Studying QCD and other gauge theories on quantum hardware requires the preparation of physically interesting states. The Variational Quantum Eigensolver (VQE) provides a way of performing vacuum state preparation on quantum hardware. In this work, VQE is applied to pure SU(3) lattice Yang-Mills on a single plaquette and one dimensional plaquette chains. Bayesian optimization and gradient descent were investigated for performing the classical optimization. Ansatz states for plaquette chains are constructed in a scalable manner from smaller systems using domain decomposition and a stitching procedure analogous to the Density Matrix Renormalization Group (DMRG). Small examples are performed on IBM's superconducting Manila processor.
CONTENTSA. Hardware Calculations 14 B. Bayesian Optimization 15 C. Plaquette Chain Tensor Network 16 D. Data from IBM's Manila Processor 17 References 18
Studying QCD and other gauge theories on quantum hardware requires the preparation of physical states. In this work, VQE is applied to pure SU(3) lattice Yang-Mills on a single plaquette and one dimensional plaquette chains. Ansatz states for plaquette chains are constructed in a scalable manner from smaller systems using a stitching procedure.
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