Merge branch 'calib' of https://github.com/yutuer21/tensorcircuit into tqlpr62

Author: refraction-ray

tests/test_calibrating.py

@@ -13,29 +13,15 @@
 import tensorcircuit as tc
 
 
-def exp_fit(x, y):
-    def fit_function(x_values, y_values, function, init_params):
-        fitparams, _ = curve_fit(function, x_values, y_values, init_params)
-        return fitparams
+def fit_function(x_values, y_values, function, init_params):
+    fitparams, _ = curve_fit(function, x_values, y_values, init_params)
+    return fitparams
 
-    fit_params = fit_function(
-        x, y, lambda x, A, C, T: (A * np.exp(-x / T) + C), [-3, 0, 100]
-    )
-
-    _, _, T = fit_params
 
-    return T
-
-
-@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
-def test_calib_t1(backend):
-
-    t1 = 300
-    t2 = 100
-    time = 100
-    nstep = int(4 * t1 / time)
+def T1_cali(t1, t2, time, method, excitedstatepopulation):
 
     # calibrating experiments
+    nstep = int(4 * t1 / time)
     pex = []
     for i in range(nstep):
 
@@ -44,38 +30,42 @@ def test_calib_t1(backend):
         for _ in range(i):
             dmc.i(0)
             dmc.thermalrelaxation(
-                0, t1=t1, t2=t2, time=time, method="AUTO", excitedstatepopulation=0
+                0,
+                t1=t1,
+                t2=t2,
+                time=time,
+                method=method,
+                excitedstatepopulation=excitedstatepopulation,
             )
 
         val = dmc.expectation_ps(z=[0])
         p = (1 - val) / 2.0
         pex.append(p)
 
-    # data fitting
-    x1 = np.array([i * time for i in range(nstep)])
-    y1 = np.array(np.real(pex))
+    timelist = np.array([i * time for i in range(nstep)])
+    measurement = np.array(np.real(pex))
 
-    T1 = exp_fit(x1, y1)
-    np.testing.assert_allclose(t1, T1, atol=1e-1)
+    return measurement, timelist
 
 
-@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
-def test_calib_t2(backend):
+def T2_cali(t1, t2, time, method, excitedstatepopulation):
 
-    pex = []
-    t1 = 300
-    t2 = 280
-    time = 50
+    # calibrating experiments
     nstep = int(4 * t2 / time)
-
+    pex = []
     for i in range(nstep):
 
         dmc = tc.DMCircuit(1)
         dmc.h(0)
         for _ in range(0, i):
             dmc.i(0)
             dmc.thermalrelaxation(
-                0, t1=t1, t2=t2, time=time, method="AUTO", excitedstatepopulation=0
+                0,
+                t1=t1,
+                t2=t2,
+                time=time,
+                method=method,
+                excitedstatepopulation=excitedstatepopulation,
             )
         # dmc.rz(0,theta = i*np.pi/1.5)
         dmc.h(0)
@@ -84,44 +74,81 @@ def test_calib_t2(backend):
         p = (1 - val) / 2.0
         pex.append(p)
 
-    # data fitting
-    x1 = np.array([i * time for i in range(nstep)])
-    y1 = np.array(np.real(pex))
-
-    T2 = exp_fit(x1, y1)
-    np.testing.assert_allclose(t2, T2, atol=1e-1)
+    timelist = np.array([i * time for i in range(nstep)])
+    measurement = np.array(np.real(pex))
 
+    return measurement, timelist
 
-@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
-def test_calib_dep(backend):
 
+def dep_cali(dep, nqubit):
     pex = []
-    pz = 0.02
     nstep = 40
     for i in range(nstep):
 
         dmc = tc.DMCircuit(1)
         dmc.x(0)
         for _ in range(i):
             dmc.s(0)
-            dmc.generaldepolarizing(0, p=pz, num_qubits=1)
+            dmc.generaldepolarizing(0, p=dep, num_qubits=nqubit)
 
         val = dmc.expectation_ps(z=[0])
         p = (1 - val) / 2.0
         if i % 2 == 0:
             pex.append(p)
 
-    # data fitting
-    x1 = np.array([i for i in range(0, nstep, 2)])
-    y1 = np.array(np.real(pex))
+    timelist = np.array([i for i in range(0, nstep, 2)])
+    measurement = np.array(np.real(pex))
+
+    return measurement, timelist
+
+
+@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
+def test_cali_t1(backend):
+    t1 = 300
+    t2 = 100
+    time = 100
+    method = "AUTO"
+    excitedstatepopulation = 0
+    measurement, timelist = T1_cali(t1, t2, time, method, excitedstatepopulation)
+
+    fit_params = fit_function(
+        timelist, measurement, lambda x, A, C, T: (A * np.exp(-x / T) + C), [-3, 0, 100]
+    )
+
+    _, _, T = fit_params
+
+    np.testing.assert_allclose(t1, T, atol=1e-1)
+
+
+@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
+def test_cali_t2(backend):
+    t1 = 300
+    t2 = 280
+    time = 50
+    method = "AUTO"
+    excitedstatepopulation = 0
+    measurement, timelist = T2_cali(t1, t2, time, method, excitedstatepopulation)
 
-    def fit_function(x_values, y_values, function, init_params):
-        fitparams, _ = curve_fit(function, x_values, y_values, init_params)
-        return fitparams
+    fit_params = fit_function(
+        timelist, measurement, lambda x, A, C, T: (A * np.exp(-x / T) + C), [-3, 0, 100]
+    )
+
+    _, _, T = fit_params
 
-    fit_params = fit_function(x1, y1, lambda x, A, B, C: (A * B**x + C), [-0, 0, 0])
+    np.testing.assert_allclose(t2, T, atol=1e-1)
+
+
+@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
+def test_cali_dep(backend):
+    dep = 0.02
+    nqubit = 1
+    measurement, timelist = dep_cali(dep, nqubit)
+
+    fit_params = fit_function(
+        timelist, measurement, lambda x, A, B, C: (A * B**x + C), [-0, 0, 0]
+    )
 
     _, B, _ = fit_params
-    pz1 = (1 - B) / 4.0
+    dep1 = (1 - B) / 4.0**nqubit
 
-    np.testing.assert_allclose(pz, pz1, atol=1e-1)
+    np.testing.assert_allclose(dep, dep1, atol=1e-1)