The authors show that a Walsh-Hadamard transform cuts encoding memory by up to 1298x and an SVD-based two-term encoding gives a >10,000x per-iteration speedup for VQLS on CFD problems, while expressibility metrics fail to predict ansatz convergence.
Uncertainty quantification and experimental design for large-scale linear inverse problems under gaussian process priors,
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Memory-, Circuit-, and Ansatz-Efficient VQLS for CFD on Hybrid Quantum-HPC Systems
The authors show that a Walsh-Hadamard transform cuts encoding memory by up to 1298x and an SVD-based two-term encoding gives a >10,000x per-iteration speedup for VQLS on CFD problems, while expressibility metrics fail to predict ansatz convergence.