Fock State-enhanced Expressivity of Quantum Machine Learning Models

• URL: http://arxiv.org/abs/2107.05224v1
• Date: Mon, 12 Jul 2021 07:07:39 GMT
• Title: Fock State-enhanced Expressivity of Quantum Machine Learning Models
• Authors: Beng Yee Gan, Daniel Leykam, and Dimitris G. Angelakis
• Abstract summary: photonic-based bosonic data-encoding scheme embeds classical data points using fewer encoding layers. We propose three different noisy intermediate-scale quantum-compatible binary classification methods with different scaling of required resources.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The data-embedding process is one of the bottlenecks of quantum machine learning, potentially negating any quantum speedups. In light of this, more effective data-encoding strategies are necessary. We propose a photonic-based bosonic data-encoding scheme that embeds classical data points using fewer encoding layers and circumventing the need for nonlinear optical components by mapping the data points into the high-dimensional Fock space. The expressive power of the circuit can be controlled via the number of input photons. Our work shed some light on the unique advantages offers by quantum photonics on the expressive power of quantum machine learning models. By leveraging the photon-number dependent expressive power, we propose three different noisy intermediate-scale quantum-compatible binary classification methods with different scaling of required resources suitable for different supervised classification tasks.

Related papers

• Universal Logical Quantum Photonic Neural Network Processor via Cavity-Assisted Interactions [0.0]
  We propose an architecture to prepare and perform logical quantum operations on arbitrary multimode multi-photon states using a quantum photonic neural network. The proposed architecture paves the way for near-term quantum photonic processors that enable error-corrected quantum computation.
  arXiv  Detail & Related papers  (2024-10-02T23:21:50Z)
• Empirical Power of Quantum Encoding Methods for Binary Classification [0.2118773996967412]
  We will focus on encoding schemes and their effects on various machine learning metrics. Specifically, we focus on real-world data encoding to demonstrate differences between quantum encoding strategies for several real-world datasets.
  arXiv  Detail & Related papers  (2024-08-23T14:34:57Z)
• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Supervised binary classification of small-scale digits images with a trapped-ion quantum processor [56.089799129458875]
  We show that a quantum processor can correctly solve the basic classification task considered. With the increase of the capabilities quantum processors, they can become a useful tool for machine learning.
  arXiv  Detail & Related papers  (2024-06-17T18:20:51Z)
• Quantum Sparse Coding and Decoding Based on Quantum Network [1.0683439960798695]
  We propose a symmetric quantum neural network for realizing sparse coding and decoding algorithms. Our networks consist of multi-layer, two-level unitary transformations that are naturally suited for optical circuits. We achieve sparse coding and decoding of binary and grayscale images in classical problems, as well as that of complex quantum states in quantum problems separately.
  arXiv  Detail & Related papers  (2024-06-10T04:21:27Z)
• Neural auto-designer for enhanced quantum kernels [59.616404192966016]
  We present a data-driven approach that automates the design of problem-specific quantum feature maps. Our work highlights the substantial role of deep learning in advancing quantum machine learning.
  arXiv  Detail & Related papers  (2024-01-20T03:11:59Z)
• Quantum Computation via Multiport Quantum Fourier Optical Processors [9.992810060555813]
  A single photon's image possesses a vast information capacity that can be harnessed for quantum information processing. This paper employs quantum Fourier optics to implement some key quantum logical gates that can be instrumental in optical quantum computations.
  arXiv  Detail & Related papers  (2023-03-07T13:23:56Z)
• Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing [63.69764116066747]
  This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.
  arXiv  Detail & Related papers  (2021-01-26T09:31:46Z)
• Entanglement transfer, accumulation and retrieval via quantum-walk-based qubit-qudit dynamics [50.591267188664666]
  Generation and control of quantum correlations in high-dimensional systems is a major challenge in the present landscape of quantum technologies. We propose a protocol that is able to attain entangled states of $d$-dimensional systems through a quantum-walk-based it transfer & accumulate mechanism. In particular, we illustrate a possible photonic implementation where the information is encoded in the orbital angular momentum and polarization degrees of freedom of single photons.
  arXiv  Detail & Related papers  (2020-10-14T14:33:34Z)
• Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
  Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry. New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications. We present a novel technique to reconstruct the transfer matrix of linear optical networks.
  arXiv  Detail & Related papers  (2020-10-01T16:04:22Z)
• Experimental realization of a quantum image classifier via tensor-network-based machine learning [4.030017427802459]
  We demonstrate highly successful classifications of real-life images using photonic qubits. We focus on binary classification for hand-written zeroes and ones, whose features are cast into the tensor-network representation. Our scheme can be scaled to efficient multi-qubit encodings of features in the tensor-product representation.
  arXiv  Detail & Related papers  (2020-03-19T03:26:27Z)

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.