代码封装

README.md

@@ -28,15 +28,24 @@ Currently, the supported tensor network libraries are:
 
 ## CPU expectation benchmarks
 
-The current CPU expectation entrypoint is:
+Use the library APIs directly:
 
-```sh
-python -u benchmark_cpu_expectation.py --ansatz mps --nqubits 40 --nlayers 10 --bond 2048 --circuits brickwall_cnot --observables ring_xz
+```py
+import qibotn
+
+records = qibotn.run_cpu_benchmark_cases(
+    ansatz="mps",
+    nqubits=40,
+    nlayers=10,
+    bond=2048,
+    circuits=("brickwall_cnot",),
+    observables=("ring_xz",),
+)
 ```
 
-Use `--ansatz tn` for the generic TN path and `--mpi` under `mpiexec` for MPI runs.
-Reusable circuit and observable builders live in `src/qibotn/benchmark_cases.py`; execution logic lives in `src/qibotn/expectation_runner.py`.
-For Vidal/MPS 1D-chain scale tests, use `run_vidal_mps_cases.sh`.
+For generic TN use `ansatz="tn"`.  Contest/custom runners are available as
+`qibotn.run_contest_tn_case`, `qibotn.run_custom_tn_expectation`,
+`qibotn.run_contest_mps_case`, and `qibotn.run_vidal_validation_cases`.
 
 ## Installation