Quantum optical shallow networks

• URL: http://arxiv.org/abs/2507.21036v1
• Date: Mon, 28 Jul 2025 17:55:19 GMT
• Title: Quantum optical shallow networks
• Authors: Simone Roncallo, Angela Rosy Morgillo, Seth Lloyd, Chiara Macchiavello, Lorenzo Maccone,
• Abstract summary: We present a quantum optical protocol that implements a shallow network with an arbitrary number of neurons.<n>The network output is determined by the coincidence rates measured when the photons interfere at a beam splitter.<n>Remarkably, once trained, our model requires constant optical resources regardless of the number of input features and neurons.
• Score: 3.262230127283452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classical shallow networks are universal approximators. Given a sufficient number of neurons, they can reproduce any continuous function to arbitrary precision, with a resource cost that scales linearly in both the input size and the number of trainable parameters. In this work, we present a quantum optical protocol that implements a shallow network with an arbitrary number of neurons. Both the input data and the parameters are encoded into single-photon states. Leveraging the Hong-Ou-Mandel effect, the network output is determined by the coincidence rates measured when the photons interfere at a beam splitter, with multiple neurons prepared as a mixture of single-photon states. Remarkably, once trained, our model requires constant optical resources regardless of the number of input features and neurons.

Related papers

• Photonic Quantum Convolutional Neural Networks with Adaptive State Injection [0.39928148142956393]
  We design and experimentally implement the first photonic quantum convolutional neural network (PQCNN) based on particle-number preserving circuits equipped with state injection.<n>We experimentally validate the PQCNN for a binary image classification on a photonic platform utilizing a semiconductor quantum dot-based single-photon source.<n>We highlight the potential utility of a simple adaptive technique for a nonlinear Boson Sampling task, compatible with near-term quantum devices.
  arXiv  Detail & Related papers  (2025-04-29T17:57:01Z)
• Training the parametric interactions in an analog bosonic quantum neural network with Fock basis measurement [0.9786690381850356]
  Quantum neural networks have the potential to be seamlessly integrated with quantum devices for automatic recognition of quantum states.<n>We propose leveraging bosonic modes and performing Fock basis measurements, enabling the extraction of an exponential number of features relative to the number of modes.<n>We show that the network can be trained even though the number of trainable parameters scales only linearly with the number of modes, whereas the number of neurons grows exponentially.
  arXiv  Detail & Related papers  (2024-11-28T12:59:19Z)
• 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)
• Quantum optical classifier with superexponential speedup [3.262230127283452]
  We present a quantum optical pattern recognition method for binary classification tasks.<n>It classifies objects without reconstructing their images.<n>It shows a constant $mathcalO(1)$ complexity in the number of computational operations and photons required by a single classification.
  arXiv  Detail & Related papers  (2024-04-23T17:55:49Z)
• Quantum machine learning for image classification [39.58317527488534]
  This research introduces two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era. A second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process.
  arXiv  Detail & Related papers  (2023-04-18T18:23:20Z)
• Simulation of Entanglement Generation between Absorptive Quantum Memories [56.24769206561207]
  We use the open-source Simulator of QUantum Network Communication (SeQUeNCe), developed by our team, to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. We realize the representation of photonic quantum states within truncated Fock spaces in SeQUeNCe. We observe varying fidelity with SPDC source mean photon number, and varying entanglement generation rate with both mean photon number and memory mode number.
  arXiv  Detail & Related papers  (2022-12-17T05:51:17Z)
• Meta-Learning Sparse Implicit Neural Representations [69.15490627853629]
  Implicit neural representations are a promising new avenue of representing general signals. Current approach is difficult to scale for a large number of signals or a data set. We show that meta-learned sparse neural representations achieve a much smaller loss than dense meta-learned models.
  arXiv  Detail & Related papers  (2021-10-27T18:02:53Z)
• MWQ: Multiscale Wavelet Quantized Neural Networks [45.22093693422084]
  We propose a novel multiscale wavelet quantization (MWQ) method inspired by the characteristics of images in the frequency domain. It exploits the multiscale frequency and spatial information to alleviate the information loss caused by quantization in the spatial domain. Because of the flexibility of MWQ, we demonstrate three applications on the ImageNet and COCO datasets.
  arXiv  Detail & Related papers  (2021-03-09T11:21:59Z)
• 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)
• Searching for Low-Bit Weights in Quantized Neural Networks [129.8319019563356]
  Quantized neural networks with low-bit weights and activations are attractive for developing AI accelerators. We present to regard the discrete weights in an arbitrary quantized neural network as searchable variables, and utilize a differential method to search them accurately.
  arXiv  Detail & Related papers  (2020-09-18T09:13:26Z)
• Efficient trainability of linear optical modules in quantum optical neural networks [0.0]
  We show that coherent light in $m$ modes can be generically compiled efficiently if the total intensity scales sublinearly with $m$. We further demonstrate efficient trainability of $m$ mode linear optical quantum circuits for variational mean field energy estimation of positive quadratic Hamiltonians for input states that do not have energy exponentially vanishing with $m$.
  arXiv  Detail & Related papers  (2020-08-20T19:26:54Z)
• A bright and fast source of coherent single photons [46.25143811066789]
  A single photon source is a key enabling technology in device-independent quantum communication. We report a single photon source with an especially high system efficiency.
  arXiv  Detail & Related papers  (2020-07-24T17:08:46Z)

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.