update v0.2.0, readme & tutorials

quairkit/__init__.py

@@ -47,39 +47,151 @@
     cd QuAIRKit
     pip install -e .
 
-Functionality
--------------
+Batch computation
+-----------------
+
+QuAIRKit supports batch computations for quantum circuit simulations, state measurement and quantum information processing. It is easy to use and can be customized for different quantum (machine learning) algorithms.
+
+Below is an example of batch computation for quantum circuit simulation. Here a zero state is passed through four different quantum circuits, and compared with the target state.
+
+```python
+import quairkit as qkit
+from quairkit.database import *
+from quairkit.qinfo import *
+
+target_state = zero_state(1)
+unitary_data = pauli_group(1)
+
+cir = qkit.Circuit(1)
+cir.oracle(unitary_data, 0)
+cir.ry(param=[0, 1, 2, 3])
+
+print(state_fidelity(cir(), target_state)) # zero-state input by default
+```
+
+```text
+tensor([1.0000, 0.4794, 0.8415, 0.0707])
+```
+
+Qudit computation
+-----------------
+
+QuAIRKit also supports batch computations for quantum circuit simulations and most of the quantum information processing tools in qudit quantum computing. Note that qudit computation can be used with batch computation, as shown below
+
+```python
+# claim three systems, with 1 qubit and 1 qutrit
+cir = qkit.Circuit(2, system_dim=[2, 3])
+
+# apply the Heisenberg-Weyl operators on all systems
+cir.oracle(heisenberg_weyl(6), [0, 1])
+
+# apply the H gate on the first system, controlled by the second system
+cir.control_oracle(h(), [1, 0])
+
+# trace out the qutrit system and get the qubit state
+traced_state = cir().trace(1)
+
+print('The 6th and 7th state for the batched qubit state is', traced_state[5:7])
+```
+
+```text
+The 6th and 7th state for the batched qubit state is 
+---------------------------------------------------
+ Backend: density_matrix
+ System dimension: [2]
+ System sequence: [0]
+ Batch size: [2]
+
+ # 0:
+[[1.+0.j 0.+0.j]
+ [0.+0.j 0.+0.j]]
+ # 1:
+[[0.5+0.j 0.5+0.j]
+ [0.5+0.j 0.5+0.j]]
+---------------------------------------------------
+```
+
+Fast construction
+-----------------
+
+QuAIRKit provides a fast and flexible way to construct quantum circuits, by self-managing the parameters. All parameters would be created randomly if not specified. QuAIRKit also supports built-in layer ansatzes, such as `complex_entangled_layer`.
+
+```python
+cir = qkit.Circuit(3)
+
+cir.h() # apply Hadamard gate on all qubits
+cir.complex_entangled_layer(depth=3) # apply complex entangled layers of depth 3
+cir.universal_three_qubits() # apply universal three-qubit gate with random parameters
+```
+
+`qkit.Circuit` is a child class of `torch.nn.Module`, so you can access its parameters and other attributes directly, or use it as a layer in a hybrid neural network.
+
+Implicit transition
+-------------------
+
+If you want to perform noise simulation or mixed-state-related tools, there is no need to specify the backend, or import other libraries. Just call the function, and QuAIRKit will transit the backend for you.
+
+```python
+cir = qkit.Circuit(3)
+
+cir.complex_entangled_layer(depth=3)
+print(cir().backend)
+
+# partial transpose on the first two qubits
+print(cir().transpose([0, 1]).backend)
+
+cir.depolarizing(prob=0.1)
+print(cir().backend)
+```
+
+```text
+state_vector
+density_matrix
+density_matrix
+```
+
+Global setup
+------------
+
+QuAIRKit provides global setup functions to set the default data type, device and random seed.
+
+```python
+qkit.set_dtype('complex128') # default data type is complex64
+qkit.set_device('cuda') # make sure CUDA is setup with torch
+qkit.set_seed(73) # set seeds for all random number generators
+```
+
+Overall Structure
+-----------------
+
+QuAIRKit provides the following functionalities,
 
 - Quantum neural network algorithm simulation
 - Quantum circuit simulation & visualization
 - Quantum channel simulation
 - Quantum algorithm/information tools
 
-Modules
--------
-
-``quairkit``: QuAIRKit source code
+`quairkit`: QuAIRKit source code
 
-- ``ansatz``: module of circuit templates
-- ``database``: module of useful matrices & sets
-- ``operator``: module of quantum operators
-- ``qinfo``: library of quantum algorithms & information tools
-- ``circuit``: quantum circuit interface
+- `database`: module of useful matrices & sets
+- `loss`: module of quantum loss functions
+- `qinfo`: library of quantum algorithms & information tools
+- `circuit`: quantum circuit interface
 
 Tutorials
 ---------
 
-Check out the tutorial folder on `GitHub <https://github.com/QuAIR/QuAIRKit>`_ for more information.
-
-Relations with Paddle Quantum
------------------------------
+- `Hamiltonian in QuAIRKit <https://github.com/QuAIR/QuAIRKit/tree/v0.2.0/tutorials/introduction/Hamiltonian.ipynb>`_
+- `Constructing Quantum Circuits in QuAIRKit <https://github.com/QuAIR/QuAIRKit/tree/v0.2.0/tutorials/introduction/circuit.ipynb>`_
+- `Measuring quantum states in QuAIRKit <https://github.com/QuAIR/QuAIRKit/tree/v0.2.0/tutorials/introduction/measure.ipynb>`_
+- `Quantum gates and quantum channels <https://github.com/QuAIR/QuAIRKit/tree/v0.2.0/tutorials/introduction/operator.ipynb>`_
+- `Quantum information tools <https://github.com/QuAIR/QuAIRKit/tree/v0.2.0/tutorials/introduction/qinfo.ipynb>`_
+- `Manipulation of Quantum States in QuAIRKit <https://github.com/QuAIR/QuAIRKit/tree/v0.2.0/tutorials/introduction/state.ipynb>`_
+- `Training parameterized quantum circuits <https://github.com/QuAIR/QuAIRKit/tree/v0.2.0/tutorials/introduction/training.ipynb>`_
+- `Batch Computation <https://github.com/QuAIR/QuAIRKit/tree/v0.2.0/tutorials/feature/batch.ipynb>`_
+- `Neural network setup customization <https://github.com/QuAIR/QuAIRKit/tree/v0.2.0/tutorials/feature/custom.ipynb>`_
+- `Introduction to qudit quantum computing <https://github.com/QuAIR/QuAIRKit/tree/v0.2.0/tutorials/feature/qudit.ipynb>`_
 
-`Paddle Quantum <https://github.com/PaddlePaddle/Quantum>`_ is the world's first cloud-integrated
-quantum machine learning platform based on Baidu PaddlePaddle. As most contributors to this project
-are also contributors to Paddle Quantum, QuAIRKit incorporates key architectural elements and
-interface designs from its predecessor. QuAIRKit focuses more on providing specialized tools and
-resources for researchers and developers engaged in cutting-edge quantum algorithm design and
-theoretical explorations in quantum information science.
 """
 
 import os
@@ -103,10 +215,10 @@ def print_info() -> None:
     r"""Print the information of QuAIRKit, its dependencies and current environment.
 
     """
-    import torch
+    import matplotlib
     import numpy
     import scipy
-    import matplotlib
+    import torch
     print("\n---------VERSION---------")
     print("quairkit:", __version__)
     print("torch:", torch.__version__)
@@ -123,8 +235,9 @@ def print_info() -> None:
     print("OS version:", platform.version())
 
 
-    import subprocess
     import re
+    import subprocess
+
     # stack overflow #4842448
     print("---------DEVICE---------")
     if platform.system() == "Windows":