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 …

Abstract Machine learning is among the most widely anticipated use cases for near-term
quantum computers, however there remain significant theoretical and implementation …

Exploiting the power of quantum computation to realise superior machine learning
algorithmshas been a major research focus of recent years, but the prospects of quantum …

Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which
can be efficiently simulated with a low depth on near-term quantum hardware in the …

Variational algorithms require architectures that naturally constrain the optimisation space to
run efficiently. In geometric quantum machine learning, one achieves this by encoding group …

Security has always been a critical issue in machine learning (ML) applications. Due to the
high cost of model training–such as collecting relevant samples, labeling data, and …

Quantum circuits play an essential role in many disciplines of quantum information science.
They can not only be represented in the traditional gate-based paradigm, but also an …

Abstract Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms
which can be efficiently simulated with a low depth on near-term quantum hardware in the …