diff --git a/.github/CHANGELOG.md b/.github/CHANGELOG.md
index 4c0b3875..b90dfbc7 100644
--- a/.github/CHANGELOG.md
+++ b/.github/CHANGELOG.md
@@ -15,6 +15,8 @@
 
 * Simplifies the internal working of Williamson decomposition [(#366)](https://github.com/XanaduAI/thewalrus/pull/338). 
 
+* Improves the handling of an edge case in Takagi [(#373)](https://github.com/XanaduAI/thewalrus/pull/373).
+
 ### Bug fixes
 
 ### Documentation
diff --git a/thewalrus/decompositions.py b/thewalrus/decompositions.py
index b96ffdbb..43967077 100644
--- a/thewalrus/decompositions.py
+++ b/thewalrus/decompositions.py
@@ -210,8 +210,10 @@ def takagi(A, svd_order=True):
         list_vals.sort(reverse=svd_order)
         sorted_ls, permutation = zip(*list_vals)
         return np.array(sorted_ls), Uc[:, np.array(permutation)]
-
-    phi = np.angle(A[0, 0])
+    # Find the element with the largest absolute value
+    pos = np.unravel_index(np.argmax(np.abs(A)), (n, n))
+    # Use it to find whether the input is a global phase times a real matrix
+    phi = np.angle(A[pos])
     Amr = np.real_if_close(np.exp(-1j * phi) * A)
     if np.isrealobj(Amr):
         vals, U = takagi(Amr, svd_order=svd_order)
diff --git a/thewalrus/tests/test_decompositions.py b/thewalrus/tests/test_decompositions.py
index 630eb32a..53975dd2 100644
--- a/thewalrus/tests/test_decompositions.py
+++ b/thewalrus/tests/test_decompositions.py
@@ -394,3 +394,24 @@ def test_real_degenerate():
     rl, U = takagi(mat)
     assert np.allclose(U @ U.conj().T, np.eye(len(mat)))
     assert np.allclose(U @ np.diag(rl) @ U.T, mat)
+
+
+@pytest.mark.parametrize("size", [10, 20, 100])
+def test_flat_phase(size):
+    """Test that the correct decomposition is obtained even if the first entry is 0"""
+    A = np.random.rand(size, size) + 1j * np.random.rand(size, size)
+    A += A.T
+    A[0, 0] = 0
+    l, u = takagi(A)
+    assert np.allclose(A, u * l @ u.T)
+
+
+def test_real_input_edge():
+    """Adapted from https://math.stackexchange.com/questions/4418925/why-does-this-algorithm-for-the-takagi-factorization-fail-here"""
+    rng = np.random.default_rng(0)  # Important for reproducibility
+    A = (rng.random((100, 100)) - 0.5) * 114
+    A = A * A.T  # make A symmetric
+    l, u = takagi(A)
+    # Now, reconstruct A, see
+    Ar = u * l @ u.T
+    assert np.allclose(A, Ar)