3.3k citations · 14.8k across the 91 of their papers we have counts for
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Resource frugal optimizer for quantum machine learning
Charles Moussa, Max Hunter Gordon, Michal Baczyk +3
Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms hav…
Theory for Equivariant Quantum Neural Networks
Quynh T. Nguyen, Louis Schatzki, Paolo Braccia +5
Quantum neural network architectures that have little-to-no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakth…
Representation Theory for Geometric Quantum Machine Learning
Michael Ragone, Paolo Braccia, Quynh T. Nguyen +5
Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance. Importatio…
Quantum Mixed State Compiling
Nic Ezzell, Elliott M. Ball, Aliza U. Siddiqui +4
The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states wh…
Building spatial symmetries into parameterized quantum circuits for faster training
Frederic Sauvage, Martin Larocca, Patrick J. Coles +1
Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates emplo…
Practical Black Box Hamiltonian Learning
Andi Gu, Lukasz Cincio, Patrick J. Coles
We study the problem of learning the parameters for the Hamiltonian of a quantum many-body system, given limited access to the system. In this work, we build upon recent approaches…