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Arbitrary Ground State Observables from Quantum Computed Moments
Harish J. Vallury, Lloyd C. L. Hollenberg
The determination of ground state properties of quantum systems is a fundamental problem in physics and chemistry, and is considered a key application of quantum computers. A commo…
Characterization of entanglement on superconducting quantum computers of up to 414 qubits
John F Kam, Haiyue Kang, Charles D Hill +2
As quantum technology advances and the size of quantum computers grow, it becomes increasingly important to understand the extent of quality in the devices. As large-scale entangle…
Kernel Alignment for Quantum Support Vector Machines Using Genetic Algorithms
Floyd M. Creevey, Jamie A. Heredge, Martin E. Sevior +1
The data encoding circuits used in quantum support vector machine (QSVM) kernels play a crucial role in their classification accuracy. However, manually designing these circuits po…
Precision ground-state energy calculation for the water molecule on a superconducting quantum processor
Michael A. Jones, Harish J. Vallury, Lloyd C. L. Hollenberg
The accurate computation of properties of large molecular systems is classically infeasible and is one of the applications in which it is hoped that quantum computers will demonstr…
Quantum computer error structure probed by quantum error correction syndrome measurements
Spiro Gicev, Lloyd C. L. Hollenberg, Muhammad Usman
With quantum devices rapidly approaching qualities and scales needed for fault tolerance, the validity of simplified error models underpinning the study of quantum error correction…
Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning
Maxwell T. West, Azar C. Nakhl, Jamie Heredge +4
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 re…
Quantum autoencoders using mixed reference states
Hailan Ma, Gary J. Mooney, Ian R. Petersen +2
One of the fundamental tasks in quantum information theory is quantum data compression, which can be realized via quantum autoencoders that first compress quantum states to low-dim…
Towards quantum enhanced adversarial robustness in machine learning
Maxwell T. West, Shu-Lok Tsang, Jia S. Low +5
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input…
Permutation Invariant Encodings for Quantum Machine Learning with Point Cloud Data
Jamie Heredge, Charles Hill, Lloyd Hollenberg +1
Quantum Computing offers a potentially powerful new method for performing Machine Learning. However, several Quantum Machine Learning techniques have been shown to exhibit poor gen…
GASP -- A Genetic Algorithm for State Preparation
Floyd M. Creevey, Charles D. Hill, Lloyd C. L. Hollenberg
The efficient preparation of quantum states is an important step in the execution of many quantum algorithms. In the noisy intermediate-scale quantum (NISQ) computing era, this is…