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SPIE Metamaterials Conference

Hybrid quantum photonics

Vladimir M. Shalaev

Published May 18, 2022• 1 citation
Publisher

Abstract

We will discuss some recent ideas and developments on how plasmonics and machine learning can be employed to dramatically speed up quantum process to make them immune to decoherence and how they can significantly improve the performance of quantum photonics devices and systems. Our recent discovery of single-photon emitters in technologically important SiN platform will be also discussed.

DOI
10.1117/12.2624301

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We will discuss some recent ideas and developments on how plasmonics and machine learning can be employed to dramatically speed up quantum process to make them immune to decoherence and how they can significantly improve the performance of quantum photonics devices and systems. Our recent discovery of single-photon emitters in technologically important SiN platform will be also discussed.
Our group recently discovered bright, stable, linearly polarized, and high-purity sources of single-photon emission at room temperature in SiN. Currently, we study the possibility of generating indistinguishable photons by plasmonic speed-up of photoemission, which may enable broader applications of SiN single-photon sources in quantum information technology. The enhancement of the light-matter interaction with plasmonic materials can shorten the spontaneous emission time to beat the dephasing time and achieve coherence even at non-cryogenic temperatures. Our findings spark further studies of quantum emitters toward deeper understanding of their nature, deterministic formation, and scalable integration with on-chip quantum photonic circuitry.
The development of on-chip, CMOS-compatible quantum photonics is critical for future scalable quantum communications, quantum computing, and quantum sensing. Integrated photonic waveguides, photonic resonators, and single-photon emitters are essential building blocks for such a purpose. In this talk, I will present how machine learning (ML) can enhance the quantum properties of these building blocks, specifically the indistinguishability (I) of the generated single photons, with a further decrease in quantum decoherence.
In recent years, Machine Learning (ML) has started to be ubiquitously applied to practically most of the human activity domains. Although traditional, or classical machine learning (CML) approaches are useful in solving many complex tasks, there are still many challenges that such approaches are facing. One issue is the limitation in the processing speed of current silicon technology based computers, which made researchers look to the underlying quantum theory principles and try to run complex quantum computing experiments. In future, in order to develop more complex artificial intelligence systems, we will have to process huge amounts of data at high speed, and the existing classical computing will probably not serve well this purpose, due to silicon technology limitations. A different technology that can handle huge volumes of data and at high speed is needed. In recent years, the progress in quantum computing research seems to provide hopeful answers to overcome the speed processing barrier. This is very important for the training of many computational intensive machine learning models. The latest advancements in quantum technology appear to be promising, which can boost the field of machine learning overall. In this chapter, we discuss the transition from classical machine learning to quantum machine learning (QML) and explore the recent progress in this domain. QML is not only associated with the development of high-performance machine learning algorithms that can run on a quantum computer with significant performance improvements but also has a very diverse meaning in other aspects. The chapter tried to touch those aspects in brief too, but the main focus is on the advancements in the field of developing machine learning algorithms that will run on a quantum computer.
One of the main challenges in drug discovery is to find molecules that bind specifically and strongly to their target protein while having minimal binding to other proteins. By predicting binding affinity, it is possible to identify the most promising candidates from a large pool of potential compounds, reducing the number of compounds that need to be tested experimentally. Recently, deep learning methods have shown superior performance than traditional computational methods for making accurate predictions on large datasets. However, the complexity and time-consuming nature of these methods have limited their usage and development. Quantum machine learning is an emerging technology that has the potential to improve many classical machine learning algorithms. In this work we present a hybrid quantum-classical convolutional neural network, which is able to reduce by 20% the complexity of the classical network while maintaining optimal performance in the predictions. Additionally, it results in a significant time savings of up to 40% in the training process, which means a meaningful speed up of the drug discovery process.