arXiv subjects

Martin Sevior

Publications and source records attributed to Martin Sevior.

30 records · Page 2Linked to original sources

arXiv2024

Provably Trainable Rotationally Equivariant Quantum Machine Learning

Exploiting the power of quantum computation to realise superior machine learning algorithmshas been a major research focus of recent years, but the prospects of quantum machine learning (QML) remain dampened by considerable technical challenges. A particularly significant issue is that generic QML models suffer from so-called barren plateaus in their training landscapes -- large regions where cost function gradients vanish exponentially in the number of qubits employed, rendering large models effectively untrainable. A leading strategy for combating this effect is to build problem-specific models which take into account the symmetries of their data in order to focus on a smaller, relevant subset of Hilbert space. In this work, we introduce a family of rotationally equivariant QML models built upon the quantum Fourier transform, and leverage recent insights from the Lie-algebraic study of QML models to prove that (a subset of) our models do not exhibit barren plateaus. In addition to our analytical results we numerically test our rotationally equivariant models on a dataset of simulated scanning tunnelling microscope images of phosphorus impurities in silicon, where rotational symmetry naturally arises, and find that they dramatically outperform their generic counterparts in practice.

quant-ph↗

arXiv2023

Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification

Machine learning is among the most widely anticipated use cases for near-term quantum computers, however there remain significant theoretical and implementation challenges impeding its scale up. In particular, there is an emerging body of work which suggests that generic, data agnostic quantum machine learning (QML) architectures may suffer from severe trainability issues, with the gradient of typical variational parameters vanishing exponentially in the number of qubits. Additionally, the high expressibility of QML models can lead to overfitting on training data and poor generalisation performance. A promising strategy to combat both of these difficulties is to construct models which explicitly respect the symmetries inherent in their data, so-called geometric quantum machine learning (GQML). In this work, we utilise the techniques of GQML for the task of image classification, building new QML models which are equivariant with respect to reflections of the images. We find that these networks are capable of consistently and significantly outperforming generic ansatze on complicated real-world image datasets, bringing high-resolution image classification via quantum computers closer to reality. Our work highlights a potential pathway for the future development and implementation of powerful QML models which directly exploit the symmetries of data.

quant-ph↗

arXiv2023

Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning

Quantum machine learning (QML) is emerging as an application of quantum computing with the potential to deliver quantum advantage, but its realisation for practical applications remains impeded by challenges. Amongst those, a key barrier is the computationally expensive task of encoding classical data into a quantum state, which could erase any prospective speed-ups over classical algorithms. In this work, we implement methods for the efficient preparation of quantum states representing encoded image data using variational, genetic and matrix product state based algorithms. Our results show that these methods can approximately prepare states to a level suitable for QML using circuits two orders of magnitude shallower than a standard state preparation implementation, obtaining drastic savings in circuit depth and gate count without unduly sacrificing classification accuracy. Additionally, the QML models trained and evaluated on approximately encoded data display an increased robustness to adversarially generated input data perturbations. This partial alleviation of adversarial vulnerability, possible due to the "drowning out" of adversarial perturbations while retaining the meaningful large-scale features of the data, constitutes a considerable benefit for approximate state preparation in addition to lessening the requirements of the quantum hardware. Our results, based on simulations and experiments on IBM quantum devices, highlight a promising pathway for the future implementation of accurate and robust QML models on complex datasets relevant for practical applications, bringing the possibility of NISQ-era QML advantage closer to reality.

quant-ph↗

arXiv2023

Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavour Tagging

The recent physical realisation of quantum computers with dozens to hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabilities. One area that has received particular attention is quantum machine learning (QML), the study of machine learning algorithms running natively on quantum computers. Such algorithms have begun to be applied to data intensive problems in particle physics, driven by the expected increased capacity for pattern recognition of quantum computers. In this work we develop and apply QML methods to B meson flavour tagging, an important component of experiments in particle physics which probe heavy quark mixing and CP violation in order to obtain a better understanding of the matter-antimatter asymmetry observed in the universe. We simulate boosted ensembles of quantum support vector machines (QSVMs) based on both conventional qubit-based and continuous variable architectures, attaining effective tagging efficiencies of 28.0% and 29.2% respectively, comparable with the leading published result of 30.0% using classical machine learning algorithms. The ensemble nature of our classifier is of particular importance, doubling the effective tagging efficiency of a single QSVM, which we find to be highly prone to overfitting. These results are obtained despite the strong constraint of working with QSVM architectures that are classically simulable, and we find evidence that continuous variable QSVMs beyond the classically simulable regime may be able to realise even higher performance, surpassing the reported classical results, when sufficiently powerful quantum hardware is developed to execute them.

quant-ph↗

arXiv2023

Permutation Invariant Encodings for Quantum Machine Learning with Point Cloud Data

Quantum Computing offers a potentially powerful new method for performing Machine Learning. However, several Quantum Machine Learning techniques have been shown to exhibit poor generalisation as the number of qubits increases. We address this issue by demonstrating a permutation invariant quantum encoding method, which exhibits superior generalisation performance, and apply it to point cloud data (three-dimensional images composed of points). Point clouds naturally contain permutation symmetry with respect to the ordering of their points, making them a natural candidate for this technique. Our method captures this symmetry in a quantum encoding that contains an equal quantum superposition of all permutations and is therefore invariant under point order permutation. We test this encoding method in numerical simulations using a Quantum Support Vector Machine to classify point clouds drawn from either spherical or toroidal geometries. We show that a permutation invariant encoding improves in accuracy as the number of points contained in the point cloud increases, while non-invariant quantum encodings decrease in accuracy. This demonstrates that by implementing permutation invariance into the encoding, the model exhibits improved generalisation.

quant-ph↗

arXiv2023

A Time Projection Chamber to Search for Feebly Interacting Bosons via Proton Induced Nuclear Reactions

We propose a new Time Projection Chamber particle detector (TPC) to search for the existence of feebly-interacting bosons and to investigate the existence of the X17 boson, proposed by the ATOMKI group to explain anomalous results in the angular distributions of electron-positron pairs created in proton-induced nuclear reactions. Our design will provide 200 times greater sensitivity than ATOMKI and the program of research will also provide world-leading limits on feebly interacting bosons in the mass range of 5 - 25 MeV.

hep-ex↗

arXiv2022

Benchmarking Adversarially Robust Quantum Machine Learning at Scale

Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophistication, neural networks can be easily fooled by carefully designed malicious inputs known as adversarial attacks. While such vulnerabilities remain a serious challenge for classical neural networks, the extent of their existence is not fully understood in the quantum ML setting. In this work, we benchmark the robustness of quantum ML networks, such as quantum variational classifiers (QVC), at scale by performing rigorous training for both simple and complex image datasets and through a variety of high-end adversarial attacks. Our results show that QVCs offer a notably enhanced robustness against classical adversarial attacks by learning features which are not detected by the classical neural networks, indicating a possible quantum advantage for ML tasks. Contrarily, and remarkably, the converse is not true, with attacks on quantum networks also capable of deceiving classical neural networks. By combining quantum and classical network outcomes, we propose a novel adversarial attack detection technology. Traditionally quantum advantage in ML systems has been sought through increased accuracy or algorithmic speed-up, but our work has revealed the potential for a new kind of quantum advantage through superior robustness of ML models, whose practical realisation will address serious security concerns and reliability issues of ML algorithms employed in a myriad of applications including autonomous vehicles, cybersecurity, and surveillance robotic systems.

quant-ph↗

arXiv2021

Quantum Support Vector Machines for Continuum Suppression in B Meson Decays

Quantum computers have the potential to speed up certain computational tasks. A possibility this opens up within the field of machine learning is the use of quantum techniques that may be inefficient to simulate classically but could provide superior performance in some tasks. Machine learning algorithms are ubiquitous in particle physics and as advances are made in quantum machine learning technology there may be a similar adoption of these quantum techniques. In this work a quantum support vector machine (QSVM) is implemented for signal-background classification. We investigate the effect of different quantum encoding circuits, the process that transforms classical data into a quantum state, on the final classification performance. We show an encoding approach that achieves an average Area Under Receiver Operating Characteristic Curve (AUC) of 0.848 determined using quantum circuit simulations. For this same dataset the best classical method tested, a classical Support Vector Machine (SVM) using the Radial Basis Function (RBF) Kernel achieved an AUC of 0.793. Using a reduced version of the dataset we then ran the algorithm on the IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703. As further improvements to the error rates and availability of quantum computers materialise, they could form a new approach for data analysis in high energy physics.

quant-ph↗

arXiv2019

Progress and challenges in the study of direct CP violation and $γ$ determinations: summary of CKM 2018 working group V

In the study of CP violation within and beyond the Standard Model of particle physics, pure hadronic B decays are the key players. Improving the determination of the CKM angle $γ$ is one of the important tasks in this endeavour. Besides, the full description of pure hadronic decays due to their strong interaction dynamics remains one of the challenges. In this summary, we discuss the recent results and progress on $γ$ determinations and direct CP violation in pure hadronic B decays as presented at CKM 2018.

hep-ph↗

arXiv2018

The use of adversaries for optimal neural network training

B-decay data from the Belle experiment at the KEKB collider have a substantial background from $e^{+}e^{-}\to q \bar{q}$ events. To suppress this we employ deep neural network algorithms. These provide improved signal from background discrimination. However, the deep neural network develops a substantial correlation with the $ΔE$ kinematic variable used to distinguish signal from background in the final fit due to its relationship with input variables. The effect of this correlation is reduced by deploying an adversarial neural network. Overall the adversarial deep neural network performs better than a Boosted Decision Tree algorithimn and a commercial package, NeuroBayes, which employs a neural net with a single hidden layer.

physics.data-an↗

arXiv1997

Working group on $ππ$ and $π$N interactions - Summary

This is the summary of the working group on $ππ$ and $π$N interactions of the Chiral Dynamics Workshop in Mainz, September 1-5, 1997. Each talk is represented by an extended one page abstract. Some additional remarks by the convenors are added

hep-ph↗

arXiv1993

Investigation of Long Baseline Neutrino Oscillations from Experiments at KAON

The proposed KAON factory at the Triumf Laboratory in Vancouver, Canada provides a unique opportunity for high statistics long baseline neutrino oscillation experiments. Several possibilities are under active consideration. In this paper we describe the theoretical expectations for a {\it very} long baseline experiment in which the neutrinos are directed towards, and detected at the Superkamiokande detector in Kamioka, Japan 7200 km away. We find that in the first year this experiment would probe oscillations down to about $Δm^2_0 = 9 \times 10^{-5} eV^2$ for maximal mixing, and if $Δm^2_0 \geq 5 \times 10^{-4} eV^2$ it would be sensitive to $sin^2(2 θ_0) \geq 0.2$. These results are compared with a more modest proposal of a 100 km baseline with a 6300 tonne detector which would probe $Δm^2_0 \geq 1.3 \times 10^{-3} eV^2$ for maximal mixing, and if $Δm^2_0 \geq 0.02 eV^2$ it would be sensitive to $sin^2(2 θ_0) \geq 0.01$. These experiments would either confirm or rule out the entire range of parameters allowed by Kamiokande and IMB to explain the deficit in the ratio of $ν_μ$ to $ν_e$ in current atmospheric neutrino experiments. The Kamiokande experiment would also investigate matter enhanced neutrino oscillations (the MSW effect) down to about $Δm^2 = 3 \times 10^{-4} eV^2$.

hep-ph↗