Structured state preparation in QCQMC improves energy accuracy over pure variational methods across molecular, condensed-matter, nuclear, and graph 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.
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.
QGANs with quantum generators and classical discriminators generate financial time series matching target distributions and desired temporal correlations, with quality varying by circuit depth, bond dimension, and simulation method.
A gate freezing method improves convergence of gradient-free optimizers Rotosolve, Fraxis, and FQS for parameterized quantum circuits by reallocating resources to poorly optimized gates using previous iteration information.
citing papers explorer
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A unified quantum computing quantum Monte Carlo framework through structured state preparation
Structured state preparation in QCQMC improves energy accuracy over pure variational methods across molecular, condensed-matter, nuclear, and graph 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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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.
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Quantum generative modeling for financial time series with temporal correlations
QGANs with quantum generators and classical discriminators generate financial time series matching target distributions and desired temporal correlations, with quality varying by circuit depth, bond dimension, and simulation method.
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Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization
A gate freezing method improves convergence of gradient-free optimizers Rotosolve, Fraxis, and FQS for parameterized quantum circuits by reallocating resources to poorly optimized gates using previous iteration information.