A new adaptive MPS/TT sampling method iteratively refines distributions over discrete quantum control sequences and shows competitive performance on benchmarks including qubit state transfer and gate synthesis.
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Tailoring tensor network algorithms to the scale hierarchy in quantics representation produces faster, more robust solvers for high-dimensional linear and eigenvalue PDE problems.
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Adaptive Tensor Network Sampling for Quantum Optimal Control
A new adaptive MPS/TT sampling method iteratively refines distributions over discrete quantum control sequences and shows competitive performance on benchmarks including qubit state transfer and gate synthesis.
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Tailoring tensor network techniques to the quantics representation for highly inhomogeneous problems and few body problems
Tailoring tensor network algorithms to the scale hierarchy in quantics representation produces faster, more robust solvers for high-dimensional linear and eigenvalue PDE problems.