HAVQDS achieves higher approximation ratios on 6-14 qubit SK instances than adiabatic or CD methods while cutting CNOT counts by 1-2 orders of magnitude.
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DDQN reinforcement learning automates VITE circuit design, producing circuits with ~37% fewer gates and ~43% less depth than hardware-efficient ansatze for Max-Cut while reaching Full-CI for H2 with shallower depth.
A penalty-free, fully quantum algorithm is proposed for finding ground and excited states of many-body Hamiltonians.
citing papers explorer
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Hybrid Real-Imaginary Time Evolution for Low-Depth Hamiltonian Simulation in Quantum Optimization
HAVQDS achieves higher approximation ratios on 6-14 qubit SK instances than adiabatic or CD methods while cutting CNOT counts by 1-2 orders of magnitude.
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Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
DDQN reinforcement learning automates VITE circuit design, producing circuits with ~37% fewer gates and ~43% less depth than hardware-efficient ansatze for Max-Cut while reaching Full-CI for H2 with shallower depth.
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A penalty-free quantum algorithm to find energy eigenstates
A penalty-free, fully quantum algorithm is proposed for finding ground and excited states of many-body Hamiltonians.