13 citations · 15 across the 5 of their papers we have counts for
5 papers
Exploiting the equivalence between quantum neural networks and perceptrons
Chris Mingard, Jessica Pointing, Charles London +2
Quantum machine learning models based on parametrized quantum circuits, also called quantum neural networks (QNNs), are considered to be among the most promising candidates for app…
Do Quantum Neural Networks have Simplicity Bias?
Jessica Pointing
One hypothesis for the success of deep neural networks (DNNs) is that they are highly expressive, which enables them to be applied to many problems, and they have a strong inductiv…
Quartz: Superoptimization of Quantum Circuits (Extended Version)
Mingkuan Xu, Zikun Li, Oded Padon +8
Existing quantum compilers optimize quantum circuits by applying circuit transformations designed by experts. This approach requires significant manual effort to design and impleme…
Quanto: Optimizing Quantum Circuits with Automatic Generation of Circuit Identities
Jessica Pointing, Oded Padon, Zhihao Jia +4
Existing quantum compilers focus on mapping a logical quantum circuit to a quantum device and its native quantum gates. Only simple circuit identities are used to optimize the quan…
A quantum computational approach to the open-pit mining problem
Yousef Hindy, Jessica Pointing, Meltem Tolunay +3
The determination of optimal open-pit profiles is a well-studied combinatorial optimization problem, with profound technical and conceptual relevance in computational mining. The o…