A variational neural network ansatz approximates the ground-state wavefunctional of the free Klein-Gordon theory in momentum-space field basis and is validated against exact analytic observables.
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A perceptrain-based variational ansatz achieves relative ground-state energy accuracy of 10^{-5} (VMC) to 10^{-6} (GFMC) on a 10x10 transverse-field Ising model with 1/r^6 interactions using ranks of only 2-5.
SG-QST achieves comparable fidelity to full tomography on 3-5 qubit GHZ states using significantly fewer parameters by restricting operators to physically relevant correlations.
citing papers explorer
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Variational Neural Network Approach to QFT in the Field Basis
A variational neural network ansatz approximates the ground-state wavefunctional of the free Klein-Gordon theory in momentum-space field basis and is validated against exact analytic observables.
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Hybrid between biologically and quantum-inspired many-body states
A perceptrain-based variational ansatz achieves relative ground-state energy accuracy of 10^{-5} (VMC) to 10^{-6} (GFMC) on a 10x10 transverse-field Ising model with 1/r^6 interactions using ranks of only 2-5.
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Structured Quantum State Reconstruction via Physically Motivated Operator Selection
SG-QST achieves comparable fidelity to full tomography on 3-5 qubit GHZ states using significantly fewer parameters by restricting operators to physically relevant correlations.