Flexible Error Mitigation of Quantum Processes with Data Augmentation Empowered Neural Model

• URL: http://arxiv.org/abs/2311.01727v1
• Date: Fri, 3 Nov 2023 05:52:14 GMT
• Title: Flexible Error Mitigation of Quantum Processes with Data Augmentation Empowered Neural Model
• Authors: Manwen Liao, Yan Zhu, Giulio Chiribella, Yuxiang Yang
• Abstract summary: We propose a data augmentation empowered neural model for error mitigation (DAEM) Our model does not require any prior knowledge about the specific noise type and measurement settings. It can estimate noise-free statistics solely from the noisy measurement results of the target quantum process.
• Score: 9.857921247636451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural networks have shown their effectiveness in various tasks in the realm of quantum computing. However, their application in quantum error mitigation, a crucial step towards realizing practical quantum advancements, has been restricted by reliance on noise-free statistics. To tackle this critical challenge, we propose a data augmentation empowered neural model for error mitigation (DAEM). Our model does not require any prior knowledge about the specific noise type and measurement settings and can estimate noise-free statistics solely from the noisy measurement results of the target quantum process, rendering it highly suitable for practical implementation. In numerical experiments, we show the model's superior performance in mitigating various types of noise, including Markovian noise and Non-Markovian noise, compared with previous error mitigation methods. We further demonstrate its versatility by employing the model to mitigate errors in diverse types of quantum processes, including those involving large-scale quantum systems and continuous-variable quantum states. This powerful data augmentation-empowered neural model for error mitigation establishes a solid foundation for realizing more reliable and robust quantum technologies in practical applications.

Related papers

• Quantum noise modeling through Reinforcement Learning [38.47830254923108]
  We introduce a machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. Our algorithm leverages reinforcement learning, offering increased flexibility in reproducing various noise models. The effectiveness of the RL agent has been validated through simulations and testing on real superconducting qubits.
  arXiv  Detail & Related papers  (2024-08-02T18:05:21Z)
• Lindblad-like quantum tomography for non-Markovian quantum dynamical maps [46.350147604946095]
  We introduce Lindblad-like quantum tomography (L$ell$QT) as a quantum characterization technique of time-correlated noise in quantum information processors. We discuss L$ell$QT for the dephasing dynamics of single qubits in detail, which allows for a neat understanding of the importance of including multiple snapshots of the quantum evolution in the likelihood function.
  arXiv  Detail & Related papers  (2024-03-28T19:29:12Z)
• Power Characterization of Noisy Quantum Kernels [52.47151453259434]
  We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small. We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
  arXiv  Detail & Related papers  (2024-01-31T01:02:16Z)
• Volumetric Benchmarking of Quantum Computing Noise Models [3.0098885383612104]
  We present a systematic approach to benchmark noise models for quantum computing applications. It compares the results of hardware experiments to predictions of noise models for a representative set of quantum circuits. We also construct a noise model and optimize its parameters with a series of training circuits.
  arXiv  Detail & Related papers  (2023-06-14T10:49:01Z)
• Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning [47.991114317813555]
  Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots. This paper proposes to leverage such randomness to define prediction sets for both classification and regression that provably capture the uncertainty of the model.
  arXiv  Detail & Related papers  (2023-04-06T22:05:21Z)
• Non-Markovian noise sources for quantum error mitigation [0.0]
  We present a non-Markovian model of quantum state evolution and a quantum error mitigation cost function tailored for NISQ devices. Our findings reveal that the cost function for quantum error mitigation increases as the coupling strength between the quantum system and its environment intensifies.
  arXiv  Detail & Related papers  (2023-02-10T05:10:27Z)
• Impact of quantum noise on the training of quantum Generative Adversarial Networks [0.0]
  We conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process.
  arXiv  Detail & Related papers  (2022-03-02T10:35:34Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Neural Error Mitigation of Near-Term Quantum Simulations [0.0]
  We introduce $textitneural error mitigation$, a novel method that uses neural networks to improve estimates of ground states and ground-state observables. Our results show that neural error mitigation improves the numerical and experimental VQE computation to yield low-energy errors. Our method is a promising strategy for extending the reach of near-term quantum computers to solve complex quantum simulation problems.
  arXiv  Detail & Related papers  (2021-05-17T18:00:57Z)
• Enhancing quantum models of stochastic processes with error mitigation [0.0]
  We bridge the gap between theoretical quantum models and practical use with the inclusion of error mitigation methods. It is observed that error mitigation is successful in improving the resultant expectation values. While our results indicate that error mitigation work, we show that its methodology is ultimately constrained by hardware limitations in these quantum computers.
  arXiv  Detail & Related papers  (2021-05-13T17:45:34Z)
• 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.