Sparse power-of-two circuit connectivity provides task-dependent advantages for variational quantum learning, and a Monna-map qubit reordering enables short-range circuits to solve long-range problems.
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Quantum state evolution in variational algorithms is governed by geometric phase rather than dynamical phase, with entanglement decoupled from evolution in hardware-efficient ansatzes but acting as a dynamical resource in Hamiltonian variational ansatzes.
A comprehensive review organizing progress at the AI-quantum information intersection from both directions.
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Variational Learning with Sparse Long-range Entangling Gates
Sparse power-of-two circuit connectivity provides task-dependent advantages for variational quantum learning, and a Monna-map qubit reordering enables short-range circuits to solve long-range problems.
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Calibrating the Role of Entanglement in Variational Quantum Algorithms from a Geometric Perspective
Quantum state evolution in variational algorithms is governed by geometric phase rather than dynamical phase, with entanglement decoupled from evolution in hardware-efficient ansatzes but acting as a dynamical resource in Hamiltonian variational ansatzes.
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When AI meets quantum information: A comprehensive review
A comprehensive review organizing progress at the AI-quantum information intersection from both directions.