Effect of data encoding on the expressive power of variational quantum-machine-learning models
Quantum computers can be used for supervised learning by treating parametrized quantum
circuits as models that map data inputs to predictions. While a lot of work has been done to …
circuits as models that map data inputs to predictions. While a lot of work has been done to …
Parameterized quantum circuits as machine learning models
Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …
Robust data encodings for quantum classifiers
Data representation is crucial for the success of machine-learning models. In the context of
quantum machine learning with near-term quantum computers, equally important …
quantum machine learning with near-term quantum computers, equally important …
Real-and imaginary-time evolution with compressed quantum circuits
The current generation of noisy intermediate-scale quantum computers introduces new
opportunities to study quantum many-body systems. In this paper, we show that quantum …
opportunities to study quantum many-body systems. In this paper, we show that quantum …
Expressive power of parametrized quantum circuits
Parametrized quantum circuits (PQCs) have been broadly used as a hybrid quantum-
classical machine learning scheme to accomplish generative tasks. However, whether …
classical machine learning scheme to accomplish generative tasks. However, whether …
Estimating the gradient and higher-order derivatives on quantum hardware
For a large class of variational quantum circuits, we show how arbitrary-order derivatives
can be analytically evaluated in terms of simple parameter-shift rules, ie, by running the …
can be analytically evaluated in terms of simple parameter-shift rules, ie, by running the …
Strategies for solving the Fermi-Hubbard model on near-term quantum computers
The Fermi-Hubbard model is of fundamental importance in condensed-matter physics, yet is
extremely challenging to solve numerically. Finding the ground state of the Hubbard model …
extremely challenging to solve numerically. Finding the ground state of the Hubbard model …
Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer
We compare the bfgs optimizer, adam and NatGrad in the context of vqes. We systematically
analyze their performance on the qaoa ansatz for the transverse field Ising and the XXZ …
analyze their performance on the qaoa ansatz for the transverse field Ising and the XXZ …
Exponentially many local minima in quantum neural networks
Abstract Quantum Neural Networks (QNNs), or the so-called variational quantum circuits,
are important quantum applications both because of their similar promises as classical …
are important quantum applications both because of their similar promises as classical …
Matrix product state pre-training for quantum machine learning
Hybrid quantum–classical algorithms are a promising candidate for develo** uses for
NISQ devices. In particular, parametrised quantum circuits (PQCs) paired with classical …
NISQ devices. In particular, parametrised quantum circuits (PQCs) paired with classical …