Searching for New Physics using Classical and Quantum Machine Learning

The development of machine learning (ML) has provided the High Energy Physics (HEP) community with new methods of analysing collider and Monte-Carlo generated data. As experiments are upgraded to generate an increasing number of events, classical techniques can be supplemented with ML to increase our ability to find signs of New Physics in the high-dimensional event data. This thesis presents three methods of performing supervised and unsupervised searches using novel ML methods. The first depends on the use of an autoencoder to perform an unsupervised anomaly detection search. We demonstrate that this method allows you to carry out a data-driven, model-independent search for New Physics. Furthermore, we show that by extending the model with an adversary we can account for systematic errors that may arise from experiments. The second method develops a form of quantum machine learning to be applied to a supervised search. Using a variational quantum classifier (a neural network style model built from quantum information principles) we demonstrate a quantum advantage arises when compared to a classical network. Finally, we make use of the continuous-variable (CV) paradigm of quantum computing to build an unsupervised method of classifying events stored as graph data. Gaussian boson sampling provides an example of a quantum advantage unique to the CV method of quantum computing and allows our events to be used in an anomaly detector model built using the Q-means clustering algorithm

Quantum machine learning for particle physics using a variational quantum classifier

Quantum machine learning aims to release the prowess of quantum computing to improve machine learning methods. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving classification problems. Our algorithm is designed for existing and near-term quantum devices. We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. By applying this algorithm to a resonance search in di-top final states, we find that this method has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method. The classifiers ability to be trained on small amounts of data indicates its benefits in data-driven classification problems

Unsupervised event classification with graphs on classical and photonic quantum computers

Photonic Quantum Computers provide several benefits over the discrete qubit-based paradigm of quantum computing. By using the power of continuous-variable computing we build an anomaly detection model to use on searches for New Physics. Our model uses Gaussian Boson Sampling, a #P-hard problem and thus not efficiently accessible to classical devices. This is used to create feature vectors from graph data, a natural format for representing data of high-energy collision events. A simple K-means clustering algorithm is used to provide a baseline method of classification. We then present a novel method of anomaly detection, combining the use of Gaussian Boson Sampling and a quantum extension to K-means known as Q-means. This is found to give equivalent results compared to the classical clustering version while also reducing the O complexity, with respect to the sample’s feature-vector length, from O(N) to O(log(N))

Quantum optimization of complex systems with a quantum annealer

We perform an in-depth comparison of quantum annealing with several classical optimization techniques, namely, thermal annealing, Nelder-Mead, and gradient descent. The focus of our study is large quasicontinuous potentials that must be encoded using a domain wall encoding. To do this, it is important to first understand the properties of a system that is discretely encoded onto an annealer, in terms of its quantum phases, and the importance of thermal versus quantum effects. We therefore begin with a direct study of the 2D Ising model on a quantum annealer, and compare its properties directly with those of the thermal 2D Ising model. These properties include an Ising-like phase transition that can be induced by either a change in “quantumness” of the theory (by way of the transverse field component on the annealer), or by scaling the Ising couplings up or down. This behavior is in accord with what is expected from the physical understanding of the quantum system. We then go on to demonstrate the efficacy of the quantum annealer at minimizing several increasingly hard two-dimensional potentials. For all potentials, we find the general behavior that Nelder-Mead and gradient descent methods are very susceptible to becoming trapped in false minima, while the thermal anneal method is somewhat better at discovering the true minimum. However, and despite current limitations on its size, the quantum annealer performs a minimization very markedly better than any of these classical techniques. A quantum anneal can be designed so the system almost never gets trapped in a false minimum, and rapidly and successfully minimizes the potentials

Adversarially-trained autoencoders for robust unsupervised new physics searches

Machine learning techniques in particle physics are most powerful when they are trained directly on data, to avoid sensitivity to theoretical uncertainties or an underlying bias on the expected signal. To be able to train on data in searches for new physics, anomaly detection methods are imperative, which can be realised by an autoencoder acting as an unsupervised classifier. The last source of uncertainties affecting the classifier are then experimental uncertainties in the reconstruction of the final-state objects. To mitigate their effect on the classifier and to allow for a realistic assessment of the method, we propose to combine the autoencoder with an adversarial neural network to remove its sensitivity to the smearing of the final-state objects. We quantify its effect and show that one can achieve a robust anomaly detection in resonance-induced tt¯ final states

Novel B-decay signatures of light scalars at high energy facilities

We study the phenomenology of light scalars of masses m1 and m2 coupling to heavy flavor-violating vector bosons of mass mV. For m1;2 ≲ few GeV, this scenario triggers the rare B meson decays B0s → 3μþ3μ−, B0 → 3μþ3μ−, Bþ → Kþ3μþ3μ−, and B0s → K03μþ3μ−; the last two being the most important ones for m1 ∼ m2. None of these signals have been studied experimentally; therefore, we propose analyses to test these channels at the LHCb. We demonstrate that the reach of this facility extends to branching ratios as small as 6.0 × 10−9, 1.6 × 10−9, 5.9 × 10−9, and 1.8 × 10−8 for the aforementioned channels, respectively. For m1;2 ≫ Oð1Þ GeV, we show that slightly modified versions of current multilepton and multitau searches at the LHC can probe wide regions of the parameter space of this scenario. Altogether, the potential of the searches we propose outperform other constraints such as those from meson mixing

AXL inhibition extinguishes primitive JAK2 mutated myeloproliferative neoplasm progenitor cells

Abstract. Myeloproliferative neoplasms (MPN) are clonal stem cell associated disorders inclusive of chronic myeloid leukemia (CML), Polycythaemia vera (PV), myelofibrosis (MF), and essential thrombocythemia (ET). They are characterized by increased production of myeloid cells with minimal effects on terminal differentiation but can undergo transformation to acute leukemias. PV is the most common chronic myeloproliferative neoplasm and in the majority of cases is characterized by a V617F point mutation in JAK2. This JAK2 activating mutation is also found in about half the patients with MF and ET. Such aberrant proteins offer great potential for the treatment of these diseases however inhibitors to JAK2 have had limited success in the clinic in terms of curing the disease. We have previously used advanced proteomic techniques to identify drug targets and thus develop novel treatment strategies to distinguish the leukemic clone in both CML and PV. Here, we build on our proteomic data sets to characterize a new target, the receptor tyrosine kinase AXL. AXL is overexpressed in acute myeloid leukemia and importantly small molecule inhibitors have been developed which are currently in clinical trial hence offer the opportunity to repurpose this drug for the treatment of MPNs. We demonstrate that AXL is upregulated and activated in JAK2 associated MPNs. Further we show that inhibition of AXL preferentially kills early hemopoietic stem cells from PV patients and as such represents a promising therapeutic approach for JAK2 driven MPNs

Erratum:Proteomic analysis of JAK2V617F-induced changes identifies potential new combinatorial therapeutic approaches

Correction to: Leukemia (2017) 31, 2717–2725; doi:https://doi.org/10.1038/leu.2017.143; published online 23 May 201