QuATON: Quantization Aware Training of Optical Neurons

• URL: http://arxiv.org/abs/2310.03049v2
• Date: Thu, 21 Mar 2024 16:21:45 GMT
• Title: QuATON: Quantization Aware Training of Optical Neurons
• Authors: Hasindu Kariyawasam, Ramith Hettiarachchi, Quansan Yang, Alex Matlock, Takahiro Nambara, Hiroyuki Kusaka, Yuichiro Kunai, Peter T C So, Edward S Boyden, Dushan Wadduwage,
• Abstract summary: Optical processors, built with "optical neurons", can efficiently perform high-dimensional linear operations at the speed of light. Such optical processors can now be 3D fabricated, but with a limited precision. This limitation translates to quantization of learnable parameters in optical neurons, and should be handled during the design of the optical processor.
• Score: 0.15320652338704774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical processors, built with "optical neurons", can efficiently perform high-dimensional linear operations at the speed of light. Thus they are a promising avenue to accelerate large-scale linear computations. With the current advances in micro-fabrication, such optical processors can now be 3D fabricated, but with a limited precision. This limitation translates to quantization of learnable parameters in optical neurons, and should be handled during the design of the optical processor in order to avoid a model mismatch. Specifically, optical neurons should be trained or designed within the physical-constraints at a predefined quantized precision level. To address this critical issues we propose a physics-informed quantization-aware training framework. Our approach accounts for physical constraints during the training process, leading to robust designs. We demonstrate that our approach can design state of the art optical processors using diffractive networks for multiple physics based tasks despite quantized learnable parameters. We thus lay the foundation upon which improved optical processors may be 3D fabricated in the future.

Related papers

• Quantum optical neural networks with programmable nonlinearities [0.0]
  We show that using programmable nonlinearities, rather than linear optics, offers a more efficient method for constructing quantum optical circuits. This approach significantly reduces the number of adjustable parameters needed to achieve high-fidelity operation.
  arXiv  Detail & Related papers  (2024-10-10T12:37:05Z)
• Optical training of large-scale Transformers and deep neural networks with direct feedback alignment [48.90869997343841]
  We experimentally implement a versatile and scalable training algorithm, called direct feedback alignment, on a hybrid electronic-photonic platform. An optical processing unit performs large-scale random matrix multiplications, which is the central operation of this algorithm, at speeds up to 1500 TeraOps. We study the compute scaling of our hybrid optical approach, and demonstrate a potential advantage for ultra-deep and wide neural networks.
  arXiv  Detail & Related papers  (2024-09-01T12:48:47Z)
• Photon Number-Resolving Quantum Reservoir Computing [1.1274582481735098]
  We propose a fixed optical network for photonic quantum reservoir computing that is enabled by photon number-resolved detection of the output states. This significantly reduces the required complexity of the input quantum states while still accessing a high-dimensional Hilbert space.
  arXiv  Detail & Related papers  (2024-02-09T11:28:37Z)
• Optical Extreme Learning Machines with Atomic Vapors [0.3069335774032178]
  Extreme learning machines explore nonlinear random projections to perform computing tasks on high-dimensional output spaces. This manuscript explores the possibility of using atomic gases in near-resonant conditions to implement an optical extreme learning machine. Our results suggest that these systems have the potential not only to work as an optical extreme learning machine but also to perform these computations at the few-photon level.
  arXiv  Detail & Related papers  (2024-01-08T10:19:28Z)
• Optical Quantum Sensing for Agnostic Environments via Deep Learning [59.088205627308]
  We introduce an innovative Deep Learning-based Quantum Sensing scheme. It enables optical quantum sensors to attain Heisenberg limit (HL) in agnostic environments. Our findings offer a new lens through which to accelerate optical quantum sensing tasks.
  arXiv  Detail & Related papers  (2023-11-13T09:46:05Z)
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography [55.41644538483948]
  We investigate the application of genetic and machine learning approaches to tomographic problems. We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time. We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
  arXiv  Detail & Related papers  (2022-10-27T11:37:14Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Optimization of Quantum-dot Qubit Fabrication via Machine Learning [0.0]
  We train a convolutional neural network to interpret in-line scanning electron micrographs. The strategy is exemplified by optimizing a model lithographic process within a five-dimensional design space.
  arXiv  Detail & Related papers  (2020-12-15T22:30:49Z)
• Photonics for artificial intelligence and neuromorphic computing [52.77024349608834]
  Photonic integrated circuits have enabled ultrafast artificial neural networks. Photonic neuromorphic systems offer sub-nanosecond latencies. These systems could address the growing demand for machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2020-10-30T21:41:44Z)
• 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)

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.