From 1f4e998126542a97cb4d47ed3ad8fdfd4d20bcf0 Mon Sep 17 00:00:00 2001
From: alleSini99 <alessandro.sinibaldi@epfl.ch>
Date: Thu, 2 Nov 2023 14:44:48 +0100
Subject: [PATCH] jitting fullsumstate with U

---
 netket_fidelity/infidelity/overlap_U/exact.py | 21 ++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/netket_fidelity/infidelity/overlap_U/exact.py b/netket_fidelity/infidelity/overlap_U/exact.py
index 4a20216..8ddd23b 100644
--- a/netket_fidelity/infidelity/overlap_U/exact.py
+++ b/netket_fidelity/infidelity/overlap_U/exact.py
@@ -1,5 +1,6 @@
 import jax.numpy as jnp
 import jax
+from functools import partial
 
 from netket import jax as nkjax
 from netket.utils.dispatch import TrueT
@@ -21,13 +22,15 @@ def infidelity(vstate: FullSumState, op: InfidelityOperatorUPsi):
     if not isinstance(op.target, FullSumState):
         raise TypeError("Can only compute infidelity of exact states.")
 
+    U_sp = sparsify(op._U)
+    Ustate_t = U_sp @ op.target.to_array(normalize=False)
+
     return infidelity_sampling_FullSumState(
         vstate._apply_fun,
         vstate.parameters,
         vstate.model_state,
         vstate._all_states,
-        op.target.to_array(),
-        op._U.to_sparse(),
+        Ustate_t,
         return_grad=False,
     )
 
@@ -45,30 +48,34 @@ def infidelity(  # noqa: F811
     if not isinstance(op.target, FullSumState):
         raise TypeError("Can only compute infidelity of exact states.")
 
+    U_sp = sparsify(op._U)
+    Ustate_t = U_sp @ op.target.to_array(normalize=False)
+
     return infidelity_sampling_FullSumState(
         vstate._apply_fun,
         vstate.parameters,
         vstate.model_state,
         vstate._all_states,
-        op.target.to_array(),
-        op._U.to_sparse(),
+        Ustate_t,
         return_grad=True,
     )
 
 
+@partial(jax.jit, static_argnames=("afun", "return_grad"))
 def infidelity_sampling_FullSumState(
     afun,
     params,
     model_state,
     sigma,
-    state_t,
-    U_sp,
+    Ustate_t,
     return_grad,
 ):
     def expect_fun(params):
         state = jnp.exp(afun({"params": params, **model_state}, sigma))
         state = state / jnp.sqrt(jnp.sum(jnp.abs(state) ** 2))
-        return jnp.abs(state.T.conj() @ (U_sp @ state_t)) ** 2
+        return jnp.abs(state.T.conj().T @ Ustate_t) ** 2 / (
+            Ustate_t.conj().T @ Ustate_t
+        )
 
     if not return_grad:
         F = expect_fun(params)