HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era

• URL: http://arxiv.org/abs/2402.08475v3
• Date: Thu, 15 Aug 2024 11:51:31 GMT
• Title: HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era
• Authors: Muhammad Kashif, Muhammad Shafique,
• Abstract summary: We find that barren plateaus (BPs) appear earlier in noisy quantum environments compared to ideal, noise-free conditions. We propose a strategic approach to observable selection to improve QNN performance in noisy quantum computing environments.
• Score: 4.348591076994875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper delves into the intricate dynamics of quantum noise and its influence on the onset and mitigation of barren plateaus (BPs) - a phenomenon that critically impedes the scalability of QNNs. We find that BPs appear earlier in noisy quantum environments compared to ideal, noise-free conditions.However, strategic selection of qubit measurement observables can effectively tackle this issue. To this end, we examine a variety of observables, such as PauliZ,PauliX, PauliY, and a specially designed arbitrary Hermitian observable, tailored to the requirements of the cost function and the desired outputs of quantum circuits. Our analysis encompasses both global and local cost function definitions, with the former involving measurements across all qubits and the latter focusing on single-qubit measurements within the QNN framework. Our findings indicate that in a global cost function scenario, PauliX and PauliY observables lead to flatter optimization landscapes, signaling BPs with increasing qubits, especially in noisy conditions. Conversely, the PauliZ observable maintains trainability up to 8 qubits but encounters BPs at 10 qubits. Notably, the arbitrary Hermitian observable, when used with a global cost function, shows a unique advantage as it benefits from noise, facilitating effective training up to 10 qubits. Furthermore, with a local cost function, out of the three conventional observables (PauliX, PauliY and PauliZ), PauliZ is more effective, sustaining training efficiency under noisy conditions for up to 10 qubits, while PauliX and PauliY do not show similar benefits and remain susceptible to BPs. Our results highlight the importance of noise consideration in QNN training and propose a strategic approach to observable selection to improve QNN performance in noisy quantum computing environments thus contributing to the advancement of quantum machine learning research.

Related papers

• NRQNN: The Role of Observable Selection in Noise-Resilient Quantum Neural Networks [4.348591076994875]
  This paper explores the complexities associated with training Quantum Neural Networks (QNNs) under noisy conditions. We first demonstrate that Barren Plateaus (BPs) emerge more readily in noisy quantum environments than in ideal conditions. We then propose that careful selection of qubit measurement observable can make QNNs resilient against noise.
  arXiv  Detail & Related papers  (2025-02-18T08:32:47Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• Dynamic Resource Allocation with Quantum Error Detection [8.436934066461625]
  We propose a novel resource allocation framework that applies Pauli checks. In this paper, we highlight their potential to infer the noise characteristics of a quantum system. We show that dynamic quantum resource allocation with Pauli checks can outperform state-of-art mapping techniques.
  arXiv  Detail & Related papers  (2024-08-10T14:21:45Z)
• Noise-induced shallow circuits and absence of barren plateaus [2.5295633594332334]
  We show that any noise truncates' most quantum circuits to effectively logarithmic depth. We then prove that quantum circuits under any non-unital noise exhibit lack of barren plateaus for cost functions composed of local observables.
  arXiv  Detail & Related papers  (2024-03-20T19:00:49Z)
• Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows [1.412425180760368]
  We propose a robust shallow shadows protocol for characterizing quantum states on current quantum computing platforms. Our protocol correctly recovers state properties such as expectation values, fidelity, and entanglement entropy, while maintaining a lower sample complexity. This combined theoretical and experimental analysis positions the robust shallow shadow protocol as a scalable, robust, and sample-efficient protocol.
  arXiv  Detail & Related papers  (2024-02-27T21:53:32Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Efficient learning of the structure and parameters of local Pauli noise channels [1.5229257192293197]
  We present a novel approach for learning Pauli noise channels over n qubits. We achieve our results by leveraging a groundbreaking result by Bresler for efficiently learning Gibbs measures. Our method is efficient both in the number of samples and postprocessing without giving up on other desirable features.
  arXiv  Detail & Related papers  (2023-07-06T12:42:49Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• The effect of quantum noise on algorithmic perfect quantum state transfer on NISQ processors [0.0]
  We investigate the influence of quantum noise on hitting time and fidelity of a typical quantum walk problem. We find that Pauli noise mostly smears out a peak in the fidelity of excitation transfer, while crosstalks between qubits mostly affect the hitting time.
  arXiv  Detail & Related papers  (2021-10-28T14:25:09Z)
• QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization [22.900530292063348]
  Quantum Circuits (PQC) are promising towards quantum advantage on near-term quantum hardware. However, due to the large quantum noises (errors), the performance of PQC models has a severe degradation on real quantum devices. We present QuantumNAT, a PQC-specific framework to perform noise-aware optimizations in both training and inference stages to improve robustness.
  arXiv  Detail & Related papers  (2021-10-21T17:59:19Z)
• Pulse-level noisy quantum circuits with QuTiP [53.356579534933765]
  We introduce new tools in qutip-qip, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian.
  arXiv  Detail & Related papers  (2021-05-20T17:06:52Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.