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arXiv:2304.10650 · IEEE Transactions on Quantum Engineering

Learning a Quantum Computer's Capability

Daniel Hothem, Kevin C Young, Tommie Catanach, Timothy J Proctor

Published Jan 1, 2024• 2 citations
arXivHTMLPDF

Abstract

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 around$2-5\%$), and we highlight the challenges to building better neural network capability models.

DOI
10.1109/tqe.2024.3430215

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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%2-5\%2−5%), and we highlight the challenges to building better neural network capability models.
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.
Holistic benchmarks for quantum computers are essential for testing and summarizing the performance of quantum hardware. However, holistic benchmarks-such as algorithmic or randomized benchmarks-typically do not predict a processor's performance on circuits outside the benchmark's necessarily very limited set of test circuits. In this paper, we introduce a general framework for building predictive models from benchmarking data using capability models. Capability models can be fit to many kinds of benchmarking data and used for a variety of predictive tasks. We demonstrate this flexibility with two case studies. In the first case study, we predict circuit (i) process fidelities and (ii) success probabilities by fitting error rates models to two kinds of volumetric benchmarking data. Error rates models are simple yet versatile capability models which assign effective error rates to individual gates, or more general circuit components. In the second case study, we construct a capability model for predicting circuit success probabilities by applying transfer learning to ResNet50, a neural network trained for image classification. Our case studies use data from cloud-accessible quantum computers and simulations of noisy quantum computers.
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.
Quantum computers have the potential to revolutionize diverse fields, including quantum chemistry, materials science, and machine learning. However, contemporary quantum computers experience errors that often cause quantum programs run on them to fail. Until quantum computers can reliably execute large quantum programs, stakeholders will need fast and reliable methods for assessing a quantum computer's capability-i.e., the programs it can run and how well it can run them. Previously, off-the-shelf neural network architectures have been used to model quantum computers' capabilities, but with limited success, because these networks fail to learn the complex quantum physics that determines real quantum computers' errors. We address this shortcoming with a new quantum-physics-aware neural network architecture for learning capability models. Our architecture combines aspects of graph neural networks with efficient approximations to the physics of errors in quantum programs. This approach achieves up to ∼50%\sim50\%∼50% reductions in mean absolute error on both experimental and simulated data, over state-of-the-art models based on convolutional neural networks.
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 Network (CNN) is a popular model in computer vision and has the advantage of making good use of the correlation information of data. However, CNN is challenging to learn efficiently if the given dimension of data or model becomes too large. Quantum Convolutional Neural Network (QCNN) provides a new solution to a problem to solve with CNN using a quantum computing environment, or a direction to improve the performance of an existing learning model. The first study to be introduced proposes a model to effectively solve the classification problem in quantum physics and chemistry by applying the structure of CNN to the quantum computing environment. The research also proposes the model that can be calculated with O(log(n)) depth using Multi-scale Entanglement Renormalization Ansatz (MERA). The second study introduces a method to improve the model's performance by adding a layer using quantum computing to the CNN learning model used in the existing computer vision. This model can also be used in small quantum computers, and a hybrid learning model can be designed by adding a quantum convolution layer to the CNN model or replacing it with a convolution layer. This paper also verifies whether the QCNN model is capable of efficient learning compared to CNN through training using the MNIST dataset through the TensorFlow Quantum platform.