The MIMO MAC admits canonical convex formulations solved via L-BFGS on per-tone Cholesky factors, yielding four solvers that match commercial performance while running up to 100x faster.
On the limited memory BFGS method for large scale optimization
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A machine-learning approach adaptively chooses quantum code sequences for concatenation to achieve target logical error rates with far fewer qubits than standard methods for structured noise.
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Canonical Optimization for MIMO MAC Design
The MIMO MAC admits canonical convex formulations solved via L-BFGS on per-tone Cholesky factors, yielding four solvers that match commercial performance while running up to 100x faster.
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Learning to Concatenate Quantum Codes
A machine-learning approach adaptively chooses quantum code sequences for concatenation to achieve target logical error rates with far fewer qubits than standard methods for structured noise.