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MIXFUNN · QUANTUM DYNAMICS

Explore quantum dynamics
with MixFunn.

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].

Schematic of sine, cosine and identity functions combined with learned weights to form a mixed function.
Elementary functions · Learned mixtures
Three experiments. Adjustable parameters. Downloadable results.
A FIRST LOOK

One qubit in motion

Interactive
h = (0.10, 0.08, 0.20)
Quantum state on the Bloch sphereExact and MixFunn state vectors change with the selected time and field.
Exact evolutionMixFunn
02
Probability of |0⟩
State fidelity

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 experiment
01

Control the dynamics

Select a system, adjust its available Hamiltonian parameters and choose the time interval.

02

Compare the predictions

Inspect probabilities, spin observables and fidelity against the exact quantum evolution.

03

Take the results further

Customize the curves and download figures and numerical data for your own analysis.

THE EXPERIMENTS

From a single qubit to interacting qubits

Explore three complementary tests of neural quantum dynamics.

THE SCIENTIFIC FRAMEWORK

The physics behind the curves.

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.

Read the scientific background