A quantics tensor train solver resolves the Gross-Pitaevskii equation across seven orders of magnitude in length scale in one dimension and on grids larger than a trillion points in two dimensions.
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Variational quantum circuit MPS ansatz with stochastic corrections simulates the DQPT of the TFIM on Quantinuum H1-1 hardware, demonstrating feasibility and revealing hidden simplicity in the dynamics.
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
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Solving the Gross-Pitaevskii equation on multiple different scales using the quantics tensor train representation
A quantics tensor train solver resolves the Gross-Pitaevskii equation across seven orders of magnitude in length scale in one dimension and on grids larger than a trillion points in two dimensions.
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Fully optimised variational simulation of a dynamical quantum phase transition on a trapped-ion quantum computer
Variational quantum circuit MPS ansatz with stochastic corrections simulates the DQPT of the TFIM on Quantinuum H1-1 hardware, demonstrating feasibility and revealing hidden simplicity in the dynamics.
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Quantum-inspired tensor networks in machine learning models
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.