Joseph E. Bowles
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View article: A new true triaxial apparatus with pore fluid system for rock deformation under representative crustal stress conditions
A new true triaxial apparatus with pore fluid system for rock deformation under representative crustal stress conditions Open
We have developed a new true triaxial apparatus for rock deformation, featuring six servo-controlled loading rams capable of applying maximum stresses of 220 MPa along the two horizontal axes and 400 MPa along the vertical axis to cubic ro…
View article: A new True Triaxial Apparatus with pore fluid system for rock deformation under representative crustal stress conditions
A new True Triaxial Apparatus with pore fluid system for rock deformation under representative crustal stress conditions Open
We have developed a new True Triaxial Apparatus (TTA) for rock deformation consisting of six servo-controlled loading rams that transmit maximum stresses of 220 MPa in the two horizontal axes and 400 MPa in the vertical axis to 50 mm side …
View article: Train on classical, deploy on quantum: scaling generative quantum machine learning to a thousand qubits
Train on classical, deploy on quantum: scaling generative quantum machine learning to a thousand qubits Open
We propose an approach to generative quantum machine learning that overcomes the fundamental scaling issues of variational quantum circuits. The core idea is to use a class of generative models based on instantaneous quantum polynomial cir…
View article: Better than classical? The subtle art of benchmarking quantum machine\n learning models
Better than classical? The subtle art of benchmarking quantum machine\n learning models Open
Benchmarking models via classical simulations is one of the main ways to\njudge ideas in quantum machine learning before noise-free hardware is\navailable. However, the huge impact of the experimental design on the results,\nthe small scal…
View article: Contextuality and inductive bias in quantum machine learning
Contextuality and inductive bias in quantum machine learning Open
Generalisation in machine learning often relies on the ability to encode structures present in data into an inductive bias of the model class. To understand the power of quantum machine learning, it is therefore crucial to identify the typ…
View article: Quantum networks self-test all entangled states
Quantum networks self-test all entangled states Open
Certifying quantum properties with minimal assumptions is a fundamental problem in quantum information science. Self-testing is a method to infer the underlying physics of a quantum experiment only from the measured statistics. While all b…
View article: Device-independent and semi-device-independent entanglement certification in broadcast Bell scenarios
Device-independent and semi-device-independent entanglement certification in broadcast Bell scenarios Open
It has recently been shown that by broadcasting the subsystems of a bipartite quantum state, one can activate Bell nonlocality and significantly improve noise tolerance bounds for device-independent entanglement certification. In this work…
View article: Quadratic Unconstrained Binary Optimisation via Quantum-Inspired\n Annealing
Quadratic Unconstrained Binary Optimisation via Quantum-Inspired\n Annealing Open
We present a classical algorithm to find approximate solutions to instances\nof quadratic unconstrained binary optimisation. The algorithm can be seen as an\nanalogue of quantum annealing under the restriction of a product state space,\nwh…