def evaluate_4TTN(params, training_data, ancilla, total, rounding):
answers = np.zeros(training_data.shape[0])
L = training_data.shape[1] - 1
for i in range(training_data.shape[0]):
"""FOR EACH DATA POINT"""
"""Lets Encode the data elements first"""
psi = initial_encode(training_data[i, :L], ancilla)
"""Stage 1: Unitaries on all of the qubits, ancilla or not"""
for j in range(total):
psi = np.matmul(ry(params[j], j, total), psi)
"""Stage 2: CNOT plus unitary for N-1 times (cascading)"""
j = 0
psi = np.matmul(cnot(j, j + 1, total), psi)
psi = np.matmul(ry(params[total], j + 1, total), psi)
j = 3
psi = np.matmul(cnot(j, j - 1, total), psi)
psi = np.matmul(ry(params[total + 1], j - 1, total), psi)
j = 1
psi = np.matmul(cnot(j, j + 1, total), psi)
psi = np.matmul(ry(params[total + 2], j + 1, total), psi)
"""Stage 3: Trace and Measure"""
zero_prob = prob_zero(keep_n(psi, total, 2))
"""Stage 4: Calculate Cost"""
answers[i] = eval_cost(zero_prob, training_data[i, L], rounding)
total_cost = np.sum(answers) / training_data.shape[0]
return total_cost
def evaluate_MPS(params, training_data, ancilla, total, rounding):
""" UNITARIES ON ALL FOLLOWED BY CASCADE OF CNOT(a,b) and unitary on b """
answers = np.zeros(training_data.shape[0])
L = training_data.shape[1] - 1
for i in range(training_data.shape[0]):
"""FOR EACH DATA POINT"""
"""Lets Encode the data elements first"""
psi = np.real(initial_encode2(training_data[i, :L], ancilla))
psi = np.reshape(psi, (2**total, 1))
"""Stage 1: Unitaries on all of the qubits, ancilla or not"""
for j in range(total):
psi = np.matmul(ry(params[j], j, total), psi)
"""Stage 2: CNOT plus unitary for N-1 times (cascading)"""
for j in range(total - 1):
psi = np.matmul(cnot(j, j + 1, total), psi)
psi = np.matmul(ry(params[total + j], j + 1, total), psi)
"""Stage 3: Trace and Measure"""
zero_prob = prob_zero(keep_last(psi, total))
"""Stage 4: Calculate Cost"""
answers[i] = eval_cost(zero_prob, training_data[i, L], rounding)
total_cost = np.sum(answers) / training_data.shape[0]
return total_cost
def evaluate_MPS_yz(params, training_data, ancilla, total, rounding):
""" AS MPS BUT INITIAL UNITARIES ARE ALL RZ,RY,RZ FORM """
answers = np.zeros(training_data.shape[0])
L = training_data.shape[1] - 1
lst = list(range(total - 1))
for i in range(training_data.shape[0]):
"""FOR EACH DATA POINT"""
"""Lets Encode the data elements first"""
psi = np.real(initial_encode2(training_data[i, :L], ancilla))
psi = np.reshape(psi, (2**total, 1))
"""Stage 1: Unitaries on all of the qubits, ancilla or not"""
for j in range(total):
psi = np.matmul(rz(params[j], j, total), psi)
for j in range(total):
psi = np.matmul(ry(params[j + total], j, total), psi)
for j in range(total):
psi = np.matmul(rz(params[j + (total * 2)], j, total), psi)
"""Stage 2: CNOT plus unitary for N-1 times (cascading)"""
for j in range(total - 1):
psi = np.matmul(cnot(j, j + 1, total), psi)
psi = np.matmul(ry(params[total * 3 + j], j + 1, total), psi)
"""Stage 3: Trace and Measure"""
dm = np.kron(np.transpose(np.conjugate(psi)), psi)
dm = np.reshape(dm, (2**total, 2**total))
dm = partial_trace(dm, lst)
# print(dm)
"""Stage 3: Trace and Measure"""
zero_prob = prob_zero(dm)
"""Stage 4: Calculate Cost"""
answers[i] = eval_cost(zero_prob, training_data[i, L], rounding)
total_cost = np.sum(answers) / training_data.shape[0]
return total_cost
def evaluate_MPS_plotter(params, training_data, ancilla, total, rounding):
guesses = np.zeros(training_data.shape[0])
L = training_data.shape[1] - 1
for i in range(training_data.shape[0]):
"""FOR EACH DATA POINT"""
"""Lets Encode the data elements first"""
psi = initial_encode(training_data[i, :L], ancilla)
"""Stage 1: Unitaries on all of the qubits, ancilla or not"""
for j in range(total):
psi = np.matmul(ry(params[j], j, total), psi)
"""Stage 2: CNOT plus unitary for N-1 times (cascading)"""
for j in range(total - 1):
psi = np.matmul(cnot(j, j + 1, total), psi)
psi = np.matmul(ry(params[total + j], j + 1, total), psi)
"""Stage 3: Trace and Measure"""
zero_prob = prob_zero(keep_last(psi, total))
"""Stage 4: Calculate Cost"""
guesses[i] = 1 - round(zero_prob)
return guesses
def evaluate_MPS_xyz(params, training_data, ancilla, total, rounding):
""" LIKE MPS BUT EACH SINGLE UNITARY IS A DIFFERENT TYPE"""
answers = np.zeros(training_data.shape[0])
L = training_data.shape[1] - 1
for i in range(training_data.shape[0]):
"""FOR EACH DATA POINT"""
"""Lets Encode the data elements first"""
psi = np.real(initial_encode2(training_data[i, :L], ancilla))
psi = np.reshape(psi, (2**total, 1))
for j in range(total - 1):
psi = np.matmul(ry(params[3 * j], j, total), psi)
psi = np.matmul(ry(params[3 * j + 1], j + 1, total), psi)
psi = np.matmul(cnot(j, j + 1, total), psi)
psi = np.matmul(rz(params[3 * j + 2], j + 1, total), psi)
"""Stage 3: Trace and Measure"""
zero_prob = prob_zero(keep_last(psi, total))
"""Stage 4: Calculate Cost"""
answers[i] = eval_cost(zero_prob, training_data[i, L], rounding)
total_cost = np.sum(answers) / training_data.shape[0]
return total_cost