Quantum coherent control in pulsed waveguide optomechanics

Zhang, Junyin; Zhu, Changlong; Wolff, Christian; Stiller, Birgit

doi:10.1103/physrevresearch.5.013010

Cited by 6 publications

(1 citation statement)

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“…The recurrent operation of OREO is based on the interaction of optical and acoustic waves through SBS, which is one of the most prominent third-order nonlinear effects and describes the coherent coupling of two optical waves, data and control, to an acoustic wave in a material. The dynamic is illustrated in Figure 1C and follows from the Hamiltonian (1) [30][31][32] :…”

Section: Concept Of An Optoacoustic Recurrent Operatormentioning

confidence: 99%

An optoacoustic field-programmable perceptron for recurrent neural networks

Becker,

Englund,

Stiller

2023

Preprint

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A critical feature in signal processing is the ability to interpret correlations in time series signals, such as speech. Machine learning systems process this contextual information by tracking internal states in recurrent neural networks (RNNs), but these can cause memory and processor bottlenecks in applications from edge devices to data centers, motivating research into new analog inference architectures. But whereas photonic accelerators have demonstrated big leaps in uni-directional feedforward deep neural network (DNN) inference, the bi-directional architecture of RNNs presents a unique challenge: the need for a short-term memory that (i) programmably transforms optical waveforms with phase coherence , (ii) minimizes added noise, and (iii) enables programmable readily scales to large neuron counts. Here, we address this challenge by introducing an optoacoustic recurrent operator (OREO) that simultaneously meets (i,ii,iii). Specifically, we experimentally demonstrate an OREO that contextualizes and computes the information carried by a sequence of optical pulses via acoustic waves. We show that the acoustic waves act as a link between the different optical pulses, capturing the optical information and using it to manipulate the subsequent operations. Our approach can be controlled optically on a pulse-by-pulse basis, offering simple reconfigurability. We use this feature to demonstrate a recurrent drop-out, which excludes optical input pulses from the recurrent operation. We apply OREO as an acceptor to recognize up-to 27 patterns in a sequence of optical pulses. Finally, we introduce a DNN architecture that uses the OREO as bi-directional perceptrons to enable new classes of DNNs in coherent optical signal processing.

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Section: Concept Of An Optoacoustic Recurrent Operatormentioning

confidence: 99%

An optoacoustic field-programmable perceptron for recurrent neural networks

Becker,

Englund,

Stiller

2023

Preprint

Self Cite

View full text Add to dashboard Cite

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An optoacoustic field-programmable perceptron for recurrent neural networks

Becker,

Englund,

Stiller

2024

Nat Commun

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Recurrent neural networks (RNNs) can process contextual information such as time series signals and language. But their tracking of internal states is a limiting factor, motivating research on analog implementations in photonics. While photonic unidirectional feedforward neural networks (NNs) have demonstrated big leaps, bi-directional optical RNNs present a challenge: the need for a short-term memory that (i) programmable and coherently computes optical inputs, (ii) minimizes added noise, and (iii) allows scalability. Here, we experimentally demonstrate an optoacoustic recurrent operator (OREO) which meets (i, ii, iii). OREO contextualizes the information of an optical pulse sequence via acoustic waves. The acoustic waves link different optical pulses, capturing their information and using it to manipulate subsequent operations. OREO’s all-optical control on a pulse-by-pulse basis offers simple reconfigurability and is used to implement a recurrent drop-out and pattern recognition of 27 optical pulse patterns. Finally, we introduce OREO as bi-directional perceptron for new classes of optical NNs.

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