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README.md

@@ -2,6 +2,19 @@ Qibotn is the tensor-network translation module for Qibo to support large-scale
 
 To get started, `python setup.py install` to install the tools and dependencies.
 
+# Computation Supported
+
+- Tensornet (TN)
+    - TN contraction to dense vector
+    - TN contraction to dense vector with Message Passing Interface (MPI)
+    - TN contraction to dense vector with NCCL
+    - TN contraction to expectation of given Pauli string
+    - TN contraction to expectation of given Pauli string with Message Passing Interface (MPI)
+    - TN contraction to expectation of given Pauli string with NCCL
+
+- Matrix Product State (MPS)
+    - MPS contraction to dense vector
+
 # Sample Codes
 ## Single Node
 The code below shows an example of how to activate the Cuquantum TensorNetwork backend of Qibo.

src/qibotn/eval.py

@@ -18,7 +18,7 @@ def expectation_pauli_tn(qibo_circ, datatype, pauli_string):
     myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
     return contract(
         *myconvertor.expectation_operands(
-            PauliStringGen(qibo_circ.nqubits, pauli_string)
+            pauli_string_gen(qibo_circ.nqubits, pauli_string)
         )
     )
 
@@ -234,7 +234,7 @@ def expectation_pauli_tn_nccl(qibo_circ, datatype, pauli_string, n_samples=8):
     # mem_avail = cp.cuda.Device().mem_info[0]
     # print("Mem avail: aft convetor",mem_avail, "rank =",rank)
     operands = myconvertor.expectation_operands(
-        PauliStringGen(qibo_circ.nqubits, pauli_string)
+        pauli_string_gen(qibo_circ.nqubits, pauli_string)
     )
 
     # mem_avail = cp.cuda.Device().mem_info[0]
@@ -315,7 +315,7 @@ def expectation_pauli_tn_MPI(qibo_circ, datatype, pauli_string, n_samples=8):
     # mem_avail = cp.cuda.Device().mem_info[0]
     # print("Mem avail: aft convetor",mem_avail, "rank =",rank)
     operands = myconvertor.expectation_operands(
-        PauliStringGen(qibo_circ.nqubits, pauli_string)
+        pauli_string_gen(qibo_circ.nqubits, pauli_string)
     )
     # mem_avail = cp.cuda.Device().mem_info[0]
     # print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
@@ -376,6 +376,8 @@ def expectation_pauli_tn_MPI(qibo_circ, datatype, pauli_string, n_samples=8):
 
 
 def dense_vector_mps(qibo_circ, gate_algo, datatype):
+    """Convert qibo circuit to matrix product state (MPS) format and perform contraction to dense vector.
+    """
     myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype)
     mps_helper = MPSContractionHelper(myconvertor.num_qubits)
 
@@ -384,17 +386,17 @@ def dense_vector_mps(qibo_circ, gate_algo, datatype):
     )
 
 
-def PauliStringGen(nqubits, pauli_string):
+def pauli_string_gen(nqubits, pauli_string_pattern):
+    """ Used internally to generate the string based on given pattern and number of qubit.
+    Example: pattern: "XZ", number of qubit: 7, output = XZXZXZX
+    """
     if nqubits <= 0:
         return "Invalid input. N should be a positive integer."
 
-    characters = pauli_string
-    # characters = "XXXZ"
-
     result = ""
 
     for i in range(nqubits):
-        char_to_add = characters[i % len(characters)]
+        char_to_add = pauli_string_pattern[i % len(pauli_string_pattern)]
         result += char_to_add
     print("pauli string", result)
     return result