Exploiting symmetry in variational quantum machine learning
Variational quantum machine learning is an extensively studied application of near-term
quantum computers. The success of variational quantum learning models crucially depends …
quantum computers. The success of variational quantum learning models crucially depends …
Avoiding barren plateaus via transferability of smooth solutions in a Hamiltonian variational ansatz
A large ongoing research effort focuses on variational quantum algorithms (VQAs),
representing leading candidates to achieve computational speed-ups on current quantum …
representing leading candidates to achieve computational speed-ups on current quantum …
Building spatial symmetries into parameterized quantum circuits for faster training
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 …
parameterized quantum circuit. Such structure is defined both by the types of gates …
Symmetry-invariant quantum machine learning force fields
Machine learning techniques are essential tools to compute efficient, yet accurate, force
fields for atomistic simulations. This approach has recently been extended to incorporate …
fields for atomistic simulations. This approach has recently been extended to incorporate …
Hardware-efficient ansatz without barren plateaus in any depth
Variational quantum circuits have recently gained much interest due to their relevance in
real-world applications, such as combinatorial optimizations, quantum simulations, and …
real-world applications, such as combinatorial optimizations, quantum simulations, and …
Hamiltonian variational ansatz without barren plateaus
CY Park, N Killoran - Quantum, 2024 - quantum-journal.org
Variational quantum algorithms, which combine highly expressive parameterized quantum
circuits (PQCs) and optimization techniques in machine learning, are one of the most …
circuits (PQCs) and optimization techniques in machine learning, are one of the most …
Shallow quantum circuits are robust hunters for quantum many-body scars
G Cenedese, M Bondani, A Andreanov… - arXiv preprint arXiv …, 2024 - arxiv.org
Presently, noisy intermediate-scale quantum computers encounter significant technological
challenges that make it impossible to generate large amounts of entanglement. We leverage …
challenges that make it impossible to generate large amounts of entanglement. We leverage …
arXiv: Symmetry-invariant quantum machine learning force fields
Abstract Machine learning techniques are essential tools to compute efficient, yet accurate,
force fields for atomistic simulations. This approach has recently been extended to …
force fields for atomistic simulations. This approach has recently been extended to …
Quantum Approximate Optimization Algorithm and Variational Quantum Computing: from binary neural networks to ground state preparation
P Torta - 2024 - iris.sissa.it
In this thesis, I explore the domain of hybrid quantum-classical computation, the foremost
approach for utilizing Noisy Intermediate-Scale Quantum (NISQ) devices. The opening …
approach for utilizing Noisy Intermediate-Scale Quantum (NISQ) devices. The opening …
Optimizing the Quantum Stack: A Machine Learning Approach
D Fitzek - 2024 - search.proquest.com
This compilation thesis explores the intersection of machine learning and quantum
computing, focusing on optimizing quantum systems and exploring use-cases for quantum …
computing, focusing on optimizing quantum systems and exploring use-cases for quantum …