QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

Wang, Hanrui; Ding, Yongshan; Gu, Jiaqi; Lin, Yujun; Pan, David Z.; Chong, Frederic T.; Han, Song

doi:10.1109/hpca53966.2022.00057

Cited by 78 publications

(47 citation statements)

References 59 publications

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“…Quantum Computing is a novel computing paradigm that solves classically intractable problems with substantially higher efficiency and speed. Due to the quantum parallelism and the effect of interference and entanglement, it has been demonstrated to have exponential advantage in various machine learning tasks [2][3][4][5][6][7]. A quantum neural network (QNN) [8] is able to generate the correlation between variables that are inefficient to represent through classical computation by defining a feature map that maps classical data into the quantum Hilbert space.…”

Section: Introductionmentioning

confidence: 99%

“…Although quantum advantage has been demonstrated, QNNs suffer from low inference accuracy. State-of-the-art QNNs [2,3] achieve <60% accuracy when inferring a 10-class MNIST [11] dataset, i.e., the smallest and simplest benchmark in the classical machine learning domain. The low inference accuracy of state-of-the-art QNNs is caused by three factors, i.e., the Noisy Intermediate-Scale Quantum (NISQ) devices, the lack of nonlinearity, and the very limited learning capability of the QNN circuit ansatz.…”

Section: Introductionmentioning

confidence: 99%

“…Noises [12] introduced by imperfect fabrication, crosstalk, and non-ideal control and readout result in various types of errors, e.g., bit-flip and phaseflip errors [13,14]. Although recent work creates an automatic framework, QuantumNAS [2], to search the most noise-resistant QNN within the design space of a large variational quantum circuit consisting of pre-defined parameterized quantum gates, Quantum-NAS does not have any error awareness during model training. In another word, the framework just naïvely searches the most accurate QNN within the design space that is not optimized for error tolerance, and then add noise during circuit mapping to retrain the selected model.…”

Section: Introductionmentioning

confidence: 99%

“…Second, prior QNNs [2] do not have much nonlinearity due to the linear and unitary nature of quantum mechanics. A deep structure cannot help such models to improve their performance, since various nonlinear activations are a crucial factor for multi-layer deep neural networks to achieve high inference accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…• High inference accuracy. We implemented, evaluated, and compared QMLP against prior QNNs such as QuantumFlow [3] and QuantumNAS [2]. Compared to prior QNN designs, QMLP increases the inference accuracy on the 10-class MNIST dataset by 10% with 2× fewer quantum gates and 3× reduced parameters.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates

Chu

Chia

Jiang

et al. 2022

Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design

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Despite potential quantum supremacy, state-of-the-art quantum neural networks (QNNs) suffer from low inference accuracy. First, the current Noisy Intermediate-Scale Quantum (NISQ) devices with high error rates of 10 −3 to 10 −2 significantly degrade the accuracy of a QNN. Second, although recently proposed Re-Uploading Units (RUUs) introduce some non-linearity into the QNN circuits, the theory behind it is not fully understood. Furthermore, previous RUUs that repeatedly upload original data can only provide marginal accuracy improvements. Third, current QNN circuit ansatz uses fixed two-qubit gates to enforce maximum entanglement capability, making task-specific entanglement tuning impossible, resulting in poor overall performance. In this paper, we propose a Quantum Multilayer Perceptron (QMLP) architecture featured by error-tolerant input embedding, rich nonlinearity, and enhanced variational circuit ansatz with parameterized two-qubit entangling gates. Compared to prior arts, QMLP increases the inference accuracy on the 10-class MNIST dataset by 10% with 2× fewer quantum gates and 3× reduced parameters. Our source code is available and can be found in [1] 1 . CCS CONCEPTS• Hardware → Quantum computation; • Computing methodologies → Supervised learning by classification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates

Chu

Chia

Jiang

et al. 2022

Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design

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Quantum Architecture Search with Neural Predictor Based on Graph Measures

He,

Li,

Deng

et al. 2024

Adv Quantum Tech

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Quantum architecture search (QAS) has attracted increasing attention owing to its remarkable ability to automate the design of quantum circuits for variational quantum algorithms (VQAs). However, evaluating the performance of numerous quantum circuits is essential to provide feedback for the search strategy, which inevitably renders QAS computationally expensive. Performance predictors have emerged as highly efficient evaluation methods to mitigate this challenge. However, the performance predictor faces a critical challenge in reducing the required number of circuit‐performance pairs for training. This study encodes circuit architecture by representing a quantum circuit as a relational graph that emphasizes message exchange. Subsequently, valuable information about circuit architecture is extracted through three types of graph measures, including distance‐based, degree‐based, and cluster‐based measures. The graph measures define a smooth space related to circuit performance, facilitating the training of the performance predictor. The effectiveness of the proposed method is assessed across three tasks within variational quantum eigensolvers (VQE): identifying the ground states of the Transverse Field Ising Model (TFIM), the Heisenberg model, and the molecule. The simulation results demonstrate notable enhancements in predictive accuracy achieved by our method, coupled with a substantial reduction in the required number of training samples for the predictor.

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Krylov complexity and spectral form factor for noisy random matrix models

Bhattacharyya,

Haque,

Jafari

et al. 2023

J. High Energ. Phys.

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We study the spectral properties of two classes of random matrix models: non-Gaussian RMT with quartic and sextic potentials, and RMT with Gaussian noise. We compute and analyze the quantum Krylov complexity and the spectral form factor for both of these models. We find that both models show suppression of the spectral form factor at short times due to decoherence effects, but they differ in their long-time behavior. In particular, we show that the Krylov complexity for the non-Gaussian RMT and RMT with noise deviates from that of a Gaussian RMT. We discuss the implications and limitations of our results for quantum chaos and quantum information in open quantum systems. Our study reveals the distinct sensitivities of the spectral form factor and complexity to non-Gaussianity and noise, which contribute to the observed differences in the different time domains.

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QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

Cited by 78 publications

References 59 publications

QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates

QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates

Quantum Architecture Search with Neural Predictor Based on Graph Measures

Krylov complexity and spectral form factor for noisy random matrix models

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