REVIEW 3 cited by
Noisy Bayesian optimization for variational quantum eigensolvers
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm used to find the ground state of a Hamiltonian using variational methods. In the context of this Lattice symposium, the procedure can be used to study lattice gauge theories (LGTs) in the Hamiltonian formulation. Bayesian optimization (BO) based on Gaussian process regression (GPR) is a powerful algorithm for finding the global minimum of a cost function, e.g. the energy, with a very low number of iterations using data affected by statistical noise. This work proposes an implementation of GPR and BO specifically tailored to perform VQE on quantum computers already available today.
Forward citations
Cited by 3 Pith papers
-
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers
Bayesian PSR with Gaussian processes and GradCoRe accelerates VQE SGD by reusing observations and minimizing per-step costs while reducing to standard PSR in special cases.
-
Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization
Proposes projected quantum kernels with misspecified GP bandit algorithms and regret bounds to trade off expressivity against learnability in quantum kernel optimization.
-
A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
Discussion (0). Sign in to comment.