Variational quantum circuits are used in quantum machine learning and variational quantum
simulation tasks. Designing good variational circuits or predicting how well they perform for …

We propose SnCQA, a set of hardware-efficient variational circuits of equivariant quantum
convolutional circuits respective to permutation symmetries and spatial lattice symmetries …

The popular qubit framework has dominated recent work on quantum kernel machine
learning, with results characterising expressivity, learnability and generalisation. As yet …

Quantum kernel methods leverage a kernel function computed by embedding input
information into the Hilbert space of a quantum system. However, large Hilbert spaces can …

Quantum machine learning with variational quantum algorithms (VQA) has been actively
investigated as a practical algorithm in the noisy intermediate-scale quantum (NISQ) era …

In this work, we use a deformed version of the Heisenberg–Weyl algebra in the Dicke Model
to incorporate a Kerr medium and nonlinear interaction between matter and radiation fields …

Adversarial transfer learning is a machine learning method that employs an adversarial
training process to learn the datasets of different domains. Recently, this method has …

The popular qubit framework has dominated recent work on quantum kernels, with results
characterising expressability, learnability and generalisation. As yet, there is no comparative …

The lifespan predictions oncologists make to schedule treatments for oncologists are
extremely error-prone; about 74% of patients get a prediction with an error margin of a year …

The unprecedented growth of data in the digital age has necessitated the development of
efficient and scalable resource allocation strategies for cloud-based big data environments …