Added MPS codes

src/qibotn/cutn.py

@@ -5,68 +5,32 @@ import cuquantum
 from cuquantum import cutensornet as cutn
 import cupy as cp
 import numpy as np
+from qibo.models import QFT
+from QiboCircuitToMPS import QiboCircuitToMPS
+from MPSContractionHelper import MPSContractionHelper
+
 
 def eval(qibo_circ, datatype):
     myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
     return contract(*myconvertor.state_vector_operands())
 
-def eval_mps(qibo_circ, datatype):
-    #Create MPS
-    cutensornet.create()
-    return contract()
-
+def eval_mps(qibo_circ, gate_algo, datatype):
+    myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, datatype)  
+    mps_helper = MPSContractionHelper(myconvertor.num_qubits)
+    sv_mps = mps_helper.contract_state_vector(myconvertor.mps_tensors,myconvertor.options)
+    return sv_mps
 
 if __name__ == "__main__":
-    print("cuTensorNet-vers:", cutn.get_version())
-    dev = cp.cuda.Device()  # get current device
-    props = cp.cuda.runtime.getDeviceProperties(dev.id)
-    print("===== device info ======")
-    print("GPU-name:", props["name"].decode())
-    print("GPU-clock:", props["clockRate"])
-    print("GPU-memoryClock:", props["memoryClockRate"])
-    print("GPU-nSM:", props["multiProcessorCount"])
-    print("GPU-major:", props["major"])
-    print("GPU-minor:", props["minor"])
-    print("========================")
-    
-    data_type = cuquantum.cudaDataType.CUDA_C_64F
-    compute_type = cuquantum.ComputeType.COMPUTE_64F
-    num_sites = 16
-    phys_extent = 2
-    max_virtual_extent = 12
-    
-    ## we initialize the MPS state as a product state |000...000>
-    initial_state = []
-    for i in range(num_sites):
-        # we create dummpy indices for MPS tensors on the boundary for easier bookkeeping
-        # we'll use Fortran layout throughout this example
-        tensor = cp.zeros((1,2,1), dtype=np.complex128, order="F")
-        tensor[0,0,0] = 1.0
-        initial_state.append(tensor)
-    
-    mps_helper = MPSHelper(num_sites, phys_extent, max_virtual_extent, initial_state, data_type, compute_type)
-
-    ##################################
-    # Setup options for gate operation
-    ##################################
-    
-    abs_cutoff = 1e-2
-    rel_cutoff = 1e-2
-    renorm = cutn.TensorSVDNormalization.L2
-    partition = cutn.TensorSVDPartition.UV_EQUAL
-    mps_helper.set_svd_config(abs_cutoff, rel_cutoff, renorm, partition)
-
-    gate_algo = cutn.GateSplitAlgo.REDUCED
-    mps_helper.set_gate_algorithm(gate_algo)
-    
-    #####################################
-    # Workspace estimation and allocation
-    #####################################
-
-    free_mem, total_mem = dev.mem_info
-    worksize = free_mem *.7
-    required_workspace_size = mps_helper.compute_max_workspace_sizes()
-    work = cp.cuda.alloc(worksize)
-    print(f"Maximal workspace size requried: {required_workspace_size / 1024 ** 3:.3f} GB")
-    mps_helper.set_workspace(work, required_workspace_size)
+    num_qubits  = 25
+    swaps = True
+    circ_qibo = QFT(num_qubits, swaps)
     
+    exact_gate_algorithm = {'qr_method': False, 
+                            'svd_method':{'partition': 'UV', 'abs_cutoff':1e-12}}
+    dtype="complex128"
+    sv_mps = eval_mps(circ_qibo, exact_gate_algorithm, dtype)
+    sv_reference = eval(circ_qibo, dtype)
+    state_vec = np.array(circ_qibo())
+    print(f"State vector difference: {abs(sv_mps-sv_reference).max():0.3e}")
+    assert cp.allclose(sv_mps, sv_reference)
+    assert cp.allclose(sv_mps.flatten(), state_vec)