Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
Mezzadri, How to generate random matrices from the classical compact groups, arXiv preprint math- ph/0609050 (2006)
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A framework is presented for designing robust and precise effective Hamiltonians by identifying the minimal toggling-frame subspace and the complete set of achievable zeroth-order terms.
Sparse phase ansatzes for the SNAP-displacement protocol achieve favorable fidelity versus resource trade-offs for qudit state preparation up to dimension 64 in both ideal and noisy regimes.
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Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
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Engineering Precise and Robust Effective Hamiltonians
A framework is presented for designing robust and precise effective Hamiltonians by identifying the minimal toggling-frame subspace and the complete set of achievable zeroth-order terms.
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Sparse Phase Ansatzes for Resource-Efficient Qudit State Preparation via the SNAP-Displacement Protocol
Sparse phase ansatzes for the SNAP-displacement protocol achieve favorable fidelity versus resource trade-offs for qudit state preparation up to dimension 64 in both ideal and noisy regimes.