reorganize

docs/transition.md

@@ -1,10 +1,55 @@
 
-## Transition Consideration
+# Transition Consideration
 This document is for Qadence users to use Qadence2 funcationalities with Qadence format.
 The examples in Qadence Contents and Tutorials will be presented with qadence2.expressions.legacy package. The provided Operators and functions are states as below
 For Qadence2 code details, please refer to https://github.com/pasqal-io/qadence2-core.
 
-### Operators
+### Features provided in both Qadence1 & Qadence2
+
+- quantum gates & custom gates
+- circuit block composition
+- quantum registers (few options are not includes (ex. circle, honeycomb))
+
+### Features need to implement
+- QNN
+- importing graph as input of registers
+- <span style="color: red;">state initialization</span>
+- random_state, is_normalized, product_state, product_block
+- uniform_state, zero_state, one_state, rand_product_state, ghz_state
+- block initialization
+- uniform_block, one_block, product_block, rand_product_block, ghz_block
+- <span style="color: red;">hamiltonian factory</span>
+- <span style="color: red;">time dependent generators</span>
+- feature map
+- <span style="color: red;">hardware efficient ansatz (hea)</span>
+- identity_initialized_ansatz
+- <span style="color: red;">wave function overlap</span>
+- AnalogRot, AnalogRX, AnalogRY, AnalogRZ, AnalogInteraction
+- RydbergDevice
+- rydberg_hea, rydberg_hea_layer
+- AddressingPattern
+- daqc_transform
+- Trainer
+- Projector
+- CUDA
+- submission to pascal cloud
+- Measurements, classical shadow
+- NoiseHandler, NoiseProtocol
+- set_noise
+- error mitigation
+
+### Features only provided in Qadence1
+
+- visualizing quantum circuits
+- tensor representation
+- arbitary hamiltonian evolution
+- `Feature Parameter`
+- density matrix
+- equivalent_state
+
+
+
+## Operators
 
 Qadence
 ```python exec="on" source="material-block" html="1" session="getting_started"
@@ -22,7 +67,7 @@ rx = RX(0, 0.5)
 cnot = CNOT(0, 1)
 ```
 
-### Block System
+## Block System
 
 Qadence
 ```python exec="on" source="material-block" html="1" session="getting_started"
@@ -44,7 +89,7 @@ chain_1 = chain(X(1), Y(1))
 kron_block = kron(chain_0, chain_1)
 ```
 
-### Compose Functions
+## Compose Functions
 
 Qadence
 ```python exec="on" source="material-block" html="1" session="getting_started"
@@ -70,7 +115,7 @@ n_qubits = 3
 xy_ham = add(xy_int(i, i+1) for i in range(n_qubits-1))
 ```
 
-### Quantum Fourier Transform Example
+## Quantum Fourier Transform Example
 
 Qadence
 ```python exec="on" source="material-block" html="1" session="getting_started"
@@ -179,28 +224,87 @@ wf_2 = compiled_model_2.run()
 - run/sample/expectation without compilation to model & backend
 - torch.Tensor & numpy.array
 
-## Next session
+## Quantum Models
 
+Qadence2 uses `compile_to_model` and `compile_to_backend` to make the expression ready to execute with `run`, `sample`, and `expectation`.
 Qadence
 ```python exec="on" source="material-block" html="1" session="getting_started"
+import torch
+from qadence import QuantumModel, PI, Z
+from qadence import QuantumCircuit, RX, RY, chain, kron
+from qadence import FeatureParameter, VariationalParameter
+
+phi = FeatureParameter("phi")
+
+block = chain(
+    kron(RX(0, phi), RY(1, phi)),
+)
+
+circuit = QuantumCircuit(2, block)
+
+observable = Z(0) + Z(1)
 
+model = QuantumModel(circuit, observable)
+
+values = {"phi": torch.tensor(PI)}
+
+wf = model.run(values)
+xs = model.sample(values, n_shots=100)
+ex = model.expectation(values)
 ```
 
 Qadence2
 ```python exec="on" source="material-block" html="1" session="getting_started"
+import torch
+from qadence2.extensions.legacy import PI, Z, RX, RY, chain, kron
+from qadence2_expressions import compile_to_model, parameter
+from qadence2_platforms.compiler import compile_to_backend
+
+phi = parameter("phi")
 
+block = chain(
+    kron(RX(0, phi), RY(1, phi)),
+)
+
+model = compile_to_model(block)
+compiled_model = compile_to_backend(model, "pyqtorch")
+
+observable = Z(0) + Z(1)
+
+values = {"phi": torch.tensor(PI)}
+
+wf = compiled_model.run(values)
+xs = compiled_model.sample(values, shots=100)
+ex = compiled_model.expectation(values, observable=observable)
 ```
 
-## Next session
+## Quantum Registers
+Qadence2 can represent the relationships between logical qubits using `grid_type`, `grid_scale`, and `qubit_position`. The `connectivity` in `qadence2_ir` is for accurately representing the connectivity between qubits.
+
 
 Qadence
 ```python exec="on" source="material-block" html="1" session="getting_started"
+from qadence import Register
 
+reg = Register.all_to_all(n_qubits = 4)
+reg_line = Register.line(n_qubits = 4)
+reg_squre = Register.square(qubits_side = 2)
+reg_triang = Register.triangular_lattice(n_cells_row = 2, n_cells_col = 2)
 ```
 
 Qadence2
 ```python exec="on" source="material-block" html="1" session="getting_started"
+from qadence2_expressions.core import set_grid_type, set_qubits_positions, set_grid_scale
+from qadence2.extensions.legacy import PI, RX, RY, CZ
+from qadence2_expressions import compile_to_model
+
+expr = RX(2, PI / 2) * CZ() * RY(0, PI / 2)
+
+set_grid_type("linear")  # "square", "triangular"
+set_qubits_positions([(-2, 1), (0, 0), (3, 1)])
+set_grid_scale(0.4)
 
+compile_to_model(expr)
 ```
 
 ## Next session