Advances in quantum machine learning

Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.Comment: 38 pages, 17 Figure

Quantum convolutional neural networks for high energy physics

"Artificial Intelligence is a tool that is increasingly becoming an integral element of scientific research. High Energy Physics, whose experiments produce some of the largest amounts of data in science, is no exception. For this reason, the objective of this thesis is to develop a pipeline for classifying backgrounds and signals particle jets using Machine Learning (ML) techniques, specifically convolutional neural networks (CNN). Particle jets have proven to be a very powerful tool for studying particle collisions at accelerators such as CMS and ATLAS, at the LHC, where the constituents of these events hadronize or decay so quickly that they are very difficult to detect. Jets are objects that seek to retrieve information about these particles by encapsulating the energy depositions that were indeed sensed by the detector. So having an algorithm that can reliably tell us which particle generated a given jet is not a straightforward assignment, even more so taking into account that many approaches can be taken to this problem depending on the way in which we believe it is most convenient to arrange our data"

Predicting Hazardous Driving Behaviour with Quantum Neural Networks

Quantum Neural Networks (QNN) were used to predict both future steering wheel signals and upcoming lane departures for N=34 drivers undergoing 37 h of sleep deprivation. The drivers drove in a moving-base truck simulator for 55 min once every third hour, resulting in 31 200 km of highway driving, out of which 8 432 km were on straights. Predicting the steering wheel signal one time step ahead, 0.1 s, was achieved with a 15-40-20-1 time-delayed feed-forward QNN with a root-mean-square error of RMSEtot = 0.007 a.u. corresponding to a 0.4 % relative error. The best prediction of the number of lane departures during the subsequent 10 s was achieved using the maximum peak-to-peak amplitude of the steering wheel signal from the previous ten 1 s segments as inputs to a 10-15-5-1 time-delayed feed-forward QNN. A correct prediction was achieved in 55 % of cases and the overall sensitivity and specificity were 31 % and 80 %, respectively.Kvantneuronätverk (QNN) användes för att förutsäga både framtida rattsignaler och filavkörningar för N=34 bilförare som genomgick 37 timmars vaka. Bilförarna körde 55 min var tredje timme i en lastbilssimulator på en rörlig plattform, vilket resulterade i 31 200 km landsvägskörning, varav 8 432 km inföll på raksträckor. Ett 15-40-20-1 strukturerat tidsförskjutet, framåtkopplat QNN användes för att förutsäga rattsignalen ett tidssteg framåt, 0,1 s, vilket lyckades med ett kvadratiskt medelvärdesfel på RMSEtot = 0.007 a.u., som motsvarar ett relativt fel på 0,4 %. Den bästa föutsägelsen av antalet filavkörningar under de följande 10 s uppnåddes genom att som in-signal till ett 10-15-5-1 tidsförskjutet, framåtkopplat QNN använda skillnaden mellan maximi- och minimivärdet i rattsignalen i de tio föregående 1 s segmenten. En korrekt förutsägelse uppnåddes i 55 % av fallen och den totala sensitiviteten var 31 % medan specificiteten var 80 %.Kvanttineuroverkkoja (QNN) käytettiin ennustamaan tulevaa rattisignaalia ja tulevia kaistalta poikkeamisia 37 tuntia valvoneille N=34 kuljettajalle. Kuljettajat ajoivat liikuvapohjaisesssa rekkasimulaattorissa 55 min ajan joka kolmas tunti, eli kokonaisuudessaan 31 200 km maantieajoa, joista 8 432 km olivat suorilla. Rattisignaalin ennustaminen yhden aika-askeleen eteenpäin, 0,1 s, suoritettin aikaviivästetyllä eteenpäinkytkeyllä QNN:llä, jolla oli 15-40-20-1 rakenne. Neliöllinen keskiarvollinen virhe oli RMSEtot = 0.007 a.u., mikä vastaa 0,4 % suhteellista virhettä. Paras ennustus kaistalta poikkeamisten määrälle tulevan 10 s aikana saavutettiin käyttämällä sisäänmenona rattisignaalin suurinta huipusta huippuun amplitudia kymmenen edellisten 1 s pätkien ajalta ja aikaviivästettyä eteenpäinkytkettyä 10-15-5-1 QNN:ää. Oikeaa ennustusta saavutettiin 55 % tapauksista ja sensitiviteetti oli 31 % ja spesifisiteetti oli 80 %

Analog Photonics Computing for Information Processing, Inference and Optimisation

This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

Artificial Intelligence in Classical and Quantum Photonics

The last decades saw a huge rise of artificial intelligence (AI) as a powerful tool to boost industrial and scientific research in a broad range of fields. AI and photonics are developing a promising two-way synergy: on the one hand, AI approaches can be used to control a number of complex linear and nonlinear photonic processes, both in the classical and quantum regimes; on the other hand, photonics can pave the way for a new class of platforms to accelerate AI-tasks. This review provides the reader with the fundamental notions of machine learning (ML) and neural networks (NNs) and presents the main AI applications in the fields of spectroscopy and chemometrics, computational imaging (CI), wavefront shaping and quantum optics. The review concludes with an overview of future developments of the promising synergy between AI and photonics

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field