models, and extending QCNN applications to larger and more diverse datasets.
This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasing amount of data, utilizing computing methods like CNN in real-time has become challenging. QCNNs overcome this challenge by utilizing qubits to represent data in a quantum environment and applying CNN structures to quantum computers. The time and accuracy of QCNNs are compared with classical CNNs and ANN models under different conditions such as batch size and input size. The maximum complexity level that QCNNs can handle in terms of these parameters is also investigated. The analysis shows that QCNNs have the potential to outperform both classical CNNs and ANN models in terms of accuracy and efficiency for certain applications, demonstrating their promise as a powerful tool in the field of machine learning.
Quantum computing has gained significant attention in recent years, with numerous algorithms and applications under active development. Limited by the current quantum technology, quantum noise and readout error have become critical issues. Various methods have been proposed to address readout error through error mitigation techniques, typically involving post-processing of measurement data. However, most of these methods increase the quantum hardware overhead, leading to higher computational costs. In this work, we present a machine-learning-based approach that minimizes hardware overhead while improving accuracy of the measurement probability distributions. We employed a convolutional neural network (CNN) autoencoder, commonly used for image denoising, as our baseline model. The datasets were derived from 4-qubit random circuits with depths ranging from 1 to 18, generated using Qiskit backends for target and noisy measurement data. The model was trained using mean squared error (MSE) as the loss function and Adam optimizer over 500 epochs, achieving an average noise reduction by 95% across the validation set, with no signs of overfitting. To validate the model's effectiveness across diverse quantum states, we conducted extensive tests on both typical quantum circuits and algorithms, including Grover's search algorithm, Quantum Fourier Transform, Haar random circuits and Trivial Paramagnet. The results demonstrated consistent and robust denoising in noisy measurement data, indicating that the autoencoder model is well-suited for efficient quantum error mitigation for current noisy quantum computers. This work contributes to the advancement of quantum error mitigation techniques using machine learning.