Task 06: use TensorCircuit native jaxode (1.50x)

[QingyunQian]

Take-home insight

Replacing the Python-side ODE path with TensorCircuit's native jaxode integration is the only material factor; sparse Hamiltonian conversion and tiny setup tweaks do not help.

Measured result

Comparison	Runtime	Speedup
Public expert	41.4259 s	1.000x
Optimized	27.5366 s	1.50446x

The README keeps one compact ablation table and one figure so the dominant contribution is immediately visible.

Reviewer-facing files

• optimized_solutions/challenge-06/README.md
• optimized_solutions/challenge-06/solution_6_native_jaxode.py
• optimized_solutions/challenge-06/factor-ablation.svg

Full profiles and research chronology remain in hmyuuu/OrbitBreakersExpertBenchmarks.

[refraction-ray]

the insight is vague
replace what by jaxode？to me jaxode is much worse than diffrax ode in my experience

[QingyunQian]

the insight is vague replace what by jaxode？to me jaxode is much worse than diffrax ode in my experience

maybe we need to compare the performance of jaxode with diffrax ode on more different situation