new test about jit and grad

Author: Huang-Xu-Yang

tests/test_timeevol.py

@@ -203,6 +203,142 @@ def objective_function(params):
     print(objective_function(tc.backend.ones(4)))
 
 
+def test_ode_evol_jit_grad(highp, jaxb):
+    try:
+        import diffrax  # pylint: disable=unused-import
+    except ImportError:
+        pytest.skip("diffrax not installed, skipping test")
+
+    zz_ham = tc.quantum.PauliStringSum2COO([[3, 3, 0, 0], [0, 3, 3, 0]], [1, 1])
+    x_ham = tc.quantum.PauliStringSum2COO([[1, 0, 0, 0], [0, 1, 0, 0]], [1, 1])
+
+    c = tc.Circuit(4)
+    c.x([1, 3])
+    psi0 = c.state()
+
+    # Example with parameterized Hamiltonian and optimization
+    def parametrized_hamiltonian(t, *params):
+        # params = [J0, J1, h0, h1] - parameters to optimize
+        J_t = params[0] + params[1] * tc.backend.sin(2.0 * t)
+        h_t = params[2] + params[3] * tc.backend.cos(1.5 * t)
+
+        return J_t * zz_ham + h_t * x_ham
+
+    def zz_correlation(state):
+        n = int(np.log2(state.shape[0]))
+        circuit = tc.Circuit(n, inputs=state)
+        return circuit.expectation_ps(z=[0, 1])
+
+    @tc.backend.jit
+    @tc.backend.value_and_grad
+    def kv_ode_solver_(params):
+        states = tc.timeevol.ode_evol_global(
+            parametrized_hamiltonian,
+            psi0,
+            tc.backend.convert_to_tensor([0, 10.0]),
+            None,
+            *params,
+            atol=1.0e-15,
+            rtol=1.0e-15,
+            solver="Kvaerno5",
+            ode_backend="diffrax",
+        )
+        return tc.backend.real(zz_correlation(states[-1]))
+
+    @tc.backend.jit
+    @tc.backend.value_and_grad
+    def ts_ode_solver_(params):
+        states = tc.timeevol.ode_evol_global(
+            parametrized_hamiltonian,
+            psi0,
+            tc.backend.convert_to_tensor([0, 10.0]),
+            None,
+            *params,
+            ode_backend="diffrax",
+            atol=1.0e-13,
+            rtol=1.0e-13,
+            dt0=0.005,
+        )
+        return tc.backend.real(zz_correlation(states[-1]))
+
+    @tc.backend.jit
+    @tc.backend.value_and_grad
+    def do5_ode_solver_(params):
+        states = tc.timeevol.ode_evol_global(
+            parametrized_hamiltonian,
+            psi0,
+            tc.backend.convert_to_tensor([0, 10.0]),
+            None,
+            *params,
+        )
+        return tc.backend.real(zz_correlation(states[-1]))
+
+    paras = np.random.rand(4)
+    s1 = kv_ode_solver_(paras)
+    s2 = ts_ode_solver_(paras)
+    s3 = do5_ode_solver_(paras)
+
+    v1, g1 = s1
+    v2, g2 = s2
+    v3, g3 = s3
+
+    assert (np.linalg.norm(v1 - v2) < 1e-8) & (np.linalg.norm(v1 - v3) < 1e-8)
+    assert (np.linalg.norm(g1 - g2) < 1e-8) & (np.linalg.norm(g1 - g3) < 1e-8)
+
+    ######################################################################
+
+    def local_hamiltonian(t, Omega, phi):
+        angle = phi * t
+        coeff = Omega * tc.backend.cos(2.0 * t)  # Amplitude modulation
+
+        # Single-qubit Rabi Hamiltonian (2x2 matrix)
+        hx = coeff * tc.backend.cos(angle) * tc.gates.x().tensor
+        hy = coeff * tc.backend.sin(angle) * tc.gates.y().tensor
+        return hx + hy
+
+    # Initial state: GHZ state |0000⟩ + |1111⟩
+    c = tc.Circuit(4)
+    c.h(0)
+    for i in range(3):
+        c.cnot(i, i + 1)
+    psi0 = c.state()
+
+    # Evolve with local Hamiltonian acting on qubit 1
+    @tc.backend.jit
+    @tc.backend.value_and_grad
+    def ts_ode_solver_local(paras):
+        states = tc.timeevol.ode_evol_local(
+            local_hamiltonian,
+            psi0,
+            tc.backend.convert_to_tensor([0, 10.0]),
+            [2],  # Apply to qubit 1
+            None,
+            *paras,  # Omega=1.0, phi=2.0
+            ode_backend="diffrax",
+        )
+        return tc.backend.real(zz_correlation(states[-1]))
+
+    @tc.backend.jit
+    @tc.backend.value_and_grad
+    def do5_ode_solver_local(paras):
+        states = tc.timeevol.ode_evol_local(
+            local_hamiltonian,
+            psi0,
+            tc.backend.convert_to_tensor([0, 10.0]),
+            [2],  # Apply to qubit 1
+            None,
+            *paras,  # Omega=1.0, phi=2.0
+        )
+        return tc.backend.real(zz_correlation(states[-1]))
+
+    paras = np.random.rand(2)
+    s1 = ts_ode_solver_local(paras)
+    s2 = do5_ode_solver_local(paras)
+    v1, g1 = s1
+    v2, g2 = s2
+    assert (np.linalg.norm(v1 - v2) < 1e-8) & (np.linalg.norm(g1 - g2) < 1e-8)
+
+
 @pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
 def test_ed_evol(backend):
     n = 4