Merge pull request #9 from qDNA-yonsei/entanglement

Entanglement

qdna/compression/schmidt.py

@@ -94,15 +94,11 @@ def __init__(self, params, label=None, opt_params=None):
             self.svd = "auto" if opt_params.get("svd") is None else \
                 opt_params.get("svd")
 
-        # The trash and latent qubits must take into account that the qiskit qubits are reversed.
-        complement = sorted(
-            set(range(self.num_qubits)).difference(set(self.partition))
-        )[::-1]
-        self.latent_qubits = [
-            self.num_qubits-i-1 for i in complement
-        ]
-        self.trash_qubits = sorted(
-            set(range(self.num_qubits)).difference(set(self.latent_qubits))
+        # The trash and latent qubits must take into account that the qiskit
+        # qubits are reversed. See line `return circuit.reverse_bits()`.
+        self.trash_qubits = sorted([self.num_qubits-i-1 for i in self.partition])
+        self.latent_qubits = sorted(
+            set(range(self.num_qubits)).difference(set(self.trash_qubits))
         )
 
         if label is None:
@@ -120,17 +116,18 @@ def _define_initialize(self):
             circuit.initialize(self.params)
             return  circuit.inverse()
 
+        # reg_a = trash register, reg_b = latent register.
         circuit, reg_a, reg_b = self._create_quantum_circuit()
 
-        # Schmidt decomposition
+        # Schmidt decomposition.
         rank, svd_u, _, svd_v = schmidt_decomposition(
             self.params, reg_a, rank=self.low_rank, svd=self.svd
         )
 
-        # Schmidt measure of entanglement
+        # Schmidt measure of entanglement.
         e_bits = _to_qubits(rank)
 
-        # Phase 3 and 4 encode gates U and V.T
+        # Phase 3 and 4 encode gates U and V.T.
         self._encode(svd_u, circuit, reg_b)
         self._encode(svd_v.T, circuit, reg_a)
 

qdna/graph.py

@@ -0,0 +1,209 @@
+# Copyright 2023 qdna-lib project.
+
+# 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.
+
+import itertools
+import random
+from dwave.samplers import SimulatedAnnealingSampler
+
+def min_cut_fixed_size_optimal(graph, size_a, size_b):
+    '''
+    Optimal solution to the minimum cut problem with fixed sizes for the sets.
+
+    O(n! / k!(n-k)!) x O(m), where m is the number of edges in the graph.
+    The total number of edges m in a complete graph with n nodes is given by
+    m = n(n-1) / 2
+    '''
+    nodes = list(graph.nodes())
+    min_cut_weight = float('inf')
+    min_cut_partition = (set(), set())
+
+    # Iterate over all combinations for set A
+    for nodes_a in itertools.combinations(nodes, size_a):
+        set_a = set(nodes_a)
+        set_b = set(nodes) - set_a
+
+        # Ensure the size of set B is as required
+        if len(set_b) != size_b:
+            continue
+
+        # Calculate the sum of weights of edges between the two sets
+        cut_weight = sum(
+            graph[u][v]['weight'] for u in set_a for v in set_b if graph.has_edge(u, v)
+        )
+
+        # Update min cut if a lower weight is found
+        if cut_weight < min_cut_weight:
+            min_cut_weight = cut_weight
+            min_cut_partition = (set_a, set_b)
+
+    return min_cut_partition, min_cut_weight
+
+def min_cut_fixed_size_heuristic(graph, size_a, size_b):
+    '''
+    Heuristic approach for the Min-Cut problem with a fixed number of nodes in
+    each partition.
+
+    This approach aims to find a partition of the graph where the total edge
+    weight crossing the cut is as low as possible, given the size constraints.
+    The algorithm iteratively attempts to reduce the total weight of the cut by
+    swapping nodes between sets A and B, provided the swap decreases the total
+    weight of the cut.
+    This is a heuristic approach and may not always find the globally optimal
+    solution, especially for complex or large graphs.
+
+    O(k * n_a * n_b * m):
+        k: number of iterations (vary significantly based on the graph's
+        structure and the initial partitioning).
+        n_a: subsystem A number of qubits.
+        n_b: subsystem B number of qubits.
+        m: number of edges between a node and subsystem B (typically equal to n_b).
+
+        Example worst-case scenario (n_a=n_b=n/2):
+        O(k * n^3)
+    '''
+    nodes = list(graph.nodes())
+    random.shuffle(nodes)  # Shuffle nodes to randomize initial selection
+
+    # Initialize sets A and B with random nodes
+    set_a = set(nodes[:size_a])
+    set_b = set(nodes[size_a:size_a + size_b])
+
+    def swapping_weight(node, other_node, set_node, set_other_node):
+        # Entanglement (without - with) swapping.
+        # A positive result indicates that a swap should be made
+        # to reduce entanglement. That is, the entanglement with
+        # a swap is smaller than without.
+        return \
+        sum(graph[node][neighbor]['weight']
+                for neighbor in set_other_node if neighbor is not other_node) - \
+        sum(graph[node][neighbor]['weight']
+                for neighbor in set_node if neighbor is not node)
+
+    # Iteratively try to improve the cut by swapping nodes between A and B
+    improved = True
+    while improved:
+        improved = False
+        for node_a in set_a:
+            for node_b in set_b:
+                weight_a = swapping_weight(node_a, node_b, set_a, set_b)
+                weight_b = swapping_weight(node_b, node_a, set_b, set_a)
+                total_weight = weight_a + weight_b
+                # Here, the entanglements of both nodes are
+                # considered simultaneously.
+                if total_weight > 0:
+                    # Swap the nodes if the total entanglement with the swap is
+                    # smaller. In other words, the entanglement without
+                    # swapping is greater, which means that `total_weight` is
+                    # positive.
+                    set_a.remove(node_a)
+                    set_b.remove(node_b)
+                    set_a.add(node_b)
+                    set_b.add(node_a)
+                    improved = True
+                    break
+
+            if improved:
+                break
+
+    # Sorts the sets when the subsystem sizes are equal
+    if size_a == size_b and sorted(set_a)[0] > sorted(set_b)[0]:
+        set_a, set_b = set_b, set_a
+
+    # Calculate the sum of weights of edges between the two sets
+    cut_weight = sum(
+        graph[u][v]['weight'] for u in set_a for v in set_b if graph.has_edge(u, v)
+    )
+
+    return (set_a, set_b), cut_weight
+
+
+# D-Wave functions
+
+def graph_to_qubo(graph):
+    '''
+    Map the graph to a QUBO model.
+    '''
+    qubo = {}
+
+    max_weight = max(weight for _, _, weight in graph.edges(data='weight'))
+
+    for i, j, weight in graph.edges(data='weight'):
+        # Set qubo_{ij} to be `max_weight - weight` for min-cut.
+        weight = max_weight - weight
+        qubo[(i, i)] = qubo.get((i, i), 0) - weight
+        qubo[(j, j)] = qubo.get((j, j), 0) - weight
+        qubo[(i, j)] = qubo.get((i, j), 0) + 2 * weight
+
+    return qubo
+
+def graph_to_ising(graph):
+    '''
+    Map the graph to an Ising model.
+    '''
+    ising = {}
+    local_field = {}
+
+    max_weight = max(weight for _, _, weight in graph.edges(data='weight'))
+
+    for i, j, weight in graph.edges(data='weight'):
+        # Set qubo_{ij} to be `max_weight - weight` for min-cut.
+        weight = max_weight - weight
+        ising[(i, j)] = weight
+
+    # Add interaction strengths and local fields.
+    for i in graph.nodes():
+        local_field[i] = 0  # local fields
+
+    return ising, local_field
+
+def min_cut_dwave(graph, size_a=None, sample_method='ising', sampler=SimulatedAnnealingSampler(), num_reads=100):
+
+    if sample_method == 'qubo':
+        # Map the graph to a QUBO model.
+        qubo = graph_to_qubo(graph)
+        response = sampler.sample_qubo(qubo, num_reads=num_reads)
+    else:
+        # Map the graph to an Ising model.
+        ising, local_field = graph_to_ising(graph)
+        response = sampler.sample_ising(local_field, ising, num_reads=num_reads)
+    print(response)
+    # Find the sample with the lowest energy (best result), respecting the size of the block.
+    min_cut_weight = float('inf')
+    set_a = None
+    if size_a is not None:
+        for sample, energy in response.data(['sample', 'energy']):
+            if energy < min_cut_weight and sum(v for v in sample.values() if v==1) == size_a:
+                min_cut_weight = energy
+                set_a = sample
+    else:
+        set_a = response.first.sample
+        min_cut_weight = response.first.energy
+
+    # Subsystem B is the complement of subsystem A.
+    nodes = set(graph.nodes())
+    set_a = {k for k, v in set_a.items() if v == 1}
+    size_a = len(set_a)
+    set_b = nodes - set_a
+    size_b = len(set_b)
+
+    # Sorts the sets when the subsystem sizes are equal.
+    if size_a == size_b and sorted(set_a)[0] > sorted(set_b)[0]:
+        set_a, set_b = set_b, set_a
+
+    # Calculate the sum of weights of edges between the two sets.
+    cut_weight = sum(
+        graph[u][v]['weight'] for u in set_a for v in set_b if graph.has_edge(u, v)
+    )
+
+    return (set_a, set_b), cut_weight