h = 1.0
periodic = False
fix_layers = True
for num_qubits in qubits:
H = tfi_chain(num_qubits, h)
grad_vals = []
for i in range(num_samples):
print(f"num_qubits {num_qubits}, step {i}")
dev = qml.device("default.qubit", wires=num_qubits)
ansatz = mera_circuit(num_qubits, periodic, fix_layers)
cost_fn = qml.ExpvalCost(ansatz, H, dev)
grad = qml.grad(cost_fn)
num_params_per_gate = 15
num_gates = get_num_mera_gates(num_qubits, periodic, fix_layers)
num_params = num_params_per_gate * num_gates
params = np.pi * (np.random.rand(num_params) - 1.0)
gradient = np.array(grad(params)[0])
grad_vals.append(gradient)
variances.append(np.mean(np.var(grad_vals, axis=0)))
print(variances)
#variances = np.array(np.mean(variances, axis=1))
qubits = np.array(qubits)
# Fit the semilog plot to a straight line
p = np.polyfit(qubits, np.log(variances), 1)
# Plot the straight line fit to the semilog
for num_qubits in qubits:
print(f"=== Number of qubits = {num_qubits} ===")
dev = qml.device("default.qubit.autograd", wires=num_qubits)
constant_hea_depth = 3
ansatze = [
ttn_circuit(num_qubits),
mera_circuit(num_qubits),
hea_circuit(num_qubits),
hea_circuit(num_qubits),
hea_circuit(num_qubits)
]
num_params_ansatz = [
num_params_per_gate * get_num_ttn_gates(num_qubits),
num_params_per_gate * get_num_mera_gates(num_qubits),
3 * (num_qubits * constant_hea_depth),
3 * (num_qubits * int(np.floor(np.log2(num_qubits)))),
3 * (num_qubits * num_qubits)
]
ansatz_names = [
"TTN", "MERA", "HEA (constant)", "HEA (log)", "HEA (linear)"
]
for (ansatz, ansatz_name, num_params) in zip(ansatze, ansatz_names,
num_params_ansatz):
print(f" --- {ansatz_name} ({num_params} parameters) ---")
for init in initializations:
print(f" +++ {init} +++")
mod_ansatz = ansatz