Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation via Neural Networks

• URL: http://arxiv.org/abs/2403.07025v1
• Date: Sun, 10 Mar 2024 15:35:41 GMT
• Title: Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation via Neural Networks
• Authors: Subhasree Bhattacharjee, Soumyadip Sarkar, Kunal Das, Bikramjit Sarkar
• Abstract summary: Variational Quantum Eigensolver (VQE) is a promising algorithm for solving complex quantum problems. The ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes. This research introduces a novel approach by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations.
• Score: 0.4779196219827508
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the emergent realm of quantum computing, the Variational Quantum Eigensolver (VQE) stands out as a promising algorithm for solving complex quantum problems, especially in the noisy intermediate-scale quantum (NISQ) era. However, the ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes. This research introduces a novel approach to ameliorate this challenge by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations. By employing the Qiskit framework, we crafted parameterized quantum circuits using the RY-RZ ansatz and examined their behavior under varying levels of depolarizing noise. Our investigations spanned from determining the expectation values of a Hamiltonian, defined as a tensor product of Z operators, under different noise intensities to extracting the ground state energy. To bridge the observed outcomes under noise with the ideal noise-free scenario, we trained a Feed Forward Neural Network on the error probabilities and their associated expectation values. Remarkably, our model proficiently predicted the VQE outcome under hypothetical noise-free conditions. By juxtaposing the simulation results with real quantum device executions, we unveiled the discrepancies induced by noise and showcased the efficacy of our neural network-based ZNE technique in rectifying them. This integrative approach not only paves the way for enhanced accuracy in VQE computations on NISQ devices but also underlines the immense potential of hybrid quantum-classical paradigms in circumventing the challenges posed by quantum noise. Through this research, we envision a future where quantum algorithms can be reliably executed on noisy devices, bringing us one step closer to realizing the full potential of quantum computing.

Related papers

• 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)
• Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis [0.0]
  In quantum computing, the potential benefits of quantum neural networks (QNNs) have become increasingly apparent. Noisy Intermediate-Scale Quantum (NISQ) processors are prone to errors, which poses a significant challenge for the execution of complex algorithms or quantum machine learning. This paper provides a comprehensive analysis of the impact of noise on QNNs, examining the Mottonen state preparation algorithm under various noise models.
  arXiv  Detail & Related papers  (2023-11-23T15:22:22Z)
• 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)
• Variational Quantum-Neural Hybrid Error Mitigation [6.555128824546528]
  Quantum error mitigation (QEM) is crucial for obtaining reliable results on quantum computers. We show how variational quantum-neural hybrid eigensolver (VQNHE) algorithm is inherently noise resilient with a unique QEM capacity.
  arXiv  Detail & Related papers  (2021-12-20T08:07:58Z)
• Error mitigation in variational quantum eigensolvers using tailored probabilistic machine learning [5.630204194930539]
  We present a novel method that employs parametric Gaussian process regression (GPR) within an active learning framework to mitigate noise in quantum computations. We demonstrate the effectiveness of our method on a 2-site Anderson impurity model and a 8-site Heisenberg model, using the IBM open-source quantum computing framework, Qiskit.
  arXiv  Detail & Related papers  (2021-11-16T22:29:43Z)
• 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)
• Entangling Quantum Generative Adversarial Networks [53.25397072813582]
  We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
  arXiv  Detail & Related papers  (2021-04-30T20:38:41Z)
• Simulating noisy variational quantum eigensolver with local noise models [4.581041382009666]
  Variational quantum eigensolver (VQE) is promising to show quantum advantage on near-term noisy-intermediate-scale quantum computers. One central problem of VQE is the effect of noise, especially the physical noise on realistic quantum computers. We study systematically the effect of noise for the VQE algorithm, by performing numerical simulations with various local noise models.
  arXiv  Detail & Related papers  (2020-10-28T08:51:59Z)
• 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)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)
• Policy Gradient based Quantum Approximate Optimization Algorithm [2.5614220901453333]
  We show that policy-gradient-based reinforcement learning algorithms are well suited for optimizing the variational parameters of QAOA in a noise-robust fashion. We analyze the performance of the algorithm for quantum state transfer problems in single- and multi-qubit systems.
  arXiv  Detail & Related papers  (2020-02-04T00:46:51Z)

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.