arXiv subjects

Lloyd Hollenberg

Publications and source records attributed to Lloyd Hollenberg.

5 recordsLinked to original sources

arXiv2024

Non-Unitary Quantum Machine Learning

We introduce several probabilistic quantum algorithms that overcome the normal unitary restrictions in quantum machine learning by leveraging the Linear Combination of Unitaries (LCU) method. Among our investigations are quantum native implementations of Residual Networks (ResNet), where we show that residual connections between layers of a variational ansatz can prevent barren plateaus in models which would otherwise contain them. Secondly, we implement a quantum analogue of average pooling layers from convolutional networks using single qubit controlled basic arithmetic operators and show that the LCU success probability remains stable for the MNIST database. This method can be further generalised to convolutional filters, while using exponentially fewer controlled unitaries than previous approaches. Finally, we propose a general framework for applying a linear combination of irreducible subspace projections on quantum encoded data. This enables a quantum state to remain within an exponentially large space, while selectively amplifying specific subspaces relative to others, alleviating simulability concerns that arise when fully projecting to a polynomially sized subspace. We demonstrate improved classification performance for partially amplified permutation invariant encoded point cloud data when compared to non-invariant or fully permutation invariant encodings. We also demonstrate a novel rotationally invariant encoding for point cloud data via Schur-Weyl duality. These quantum computing frameworks are all constructed using the LCU method, suggesting that further novel quantum machine learning algorithms could be created by utilising the LCU technique.

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↗

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↗

arXiv2016

Remote Nanodiamond Magnetometry

Optical fibres have transformed the way people interact with the world and now permeate many areas of science. Optical fibres are traditionally thought of as insensitive to magnetic fields, however many application areas from mining to biomedicine would benefit from fibre-based remote magnetometry devices. In this work, we realise such a device by embedding nanoscale magnetic sensors into tellurite glass fibres. Remote magnetometry is performed on magnetically active defect centres in nanodiamonds embedded into the glass matrix. Standard optical magnetometry techniques are applied to initialize and detect local magnetic field changes with a measured sensitivity of 26 micron Tesla/square root(Hz). Our approach utilizes straight-forward optical excitation, simple focusing elements, and low power components. We demonstrate remote magnetometry by direct reporting of the magnetic ground states of nitrogen-vacancy defect centres in the optical fibres. In addition, we present and describe theoretically an all-optical technique that is ideally suited to remote fibre-based sensing. The implications of our results broaden the applications of optical fibres, which now have the potential to underpin a new generation of medical magneto-endoscopes and remote mining sensors.

physics.optics↗

arXiv2010

Optically induced spin polarisation of the NV- centre in diamond: role of electron-vibration interaction

The novel aspect of the centre (NV-) in diamond is the high degree of spin polarisation achieved through optical illumination. In this paper it is shown that the spin polarisation occurs as a consequence of an electron-vibration interaction combined with spin-orbit interaction, and an electronic model involving these interactions is developed to account for the observed polarisation.

cond-mat.mtrl-sci↗