Multimodal deep representation learning for quantum cross-platform verification

• URL: http://arxiv.org/abs/2311.03713v1
• Date: Tue, 7 Nov 2023 04:35:03 GMT
• Title: Multimodal deep representation learning for quantum cross-platform verification
• Authors: Yang Qian, Yuxuan Du, Zhenliang He, Min-hsiu Hsieh, Dacheng Tao
• Abstract summary: Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms. We introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities. We devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation.
• Score: 60.01590250213637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms, utilizing minimal measurements. While the random measurement approach has been instrumental in this context, the quasi-exponential computational demand with increasing qubit count hurdles its feasibility in large-qubit scenarios. To bridge this knowledge gap, here we introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities: measurement outcomes and classical description of compiled circuits on explored quantum devices, both enriched with unique information. Building upon this insight, we devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation. The learned representation can effectively characterize the similarity between the explored quantum devices when executing new quantum algorithms not present in the training data. We evaluate our proposal on platforms featuring diverse noise models, encompassing system sizes up to 50 qubits. The achieved results demonstrate a three-orders-of-magnitude improvement in prediction accuracy compared to the random measurements and offer compelling evidence of the complementary roles played by each modality in cross-platform verification. These findings pave the way for harnessing the power of multimodal learning to overcome challenges in wider quantum system learning tasks.

Related papers

• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Ensembles of Quantum Classifiers [0.0]
  A viable approach for the execution of quantum classification algorithms is the introduction of the ensemble methods. In this work, we present an implementation and an empirical evaluation of ensembles of quantum classifiers for binary classification.
  arXiv  Detail & Related papers  (2023-11-16T10:27:25Z)
• MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification [10.969833959443495]
  MORE stands for measurement and correlation based variational quantum multi-classifier. We implement MORE using the Qiskit Python library and evaluate it through extensive experiments on both noise-free and noisy quantum systems.
  arXiv  Detail & Related papers  (2023-07-21T19:33:10Z)
• Data re-uploading with a single qudit [1.0923877073891446]
  Two-level quantum systems, i.e. qubits, form the basis for most quantum machine learning approaches. We explore the capabilities of multi-level quantum systems, so-called qudits, for their use in a quantum machine learning context.
  arXiv  Detail & Related papers  (2023-02-27T16:32:16Z)
• Quantum Federated Learning for Distributed Quantum Networks [9.766446130011706]
  We propose a quantum federated learning for distributed quantum networks by utilizing interesting characteristics of quantum mechanics. A quantum gradient descent algorithm is provided to help clients in the distributed quantum networks to train local models. A quantum secure multi-party computation protocol is designed, which utilizes the Chinese residual theorem.
  arXiv  Detail & Related papers  (2022-12-25T14:37:23Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Scalable approach to many-body localization via quantum data [69.3939291118954]
  Many-body localization is a notoriously difficult phenomenon from quantum many-body physics. We propose a flexible neural network based learning approach that circumvents any computationally expensive step. Our approach can be applied to large-scale quantum experiments to provide new insights into quantum many-body physics.
  arXiv  Detail & Related papers  (2022-02-17T19:00:09Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Variational learning for quantum artificial neural networks [0.0]
  We first review a series of recent works describing the implementation of artificial neurons and feed-forward neural networks on quantum processors. We then present an original realization of efficient individual quantum nodes based on variational unsampling protocols. While keeping full compatibility with the overall memory-efficient feed-forward architecture, our constructions effectively reduce the quantum circuit depth required to determine the activation probability of single neurons.
  arXiv  Detail & Related papers  (2021-03-03T16:10:15Z)
• Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
  We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
  arXiv  Detail & Related papers  (2021-01-21T22:18:49Z)

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.