Control the dynamics
Select a system, adjust its available Hamiltonian parameters and choose the time interval.
Follow the evolution of one or two qubits, change Hamiltonian parameters and compare neural predictions with exact solutions.
Physics informed neural networks (PINNs) commonly use multilayer perceptrons with fixed activation functions. MixFunn learns mixtures of elementary functions; its second order formulation also includes interactions between inputs. These components expand the flexibility of the neural representation [1]. The Schrödinger equation guides training, while a temporal FNO extends selected trajectories [2].
Move the time slider or choose a field. The sphere represents the qubit state; the curves compare its predicted and exact evolution.
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Open the full one qubit experimentSelect a system, adjust its available Hamiltonian parameters and choose the time interval.
Inspect probabilities, spin observables and fidelity against the exact quantum evolution.
Customize the curves and download figures and numerical data for your own analysis.
Explore three complementary tests of neural quantum dynamics.
Vary the field components and compare MixFunn with exact dynamics, including training with and without supervised data.
Follow interacting qubits under a fixed Hamiltonian and assess how a temporal FNO extends MixFunn trajectories.
Adjust the three diagonal interactions and examine the accuracy of MixFunn and FNO across Hamiltonians.
Qubit states, Bloch geometry, interacting spin Hamiltonians and the neural methods used to approximate their evolution. The scientific background brings together the equations, assumptions and references.