batch the calls in grad

src/tf-qas.py

@@ -400,14 +400,35 @@ def adam_optimize_tfim_energy(
         return energy_expectation_from_sv(H, sv)
 
     def grad(th: np.ndarray) -> np.ndarray:
-        # parameter-shift: d/dθ f(θ) = 0.5*(f(θ+π/2)-f(θ-π/2))
-        g = np.zeros_like(th)
+        theta_list = []
         for i in range(p):
             plus = th.copy()
-            minus = th.copy()
             plus[i] += param_shift
+            theta_list.append(plus)
+            minus = th.copy()
             minus[i] -= param_shift
-            g[i] = 0.5 * (E(plus) - E(minus))
+            theta_list.append(minus)
+        tla = np.array(theta_list).T
+
+        binds = [{param: list(row) for param, row in zip(thetas.params, tla)}]
+
+        job = backend.run([tqc], parameter_binds=binds)
+        res = job.result()
+
+        energies = np.array(
+            [
+                energy_expectation_from_sv(H, sv)
+                for sv in [
+                    np.asarray(res.get_statevector(k), dtype=np.complex128)
+                    for k in range(2 * p)
+                ]
+            ]
+        )
+
+        g = np.empty(p, dtype=np.float64)
+        for i in range(p):
+            g[i] = 0.5 * (energies[2 * i] - energies[2 * i + 1])
+
         return g
 
     m = np.zeros_like(theta)
@@ -614,6 +635,7 @@ def main():
             )
             if circ.gt_error is not None and circ.gt_error < min_error:
                 print(f"new best error for {queried}: {circ.gt_error}")
+                min_error = circ.gt_error
             queried += 1
             if circ.gt_success:
                 success_idx = i