I get 4/sec for expressibility on my laptop

src/tf-qas.py

@@ -1,11 +1,10 @@
 #!/usr/bin/env python
 from dataclasses import dataclass
 from enum import IntEnum
-import math
 from multiprocessing import Pool
 import random
 import numpy as np
-from typing import assert_type, override
+from typing import override
 from qiskit.circuit import ParameterVector
 from tqdm import tqdm
 from qiskit import QuantumCircuit as QiskitCircuit, transpile
@@ -99,30 +98,28 @@ class QuantumCircuit:
 
         backend = AerSimulator(method="statevector", seed_simulator=seed)
 
-        tqc = transpile(qc, backend)
+        tqc = transpile(qc, backend, optimization_level=0)
 
-        # 1) build 2*samples parameterized circuits (left+right)
-        binds = []
-        for _ in range(2 * samples):
-            binds.append(
-                {thetas: [rng.random() for _ in range(self.params)]}
-            )  # SAFE binding
+        nexp = 2 * samples
 
-        # 2) compute statevectors (no backend/job overhead)
-        job = backend.run(
-            [tqc.assign_parameters(bind, inplace=False) for bind in binds]
-        )
+        binds = [
+            {param: [rng.random() for _ in range(nexp)] for param in thetas.params}
+        ]
+
+        job = backend.run([tqc], parameter_binds=binds)
         result = job.result()
-        sv = [result.get_statevector(i) for i in range(len(binds))]
+
+        sv = [
+            np.asarray(result.get_statevector(i), dtype=np.complex128)
+            for i in range(nexp)
+        ]
         left = sv[:samples]
         right = sv[samples:]
 
-        # 3) fidelities F = |<ψ|φ>|^2
         inners = np.array(
-            [l.inner(r) for l, r in zip(left, right)], dtype=np.complex128
+            [np.vdot(l, r) for l, r in zip(left, right)], dtype=np.complex128
         )
-        F = (inners * inners.conjugate()).real
-        F = np.clip(F, 0.0, 1.0)  # numerical safety
+        F = (inners.conjugate() * inners).real
 
         # 4) empirical histogram (as a probability mass over bins)
         hist, edges = np.histogram(F, bins=bins, range=(0.0, 1.0), density=False)
@@ -140,7 +137,6 @@ class QuantumCircuit:
 
         kl = kl_divergence(p, q)
         self.expressibility = kl
-        print(f"completed with seed {seed}")
         return self
 
     @override
@@ -232,8 +228,8 @@ def more_single_than_double(qc: QuantumCircuit) -> bool:
 if __name__ == "__main__":
     rng = random.Random()
     qubits = 6
-    depth = 10
-    sample_amount = 50000
+    depth = 15
+    sample_amount = 5000
     expressibility_samples = 2000
     proxy_pass_amount = 5000
     circuits = (
@@ -247,14 +243,21 @@ if __name__ == "__main__":
     circuits.sort(key=lambda qc: qc.paths, reverse=True)
     seeds = [rng.randint(0, 1000000000) for _ in range(proxy_pass_amount)]
 
-    def starmap_func(circ, seed):
-        return circ.expressibility_estimate(expressibility_samples, seed)
+    def starmap_func(args):
+        (circ, seed, idx) = args
+        res = circ.expressibility_estimate(expressibility_samples, seed)
+        return res
 
+    final_circuits: list[QuantumCircuit] = []
     with Pool() as p:
-        final_circuits = p.starmap(
-            starmap_func,
-            zip(circuits[:proxy_pass_amount], seeds),
-        )
+        for circ in tqdm(
+            p.imap_unordered(
+                starmap_func,
+                zip(circuits[:proxy_pass_amount], seeds, range(proxy_pass_amount)),
+            ),
+            total=proxy_pass_amount,
+        ):
+            final_circuits.append(circ)
 
     final_circuits.sort(key=lambda qc: qc.expressibility)
     for i, circuit in enumerate(final_circuits):