Stage-dependent encoding in FMQA black-box optimization, using one-hot for learning and domain-wall for search, improves residual error on discretized Rastrigin functions under finer discretization and higher dimensions compared to uniform encodings.
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Ensuring complete marginal bit coverage in initial data for one-hot encoded FMQA improves mean optimization performance on wing-shape benchmarks with 17 and 32 variables.
FMQA uses factorization machines as surrogates for black-box optimization, converting them directly into QUBO problems solvable by Ising machines for faster acquisition function optimization.
citing papers explorer
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Stage-dependent integer-binary encoding in factorization-machine black-box optimization
Stage-dependent encoding in FMQA black-box optimization, using one-hot for learning and domain-wall for search, improves residual error on discretized Rastrigin functions under finer discretization and higher dimensions compared to uniform encodings.
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Improving FMQA via Initial Training Data Design Considering Marginal Bit Coverage in One-Hot Encoding
Ensuring complete marginal bit coverage in initial data for one-hot encoded FMQA improves mean optimization performance on wing-shape benchmarks with 17 and 32 variables.
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Black-box optimization using factorization and Ising machines
FMQA uses factorization machines as surrogates for black-box optimization, converting them directly into QUBO problems solvable by Ising machines for faster acquisition function optimization.