Particle collisions at accelerators such as the Large Hadron Collider, recorded and analyzed by experiments such as ATLAS and CMS, enable exquisite measurements of the Standard Model and searches for new phenomena. Simulations of collision events at these detectors have played a pivotal role in shaping the design of future experiments and analyzing ongoing ones. However, the quest for accuracy in Large Hadron Collider (LHC) collisions comes at an imposing computational cost, with projections estimating the need for millions of CPU-years annually during the High Luminosity LHC (HL-LHC) run \cite{collaboration2022atlas}. Simulating a single LHC event with \textsc{Geant4} currently devours around 1000 CPU seconds, with simulations of the calorimeter subdetectors in particular imposing substantial computational demands \cite{rousseau2023experimental}. To address this challenge, we propose a conditioned quantum-assisted deep generative model. Our model integrates a conditioned variational autoencoder (VAE) on the exterior with a conditioned Restricted Boltzmann Machine (RBM) in the latent space, providing enhanced expressiveness compared to conventional VAEs. The RBM nodes and connections are meticulously engineered to enable the use of qubits and couplers on D-Wave's Pegasus-structured \textit{Advantage} quantum annealer (QA) for sampling. We introduce a novel method for conditioning the quantum-assisted RBM using \textit{flux biases}. We further propose a novel adaptive mapping to estimate the effective inverse temperature in quantum annealers. The effectiveness of our framework is illustrated using Dataset 2 of the CaloChallenge \cite{calochallenge}.

With the approach of the High Luminosity Large Hadron Collider (HL-LHC) era set to begin particle collisions by the end of this decade, it is evident that the computational demands of traditional collision simulation methods are becoming increasingly unsustainable. Existing approaches, which rely heavily on first-principles Monte Carlo simulations for modeling event showers in calorimeters, are projected to require millions of CPU-years annually -- far exceeding current computational capacities. This bottleneck presents an exciting opportunity for advancements in computational physics by integrating deep generative models with quantum simulations. We propose a quantum-assisted hierarchical deep generative surrogate founded on a variational autoencoder (VAE) in combination with an energy conditioned restricted Boltzmann machine (RBM) embedded in the model's latent space as a prior. By mapping the topology of D-Wave's Zephyr quantum annealer (QA) into the nodes and couplings of a 4-partite RBM, we leverage quantum simulation to accelerate our shower generation times significantly. To evaluate our framework, we use Dataset 2 of the CaloChallenge 2022. Through the integration of classical computation and quantum simulation, this hybrid framework paves way for utilizing large-scale quantum simulations as priors in deep generative models.

Department of Physics and Astronomy, University of Waterloo, Ontario N2L 3G1, Canada The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events. Large amounts of Monte Carlo (MC) simulation will be required to constrain the statistical uncertainties of the simulated datasets below these of the experimental data. Modelling of high-energy particles propagating through the calorimeter section of the detector is the most computationally intensive MC simulation task. We introduce a technique combining recent advancements in generative models and quantum annealing for fast and efficient simulation of high-energy particle-calorimeter interactions. The Large Hadron Collider (LHC) is the highest energy particle showers is critical to enable the highest quality particle accelerator in the world, and currently collides measurements, but simulating each shower from first protons at s = 13.6 TeV at a rate of 2 10 We deploy a restricted "High-Luminosity LHC" (HL-LHC) dataset will enable Boltzmann machine (RBM) to encode a rich description significantly more precise measurements of the Higgs boson of particle showers in detectors, and use quantum and other Standard Model particles.

Entity typing (ET) is the problem of assigning labels to given entity mentions in a sentence. Existing works for ET require knowledge about the domain and target label set for a given test instance. ET in the absence of such knowledge is a novel problem that we address as ET in the wild. We hypothesize that the solution to this problem is to build supervised models that generalize better on the ET task as a whole, rather than a specific dataset. In this direction, we propose a Collective Learning Framework (CLF), which enables learning from diverse datasets in a unified way. The CLF first creates a unified hierarchical label set (UHLS) and a label mapping by aggregating label information from all available datasets. Then it builds a single neural network classifier using UHLS, label mapping, and a partial loss function. The single classifier predicts the finest possible label across all available domains even though these labels may not be present in any domain-specific dataset. We also propose a set of evaluation schemes and metrics to evaluate the performance of models in this novel problem. Extensive experimentation on seven diverse real-world datasets demonstrates the efficacy of our CLF.

Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions into more than 100 types. The type set can span various domains including biomedical (e.g., disease, gene), sport (e.g., sports event, sports player), religion and mythology (e.g., religion, god) and entertainment (e.g., movies, music). Most of the existing literature for Entity Recognition (ER) focuses on coarse-grained entity recognition (CgER), i.e., recognition of entities belonging to few types such as person, location and organization. In the past two decades, several manually annotated datasets spanning different genre of texts were created to facilitate the development and evaluation of CgER systems (Nadeau and Sekine 2007). The state-of-the-art CgER systems use supervised statistical learning models trained on manually annotated datasets (Ma and Hovy 2016). In contrast, FgER systems are yet to match the performance level of CgER systems. There are two major challenges associated with failure of FgER systems. First, manually annotating a large-scale multi-genre training data for FgER task is expensive, time-consuming and error-prone. Note that, a human-annotator will have to choose a subset of types from a large set of types and types for the same entity might differ in sentences based on the contextual information. Second, supervised statistical learning models when trained on automatically generated noisy training data fits to noise, impacting the model’s performance. The objective of my thesis is to create a FgER system by exploring an off the beaten path which can eliminate the need for manually annotating large-scale multi-genre training dataset. The path includes: (1) automatically generating a large-scale single-genre training dataset, (2) noise-aware learning models that learn better in noisy datasets, and (3) use of knowledge transfer approaches to adapt FgER system to different genres of text.