Merge pull request #10 from xmgao/fix-documentation-errors

Fix documentation errors

README.md

@@ -37,6 +37,7 @@ Here is an example of using SimQN.
     from qns.network.route.dijkstra import DijkstraRouteAlgorithm
     from qns.network.topology.topo import ClassicTopology
     import qns.utils.log as log
+    import logging
 
     init_fidelity = 0.99   # the initial entanglement's fidelity
     nodes_number = 150     # the number of nodes
@@ -102,3 +103,4 @@ Other contributors includes:
 * Zirui Xiao, School of Cyber Science and Technology, University of Science and Technology of China, China.
 * Yuqi Yang, School of Cyber Science and Technology, University of Science and Technology of China, China.
 * Bing Yang, School of Cyber Science and Technology, University of Science and Technology of China, China.
+* Xumin Gao, School of Cyber Science and Technology, University of Science and Technology of China, China.

docs/source/tutorials.entity.memory.rst

@@ -23,7 +23,7 @@ Quantum memory is an entity that can store qubits. It can be equipped to a quant
 
     q1 = Qubit()
     m.write(q1)
-    q2 = m.read()
+    q2 = m.read(q1)
 
 The memory can have a limited size. ``is_full`` function returns whether the memory is full:
 

docs/source/tutorials.models.qubit.rst

@@ -85,18 +85,18 @@ SimQN also provides some commonly used decoherence models, including dephase mod
 
 .. code-block:: python
 
-    from qns.models.qubit.decoherence import DepolarStorageErrorModel, DephaseTransmitErrorModel
+    from qns.models.qubit.decoherence import DepolarStorageErrorModel, DephaseTransferErrorModel
     from qns.models.qubit.factory import QubitFactory
 
-    Qubit = QubitFactory(store_error_model=DepolarStorageErrorModel, transfer_error_model=DephaseTransmitErrorModel)
+    Qubit = QubitFactory(store_error_model=DepolarStorageErrorModel, transfer_error_model=DephaseTransferErrorModel)
     q1 = Qubit(name="q1")
     q2 = Qubit(name="q2")
 
 SimQN also have error models for operating or measuring on qubits, by implementing the ``operate_error_model`` and ``measure_error_model``:
 
 .. code-block:: python
 
-    from qns.models.qubit.decoherence import DepolarStorageErrorModel, DephaseTransmitErrorModel
+    from qns.models.qubit.decoherence import DepolarStorageErrorModel, DephaseTransferErrorModel
     from qns.models.qubit.factory import QubitFactory
 
     Qubit = QubitFactory(operate_decoherence_rate=0.2,

docs/source/tutorials.network.route.rst

@@ -30,7 +30,7 @@ An example of using this algorithm is:
 
 .. code-block:: python
 
-    from qns.network.topology import RandomTopology, ClassicTopology
+    from qns.network.topology import RandomTopology
     from qns.network.network import QuantumNetwork
     from qns.network.route import DijkstraRouteAlgorithm
 
@@ -39,8 +39,8 @@ An example of using this algorithm is:
         lines_number=10,
         qchannel_args={"delay": 0.05, "bandwidth": 10},
         cchannel_args={"delay": 0.05},
-        memory_args=[{"capacity": memory_capacity}],
-        nodes_apps=[EntanglementDistributionApp(init_fidelity=init_fidelity)])
+        memory_args=[{"capacity": 50}],
+        nodes_apps=[EntanglementDistributionApp(init_fidelity=0.99)])
 
     # use the ``DijkstraRouteAlgorithm``, using the bandwidth as the ``metric_func``
     route = DijkstraRouteAlgorithm(metric_func=lambda qchannel: qchannel.bandwidth)

docs/source/tutorials.network.topology.rst

@@ -25,8 +25,8 @@ Topology generators may have more parameters. The following example shows how to
         lines_number=10,
         qchannel_args={"delay": 0.05},
         cchannel_args={"delay": 0.05},
-        memory_args=[{"capacity": memory_capacity}],
-        nodes_apps=[EntanglementDistributionApp(init_fidelity=init_fidelity)])
+        memory_args=[{"capacity": 50}],
+        nodes_apps=[EntanglementDistributionApp(init_fidelity=0.99)])
 
     # build the network
     net = QuantumNetwork(topo=topo)
@@ -54,16 +54,17 @@ SimQN is able to generate classic topologies as well. The classic topology is in
 
 .. code-block:: python
 
-    from qns.network.topology import RandomTopology, ClassicTopology
+    from qns.network.topology import RandomTopology
     from qns.network.network import QuantumNetwork
+    from qns.network.topology.topo import ClassicTopology
 
     topo = RandomTopology(
         nodes_number=5,
         lines_number=10,
         qchannel_args={"delay": 0.05},
         cchannel_args={"delay": 0.05},
-        memory_args=[{"capacity": memory_capacity}],
-        nodes_apps=[EntanglementDistributionApp(init_fidelity=init_fidelity)])
+        memory_args=[{"capacity": 50}],
+        nodes_apps=[EntanglementDistributionApp(init_fidelity=0.99)])
 
     # build the network, classic topology follows the quantum topology
     net = QuantumNetwork(topo=topo, classic_topo=ClassicTopology.Follow)

docs/source/tutorials.quickstart.rst

@@ -24,11 +24,11 @@ The simulation is last for 10 seconds and the ``accuracy`` is 10,000,000,000 slo
     import numpy as np
 
     light_speed = 299791458
-    length = 100000 # 100,000 km
+    length = 100000 # 100,000 m
 
     def drop_rate(length):
         # drop 0.2 db/KM
-        return 1 - np.exp(- length / 50000)
+        return 1 - np.exp(- length / 50000) #or  1 - np.power(10, - length / 50000)
 
     # generate quantum nodes
     n1 = QNode(name="n1")
@@ -88,6 +88,7 @@ First, we generate the simulator and produce the network produce:
     from qns.network.route.dijkstra import DijkstraRouteAlgorithm
     from qns.network.topology.topo import ClassicTopology
     import qns.utils.log as log
+    import logging
 
     init_fidelity = 0.99 # the initial entanglement's fidelity 
     nodes_number = 150 # the number of nodes
@@ -134,7 +135,7 @@ Now, it is possible to run the simulation and get the results:
     s.run()
 
     # count the number of successful entanglement distribution for each session
-    results = [src.apps[0].success_count for req in net.requests]
+    results = [req.src.apps[0].success_count for req in net.requests]
 
     # log the results
     log.monitor(requests_number, nodes_number, results, s.time_spend, sep=" ")

docs/source/tutorials.util.rst

@@ -19,12 +19,14 @@ Users can set the logging level and record logs:
 
 .. code-block:: Python
 
+    import logging
+    
     log.logger.setLevel(logging.INFO)
     a = 1
 
     log.debug("debug message")
-    log.info("info message", a)
-    log.warn("warn message", a+1)
+    log.info("info message %d", a)
+    log.warn("warn message %d", a + 1)
     log.error("error message")
     log.critical("critical message")