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Fully convolutional 3D neural network decoders for surface codes with syndrome circuit noise
Spiro Gicev, Lloyd C. L. Hollenberg, Muhammad Usman
Artificial Neural Networks (ANNs) are a promising approach to the decoding problem of Quantum Error Correction (QEC), but have observed consistent difficulty when generalising perf…
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…
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Maxwell T. West, Sarah M. Erfani, Christopher Leckie +3
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophisticati…
Noise-robust ground state energy estimates from deep quantum circuits
Harish J. Vallury, Michael A. Jones, Gregory A. L. White +3
In the lead up to fault tolerance, the utility of quantum computing will be determined by how adequately the effects of noise can be circumvented in quantum algorithms. Hybrid quan…
A kernel-based quantum random forest for improved classification
Maiyuren Srikumar, Charles D. Hill, Lloyd C. L. Hollenberg
The emergence of Quantum Machine Learning (QML) to enhance traditional classical learning methods has seen various limitations to its realisation. There is therefore an imperative…
Automated Quantum Circuit Design with Nested Monte Carlo Tree Search
Pei-Yong Wang, Muhammad Usman, Udaya Parampalli +2
Quantum algorithms based on variational approaches are one of the most promising methods to construct quantum solutions and have found a myriad of applications in the last few year…
Process Tomography on a 7-Qubit Quantum Processor via Tensor Network Contraction Path Finding
Aidan Dang, Gregory A. L. White, Lloyd C. L. Hollenberg +1
Quantum process tomography (QPT), where a quantum channel is reconstructed through the analysis of repeated quantum measurements, is an important tool for validating the operation…
A silicon qubit platform for in situ single molecule structure determination
Viktor S. Perunicic, Muhammad Usman, Charles D. Hill +1
Imaging individual conformational instances of generic, inhomogeneous, transient or intrinsically disordered protein systems at the single molecule level in situ is one of the nota…
A scalable and fast artificial neural network syndrome decoder for surface codes
Spiro Gicev, Lloyd C. L. Hollenberg, Muhammad Usman
Surface code error correction offers a highly promising pathway to achieve scalable fault-tolerant quantum computing. When operated as stabilizer codes, surface code computations c…
Truncated phase-based quantum arithmetic: error propagation and resource reduction
G. A. L. White, C. D. Hill, L. C. L. Hollenberg
There are two important, and potentially interconnecting, avenues to the realisation of large-scale quantum algorithms: improvement of the hardware, and reduction of resource requi…
Clustering and enhanced classification using a hybrid quantum autoencoder
Maiyuren Srikumar, Charles D. Hill, Lloyd C. L. Hollenberg
Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable inte…
An exchange-based surface-code quantum computer architecture in silicon
Charles D. Hill, Muhammad Usman, Lloyd C. L. Hollenberg
Phosphorus donor spins in silicon offer a number of promising characteristics for the implementation of robust qubits. Amongst various concepts for scale-up, the shared-control con…
What can unitary sequences tell us about multi-time physics?
Gregory A. L. White, Felix A. Pollock, Lloyd C. L. Hollenberg +2
Multi-time quantum processes are endowed with the same richness as multipartite states, including temporal entanglement and exotic causal structures. However, experimentally probin…
Non-Markovian Quantum Process Tomography
Gregory A. L. White, Felix A. Pollock, Lloyd C. L. Hollenberg +2
Characterisation protocols have so far played a central role in the development of noisy intermediate-scale quantum (NISQ) computers capable of impressive quantum feats. This traje…
Anisotropic electron-nuclear interactions in a rotating quantum spin bath
Alexander A. Wood, Russell M. Goldblatt, Russell P. Anderson +3
The interaction between a central qubit spin and a surrounding bath of spins is critical to spin-based solid state quantum sensing and quantum information processing. Spin-bath int…