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Modify models.encodings.hamming_weight_encoder to upload data in the lexicographical order #1572

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merged 4 commits into from
Feb 3, 2025
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Modify models.encodings.hamming_weight_encoder to upload data in the lexicographical order #1572

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2e5d720
lexicographical order
renatomello Feb 3, 2025
d3d4134
fix test
renatomello Feb 3, 2025
7c00b7e
removed commented lines
renatomello Feb 3, 2025
cf99513
short one liner
renatomello Feb 3, 2025
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7 changes: 7 additions & 0 deletions src/qibo/models/encodings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -371,6 +371,13 @@ def hamming_weight_encoder(
initial_string = np.array([1] * weight + [0] * (nqubits - weight))
bitstrings, targets_and_controls = _ehrlich_algorithm(initial_string)

# sort data such that the encoding is performed in lexicographical order
lex_order = [int(string, 2) for string in bitstrings]
lex_order_sorted = np.sort(np.copy(lex_order))
lex_order = [np.where(lex_order_sorted == num)[0][0] for num in lex_order]
data = data[lex_order]
del lex_order, lex_order_sorted

# Calculate all gate phases necessary to encode the amplitudes.
_data = np.abs(data) if complex_data else data
thetas = _generate_rbs_angles(_data, nqubits, architecture="diagonal")
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7 changes: 5 additions & 2 deletions tests/test_models_encodings.py
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Expand Up @@ -217,6 +217,7 @@ def test_unary_encoder_random_gaussian(backend, nqubits, seed):
backend.assert_allclose(stddev, theoretical_norm, atol=1e-1)


@pytest.mark.parametrize("seed", [10])
@pytest.mark.parametrize("optimize_controls", [False, True])
@pytest.mark.parametrize("complex_data", [False, True])
@pytest.mark.parametrize("full_hwp", [False, True])
Expand All @@ -229,6 +230,7 @@ def test_hamming_weight_encoder(
full_hwp,
complex_data,
optimize_controls,
seed,
):
n_choose_k = int(binom(nqubits, weight))
dims = 2**nqubits
Expand All @@ -237,15 +239,16 @@ def test_hamming_weight_encoder(
initial_string = np.array([1] * weight + [0] * (nqubits - weight))
indices = _ehrlich_algorithm(initial_string, False)
indices = [int(string, 2) for string in indices]
indices_lex = np.sort(np.copy(indices))

rng = np.random.default_rng(10)
rng = np.random.default_rng(seed)
data = rng.random(n_choose_k)
if complex_data:
data = data.astype(complex) + 1j * rng.random(n_choose_k)
data /= np.linalg.norm(data)

target = np.zeros(dims, dtype=dtype)
target[indices] = data
target[indices_lex] = data
target = backend.cast(target, dtype=target.dtype)

circuit = hamming_weight_encoder(
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