Calculate complementary polynomial for QSP using flip convolution.

qualtran/bloqs/qsp/fast_qsp.py

@@ -0,0 +1,156 @@
+#  Copyright 2023 Google LLC
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+from typing import Sequence, Union
+
+import numpy as np
+from numpy.typing import NDArray
+from scipy.optimize import minimize
+
+
+class FastComplementaryQSPHelper:
+    """
+    A helper class to obtain the complimentary polynomial given a QSP polynomial.
+
+    Attributes:
+        P: Co-efficients of a complex QSP polynomial.
+        only_reals: If `true`, then only real polynomial values will be returned.
+    """
+
+    def __init__(self, poly: NDArray, only_reals: bool = False):
+        self.only_reals = only_reals
+        if self.only_reals:
+            assert poly.dtype == np.float64
+            self.conv_p_negative = self.conv_by_flip_conj(poly) * -1
+        else:
+            assert poly.dtype == np.complex128
+            self.conv_p_negative = self.complex_conv_by_flip_conj(poly.real, poly.imag) * -1
+        self.conv_p_negative[poly.shape[0] - 1] = 1 - np.linalg.norm(poly) ** 2
+
+    def loss_function(self, x: NDArray):
+        if self.only_reals:
+            conv_result = self.conv_by_flip_conj(x)
+        else:
+            real_part = x[: len(x) // 2]
+            imag_part = x[len(x) // 2 :]
+            conv_result = self.complex_conv_by_flip_conj(real_part, imag_part)
+
+        # Compute loss using squared distance function
+        loss = np.linalg.norm(self.conv_p_negative - conv_result) ** 2
+        return loss
+
+    @staticmethod
+    def array_to_complex(x: NDArray) -> NDArray:
+        """
+        Converts a real array into a complex array.
+
+        This method assumes that the real array is 1d and is twice the size of the desired complex array. The first half
+        of the array is understood to be the real part, and the second half, the imaginary part.
+
+        Args:
+             A real nd array twice as long as the desired complex array
+        Returns:
+            A complex nd array built from the real array
+        """
+        real_part = x[: len(x) // 2]
+        imag_part = x[len(x) // 2 :]
+        return real_part + 1.0j * imag_part
+
+    @staticmethod
+    def conv_by_flip_conj(poly: NDArray) -> NDArray:
+        return np.convolve(poly, np.flip(poly, axis=[0]), mode="full")
+
+    @staticmethod
+    def complex_conv_by_flip_conj(real_part: NDArray, imag_part: NDArray):
+        """
+        Performs the flip convolution.
+
+        This method is used in sveral parts of the complementary polynomial
+        calculation. Due to a limitation of the scipy optimizer, the
+        input array must be split into its real and imaginary components first.
+        """
+        real_flip = np.flip(real_part, axis=[0])
+        imag_flip = np.flip(-1 * imag_part, axis=[0])
+
+        conv_real_part = np.convolve(real_part, real_flip, mode="full")
+        conv_imag_part = np.convolve(imag_part, imag_flip, mode="full")
+
+        conv_real_imag = np.convolve(real_part, imag_flip, mode="full")
+        conv_imag_real = np.convolve(imag_part, real_flip, mode="full")
+
+        # Compute real and imaginary part of the convolution
+        real_conv = conv_real_part - conv_imag_part
+        imag_conv = conv_real_imag + conv_imag_real
+
+        # Combine to form the complex result
+        return real_conv + 1j * imag_conv
+
+
+def fast_complementary_polynomial(
+    P: Union[Sequence[float], Sequence[complex]],
+    random_state: np.random.RandomState,
+    only_reals: bool = False,
+    tolerance: float = 1e-10,
+):
+    """
+    Computes the Q polynomial given P
+
+    Computes polynomial $Q$ of degree at-most that of $P$, satisfying
+
+        $$ \abs{P(e^{i\theta})}^2 + \abs{Q(e^{i\theta})}^2 = 1 $$
+
+    using the flip convolution method described in Eq(60).  This
+    an alternative for the complementary_polynomial in the
+    generalized_qsp module.
+
+    Note that by default, this method will take a complex input and
+    return a complex output. If only real-valued results are desired,
+    this must be explicitly set by setting "only_reals" to True.
+    Since there are many possible complimentary polynomials given an
+    input P, setting "only_reals" will run a slightly different method
+    than the default to insure the complementary polynomial is real.
+    This method, however, is significantly less accurate than
+    the default method. If a real valued complementary polynomial
+    is desired, it is recommended to use the complementary_polynomial
+    method from the generalized_qsp module instead.
+
+    Args:
+        P: Co-efficients of a complex polynomial.
+        random_state: The random state to use to generate the initial guess of the
+            complementary polynomial Q.
+        only_reals: If true, performs the calculation to only use and return real
+            valued coefficients. Note that if this is set to "true", and P is
+            complex, an error will be thrown.
+        tolerance: The allowable tolerance for finding the minimum of the
+            qsp loss function. In general, this number should be at least 1/10 of
+            the desired tolerance used by the code that calls this method.
+
+    References:
+    [Generalized Quantum Signal Processing](https://arxiv.org/abs/2308.01501)
+        Motlagh and Wiebe. (2023). Equation 60.
+    """
+    if only_reals:
+        poly = np.array(P, dtype=np.float64)
+        q_initial = random_state.randn(poly.shape[0])
+    else:
+        poly = np.array(P, dtype=np.complex128)
+        q_initial = random_state.randn(poly.shape[0] * 2)
+    q_initial_normalized = q_initial / np.linalg.norm(q_initial)
+
+    qsp = FastComplementaryQSPHelper(poly, only_reals=only_reals)
+
+    minimizer = minimize(qsp.loss_function, q_initial_normalized, jac="3-point", tol=tolerance)
+    if only_reals:
+        return minimizer.x
+
+    return qsp.array_to_complex(minimizer.x)

qualtran/bloqs/qsp/fast_qsp_test.py

@@ -0,0 +1,115 @@
+#  Copyright 2023 Google LLC
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import numpy as np
+import pytest
+
+from qualtran.bloqs.for_testing.matrix_gate import MatrixGate
+
+from .fast_qsp import fast_complementary_polynomial
+from .generalized_qsp_test import (
+    check_polynomial_pair_on_random_points_on_unit_circle,
+    random_qsp_polynomial,
+    verify_generalized_qsp,
+)
+
+
+@pytest.mark.parametrize("degree, precision", [(4, 1e-5), (5, 1e-5)])
+def test_complementary_polynomial_quick(degree: int, precision: float):
+    random_state = np.random.RandomState(42)
+    for _ in range(2):
+        P = random_qsp_polynomial(degree, random_state=random_state)
+        Q = fast_complementary_polynomial(P, random_state=random_state)
+        check_polynomial_pair_on_random_points_on_unit_circle(
+            P, Q, random_state=random_state, rtol=precision
+        )
+
+
+@pytest.mark.parametrize("degree, precision", [(3, 1e-2), (4, 1e-1)])
+def test_real_polynomial_has_real_complementary_polynomial_quick(degree: int, precision: float):
+    random_state = np.random.RandomState(42)
+
+    for _ in range(10):
+        P = random_qsp_polynomial(degree, random_state=random_state, only_real_coeffs=True)
+        Q = fast_complementary_polynomial(P, random_state=random_state, only_reals=True)
+        Q = np.around(Q, decimals=8)
+        assert np.isreal(Q).all()
+        check_polynomial_pair_on_random_points_on_unit_circle(
+            P, Q, random_state=random_state, rtol=precision
+        )
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize(
+    "degree, num_tests, precision", [(5, 20, 2e-5), (10, 20, 2e-5), (20, 5, 2e-5), (30, 1, 2e-5)]
+)
+def test_complementary_polynomial(degree: int, num_tests: int, precision: float):
+    random_state = np.random.RandomState(42)
+
+    for _ in range(num_tests):
+        P = random_qsp_polynomial(degree, random_state=random_state)
+        Q = fast_complementary_polynomial(P, random_state=random_state)
+        check_polynomial_pair_on_random_points_on_unit_circle(
+            P, Q, random_state=random_state, rtol=precision
+        )
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("degree, num_tests", [(2, 10), (3, 10), (4, 10), (5, 10), (10, 10)])
+def test_fast_qsp_on_random_unitaries(degree: int, num_tests: int):
+    random_state = np.random.RandomState(102)
+
+    for _ in range(num_tests):
+        P = random_qsp_polynomial(degree, random_state=random_state)
+        U = MatrixGate.random(2, random_state=random_state)
+        Q = fast_complementary_polynomial(P, random_state=random_state)
+        verify_generalized_qsp(U, P, Q=Q)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize(
+    "degree, num_tests, precision",
+    [(2, 10, 2e-2), (3, 10, 2e-2), (4, 10, 5e-2), (5, 10, 2e-2), (10, 10, 2e-2), (20, 10, 2e-2)],
+)
+def test_real_polynomial_has_real_complementary_polynomial(
+    degree: int, num_tests: int, precision: float
+):
+    random_state = np.random.RandomState(42)
+    for _ in range(num_tests):
+        P = random_qsp_polynomial(degree, random_state=random_state, only_real_coeffs=True)
+        Q = fast_complementary_polynomial(P, random_state=random_state, only_reals=True)
+        Q = np.around(Q, decimals=8)
+        assert np.isreal(Q).all()
+        check_polynomial_pair_on_random_points_on_unit_circle(
+            P, Q, random_state=random_state, rtol=precision
+        )
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("bitsize", [1, 2, 3])
+@pytest.mark.parametrize(
+    "degree, negative_power, tolerance",
+    [(2, 0, 1e-5), (2, 1, 1e-5), (2, 2, 1e-5), (5, 0, 1e-4), (5, 1, 1e-4), (5, 2, 1e-4)],
+)
+def test_generalized_qsp_with_complex_poly_on_random_unitaries(
+    bitsize: int, degree: int, negative_power: int, tolerance: float
+):
+    # TODO Fix high error on degree 20 polynomial
+    random_state = np.random.RandomState(42)
+
+    for _ in range(10):
+        U = MatrixGate.random(bitsize, random_state=random_state)
+        P = random_qsp_polynomial(degree, random_state=random_state)
+        Q = fast_complementary_polynomial(P, random_state=random_state)
+        verify_generalized_qsp(U, P, negative_power=negative_power, Q=Q, tolerance=tolerance)

qualtran/bloqs/qsp/generalized_qsp_test.py

@@ -141,6 +141,7 @@ def verify_generalized_qsp(
     Q: Optional[Sequence[complex]] = None,
     *,
     negative_power: int = 0,
+    tolerance: float = 1e-5,
 ):
     input_unitary = cirq.unitary(U)
     N = input_unitary.shape[0]
@@ -156,14 +157,14 @@ def verify_generalized_qsp(
         P, input_unitary, negative_power=negative_power
     )
     actual_top_left = result_unitary[:N, :N]
-    assert_matrices_almost_equal(expected_top_left, actual_top_left)
+    assert_matrices_almost_equal(expected_top_left, actual_top_left, atol=tolerance)
 
     assert not isinstance(gqsp_U.Q, Shaped)
     expected_bottom_left = evaluate_polynomial_of_matrix(
         gqsp_U.Q, input_unitary, negative_power=negative_power
     )
     actual_bottom_left = result_unitary[N:, :N]
-    assert_matrices_almost_equal(expected_bottom_left, actual_bottom_left)
+    assert_matrices_almost_equal(expected_bottom_left, actual_bottom_left, atol=tolerance)
 
 
 @pytest.mark.slow