add mpo expectation in templates

Author: refraction-ray

CHANGELOG.md

@@ -6,6 +6,12 @@
 
 - add sigmoid method on backends
 
+- add MPO expectation template function for MPO evaluation on circuit
+
+### Fixed
+
+- fix the bug in QuOperator.from_local_tensor where the dtype should always be in numpy context
+
 ## 0.0.220301
 
 ### Added

tensorcircuit/quantum.py

@@ -35,7 +35,7 @@
 except ImportError:
     pass
 
-from .cons import backend, contractor, dtypestr
+from .cons import backend, contractor, dtypestr, npdtype
 from .backends import get_backend  # type: ignore
 
 Tensor = Any
@@ -377,9 +377,7 @@ def from_local_tensor(
         out_edges = [localn[i] for i in out_axes]
         in_edges = [localn[i] for i in in_axes]  # type: ignore
         id_nodes = [
-            CopyNode(2, d, dtype=tensor.dtype)
-            for i, d in enumerate(space)
-            if i not in loc
+            CopyNode(2, d, dtype=npdtype) for i, d in enumerate(space) if i not in loc
         ]
         for n in id_nodes:
             out_edges.append(n[0])

tensorcircuit/templates/measurements.py

@@ -7,6 +7,7 @@
 
 from ..circuit import Circuit
 from ..cons import backend, dtypestr
+from ..quantum import QuOperator
 from .. import gates as G
 
 Tensor = Any
@@ -98,6 +99,12 @@ def sparse_expectation(c: Circuit, hamiltonian: Tensor) -> Tensor:
     return backend.real(expt)[0, 0]
 
 
+def mpo_expectation(c: Circuit, mpo: QuOperator) -> Tensor:
+    mps = c.get_quvector()
+    e = (mps.adjoint() @ mpo @ mps).eval_matrix()
+    return backend.real(e)[0, 0]
+
+
 def heisenberg_measurements(
     c: Circuit,
     g: Graph,

tests/test_templates.py

@@ -110,3 +110,22 @@ def test_amplitude_encoding(backend):
         figs, 2, tc.array_to_tensor(np.array([0, 3, 1, 2]), dtype="int32")
     )
     np.testing.assert_allclose(states[0], 1 / np.sqrt(2) * np.array([1, 1, 0, 0]))
+
+
+@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
+def test_mpo_measurement(backend):
+    def f(theta):
+        mpo = tc.quantum.QuOperator.from_local_tensor(
+            tc.array_to_tensor(tc.gates._x_matrix), [2, 2, 2], [0]
+        )
+        c = tc.Circuit(3)
+        c.ry(0, theta=theta)
+        c.H(1)
+        c.H(2)
+        e = tc.templates.measurements.mpo_expectation(c, mpo)
+        return e
+
+    v, g = tc.backend.jit(tc.backend.value_and_grad(f))(tc.backend.ones([]))
+
+    np.testing.assert_allclose(v, 0.84147, atol=1e-4)
+    np.testing.assert_allclose(g, 0.54032, atol=1e-4)