High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

• URL: http://arxiv.org/abs/2006.07680v1
• Date: Sat, 13 Jun 2020 16:55:23 GMT
• Title: High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
• Authors: Nicholas Gao, Max Wilson, Thomas Vandal, Walter Vinci, Ramakrishna Nemani, Eleanor Rieffel
• Abstract summary: Quantum machine learning is touted as a potential approach to demonstrate quantum advantage. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. We find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.
• Score: 3.6704555687356644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the Quantum-assisted Variational Autoencoder has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

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)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Quantum Architecture Search with Unsupervised Representation Learning [24.698519892763283]
  Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS) QAS is designed to optimize quantum circuits for Variational Quantum Algorithms (VQAs)
  arXiv  Detail & Related papers  (2024-01-21T19:53:17Z)
• Multimodal deep representation learning for quantum cross-platform verification [60.01590250213637]
  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.
  arXiv  Detail & Related papers  (2023-11-07T04:35:03Z)
• QArchSearch: A Scalable Quantum Architecture Search Package [1.725192300740999]
  We present textttQArchSearch, an AI based quantum architecture search package with the textttQTensor library as a backend. We show that the search package is able to efficiently scale the search to large quantum circuits and enables the exploration of more complex models for different quantum applications.
  arXiv  Detail & Related papers  (2023-10-11T20:00:33Z)
• Variational Quantum Linear Solver enhanced Quantum Support Vector Machine [3.206157921187139]
  We propose a novel approach called the Variational Quantum Linear solver (VQLS) enhanced QSVM. This is built upon our idea of utilizing the variational quantum linear solver to solve system of linear equations of a least squares-SVM on a NISQ device. The implementation of our approach is evaluated by an extensive series of numerical experiments with the Iris dataset.
  arXiv  Detail & Related papers  (2023-09-14T14:59:58Z)
• 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)
• Tweezer-programmable 2D quantum walks in a Hubbard-regime lattice [1.286202369590401]
  We study continuous-time quantum walks of single atoms on a 2D square lattice. We perform proof-of-principle demonstrations of spatial search using these walks. When scaled to more particles, the capabilities demonstrated here can be extended to study a variety of problems in quantum information science.
  arXiv  Detail & Related papers  (2022-02-02T18:56:11Z)
• Benchmarking Small-Scale Quantum Devices on Computing Graph Edit Distance [52.77024349608834]
  Graph Edit Distance (GED) measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical. In this paper we present a comparative study of two quantum approaches to computing GED.
  arXiv  Detail & Related papers  (2021-11-19T12:35:26Z)
• Quantum Embedding Search for Quantum Machine Learning [2.7612093695074456]
  We introduce a novel quantum embedding search algorithm (QES), pronounced as "quest" We establish the connection between the structures of quantum embedding and the representations of directed multi-graphs, enabling a well-defined search space. We demonstrate the feasibility of our proposed approach on synthesis and Iris datasets, which empirically shows that quantum embedding architecture by QES outperforms manual designs.
  arXiv  Detail & Related papers  (2021-05-25T11:50:57Z)
• Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
  We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations. We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
  arXiv  Detail & Related papers  (2020-12-08T01:10:30Z)

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.