Merge pull request ScQ-Cloud#122 from qtzhuang/zqt

Add amplitude embedding

quafu/algorithms/__init__.py

@@ -4,4 +4,5 @@
 from .ansatz import QAOAAnsatz, AlterLayeredAnsatz, QuantumNeuralNetwork
 from .estimator import Estimator
 from .templates.angle import AngleEmbedding
+from .templates.amplitude import AmplitudeEmbedding
 from .templates.basic_entangle import BasicEntangleLayers

quafu/algorithms/templates/amplitude.py

@@ -0,0 +1,243 @@
+# (C) Copyright 2023 Beijing Academy of Quantum Information Sciences
+#
+# 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
+#
+#    http://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.
+
+"""Amplitude Embedding by a decomposition into gates"""
+from quafu.circuits import QuantumCircuit
+import quafu.elements.element_gates as qeg
+import numpy as np
+
+
+class AmplitudeEmbedding:
+    def __init__(self, state, num_qubits, pad_with=None, normalize=False):
+        """
+        Args:
+            state(np.array): The state to be embedded
+            num_qubits(int): the number of qubit
+            pad_with (float or complex): if not None, the input will be padded to size 2**num_qubits
+            normalize (bool): whether to automatically normalize the state
+        """
+        self.num_qubits = num_qubits
+        self.pad_with = pad_with
+        self.normalize = normalize
+        self.state = self._preprocess(state, num_qubits, pad_with, normalize)
+        self.gate_list = self._build()
+
+    def __iter__(self):
+        return iter(self.gate_list)
+
+    def __getitem__(self, index):
+        return self.gate_list[index]
+
+    def _preprocess(self, state, num_qubits, pad_with, normalize):
+        batched = np.ndim(state) > 1
+        ##TODO(qtzhuang): If state are batched, additional processing is required
+        if batched:
+            raise ValueError("Currently not support batched state.")
+        state_batch = state if batched else [state]
+        new_state_batch = []
+
+        # apply pre-processing
+        for feature_set in state_batch:
+            shape = np.shape(feature_set)
+
+            # check shape
+            if len(shape) != 1:
+                raise ValueError(f"state must be a one-dimensional tensor; got shape {shape}.")
+
+            n_state = shape[0]
+            dim = 2 ** num_qubits
+            if pad_with is None and n_state != dim:
+                raise ValueError(
+                    f"The length of state should be {dim}; got length {n_state}.Please check num_qubits "
+                    f"or Use the 'pad_with' argument for automated padding."
+                )
+
+            if pad_with is not None:
+                if n_state > dim:
+                    raise ValueError(
+                        f"state must be of length {dim} or smaller to be padded; got length {n_state}."
+                    )
+
+                # pad
+                if n_state < dim:
+                    padding = [pad_with] * (dim - n_state)
+                    padding = np.asarray(padding, dtype=feature_set.dtype)
+                    feature_set = np.hstack([feature_set, padding])
+
+            # normalize
+            norm = np.sum(np.abs(feature_set) ** 2)
+            # tolerance for normalization
+            TOLERANCE = 1e-10
+            if not np.allclose(norm, 1.0, atol=TOLERANCE):
+                if normalize or pad_with:
+                    feature_set = feature_set / np.sqrt(norm)
+                else:
+                    raise ValueError(
+                        f"state must be a vector of norm 1.0; got norm {norm}. "
+                        "Use 'normalize=True' to automatically normalize."
+                    )
+            new_state_batch.append(feature_set)
+
+        return np.stack(new_state_batch).astype(np.complex128) if batched else new_state_batch[0].astype(np.complex128)
+
+    def _build(self):  
+
+        a = np.abs(self.state)
+        omega = np.angle(self.state)
+        # change order of qubits, since original code was written for IBM machines
+        qubits_reverse = range(self.num_qubits)[::-1]
+        gate_list = []
+
+        # Apply inverse y rotation cascade to prepare correct absolute values of amplitudes
+        for k in range(len(qubits_reverse), 0, -1):
+            alpha_y_k = _get_alpha_y(a, len(qubits_reverse), k)
+            control = qubits_reverse[k:]
+            target = qubits_reverse[k - 1]
+            gate_list.extend(_apply_uniform_rotation_dagger(qeg.RYGate, alpha_y_k, control, target))
+
+        # If necessary, apply inverse z rotation cascade to prepare correct phases of amplitudes
+        if not np.allclose(omega, 0):
+            for k in range(len(qubits_reverse), 0, -1):
+                alpha_z_k = _get_alpha_z(omega, len(qubits_reverse), k)
+                control = qubits_reverse[k:]
+                target = qubits_reverse[k - 1]
+                if len(alpha_z_k) > 0:
+                    gate_list.extend(
+                        _apply_uniform_rotation_dagger(qeg.RZGate, alpha_z_k, control, target)
+                    )
+
+        return gate_list
+
+## MottonenStatePreparation related functions.
+def gray_code(rank):
+    """Generates the Gray code of given rank.
+
+    Args:
+        rank (int): rank of the Gray code (i.e. number of bits)
+    """
+
+    def gray_code_recurse(g, rank):
+        k = len(g)
+        if rank <= 0:
+            return
+
+        for i in range(k - 1, -1, -1):
+            char = "1" + g[i]
+            g.append(char)
+        for i in range(k - 1, -1, -1):
+            g[i] = "0" + g[i]
+
+        gray_code_recurse(g, rank - 1)
+
+    g = ["0", "1"]
+    gray_code_recurse(g, rank - 1)
+
+    return g
+
+def _matrix_M_entry(row, col):
+
+        # (col >> 1) ^ col is the Gray code of col
+        b_and_g = row & ((col >> 1) ^ col)
+        sum_of_ones = 0
+        while b_and_g > 0:
+            if b_and_g & 0b1:
+                sum_of_ones += 1
+
+            b_and_g = b_and_g >> 1
+
+        return (-1) ** sum_of_ones
+
+def compute_theta(alpha):
+    ln = alpha.shape[-1]
+    k = np.log2(ln)
+
+    M_trans = np.zeros(shape=(ln, ln))
+    for i in range(len(M_trans)):
+        for j in range(len(M_trans[0])):
+            M_trans[i, j] = _matrix_M_entry(j, i)
+
+    theta = np.transpose(np.dot(M_trans, np.transpose(alpha)))
+
+    return theta / 2**k
+
+
+def _apply_uniform_rotation_dagger(gate, alpha, control_wires, target_wire):
+
+    gate_list = []
+    theta = compute_theta(alpha)
+
+    gray_code_rank = len(control_wires)
+
+    if gray_code_rank == 0:
+        if np.all(theta[..., 0] != 0.0):
+            gate_list.append(gate(pos = target_wire, paras = theta[0]))
+        return gate_list
+
+    code = gray_code(gray_code_rank)
+    num_selections = len(code)
+
+    control_indices = [
+        int(np.log2(int(code[i], 2) ^ int(code[(i + 1) % num_selections], 2)))
+        for i in range(num_selections)
+    ]
+
+    for i, control_index in enumerate(control_indices):
+        if np.all(theta[..., i] != 0.0):
+            gate_list.append(gate(pos=target_wire, paras=theta[i]))
+        gate_list.append(qeg.CXGate(control_wires[control_index], target_wire))
+    return gate_list
+
+def _get_alpha_z(omega, n, k):
+
+    indices1 = [
+        [(2 * j - 1) * 2 ** (k - 1) + l - 1 for l in range(1, 2 ** (k - 1) + 1)]
+        for j in range(1, 2 ** (n - k) + 1)
+    ]
+    indices2 = [
+        [(2 * j - 2) * 2 ** (k - 1) + l - 1 for l in range(1, 2 ** (k - 1) + 1)]
+        for j in range(1, 2 ** (n - k) + 1)
+    ]
+
+    term1 = np.take(omega, indices=indices1, axis=-1)
+    term2 = np.take(omega, indices=indices2, axis=-1)
+    diff = (term1 - term2) / 2 ** (k - 1)
+
+    return np.sum(diff, axis=-1)
+
+def _get_alpha_y(a, n, k):
+
+    indices_numerator = [
+        [(2 * (j + 1) - 1) * 2 ** (k - 1) + l for l in range(2 ** (k - 1))]
+        for j in range(2 ** (n - k))
+    ]
+    numerator = np.take(a, indices=indices_numerator, axis=-1)
+    numerator = np.sum(np.abs(numerator) ** 2, axis=-1)
+
+    indices_denominator = [[j * 2**k + l for l in range(2**k)] for j in range(2 ** (n - k))]
+    denominator = np.take(a, indices=indices_denominator, axis=-1)
+    denominator = np.sum(np.abs(denominator) ** 2, axis=-1)
+
+    # Divide only where denominator is zero, else leave initial value of zero.
+    # The equation guarantees that the numerator is also zero in the corresponding entries.
+
+    with np.errstate(divide="ignore", invalid="ignore"):
+        division = numerator / denominator
+
+    # Cast the numerator and denominator to ensure compatibility with interfaces
+    division = np.array(division, dtype=np.float64)
+    denominator = np.array(denominator, dtype=np.float64)
+
+    division = np.where(denominator != 0.0, division, 0.0)
+
+    return 2 * np.arcsin(np.sqrt(division))

tests/quafu/algorithms/amplitude_test.py

@@ -0,0 +1,34 @@
+# (C) Copyright 2023 Beijing Academy of Quantum Information Sciences
+#
+# 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
+#
+#    http://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 quafu.circuits import QuantumCircuit
+import quafu.elements.element_gates as qeg
+from quafu.algorithms import AmplitudeEmbedding
+import numpy as np
+
+class TestAmplitudeEmbedding:
+    """Example of amplitude embedding"""
+
+    def test_build(self):
+        num_qubits = 2
+        qc = QuantumCircuit(num_qubits)
+        state = np.array([6,-12.5,11.15,7])
+        qc.add_gates(AmplitudeEmbedding(state=state, num_qubits=num_qubits, normalize=True))
+        qc.draw_circuit(width=num_qubits)
+
+    def test_build_pad(self):
+        num_qubits = 2
+        qc = QuantumCircuit(num_qubits)
+        state = np.array([6,-12.5,11.15])
+        qc.add_gates(AmplitudeEmbedding(state=state, num_qubits=num_qubits, pad_with=7, normalize=True))
+        qc.draw_circuit(width=num_qubits)