I have a parameterized circuit attempting to classify a set of features for various samples. I need to reencode the features per sample, but through the epochs, the computations slow down considerably and after some time, the code throws a mostly nonsensical error regarding the existance of a '1' state on the measured bit. I think the circuit is maintained between runs and that the qubits somehow keep getting added to the circuit. I'd like a clean circuit for each sample, but despite adding an explicit del statement to the circuit, the issue remains.
The code for creating the circle:
def modelCircuit(self, printC=False):#, backend='qasm_simulator', shots=1000):
"""
Set up and run the model with the predefined encoders and ansatzes for the circuit.
"""
self.quantum_register = qk.QuantumRegister(self.n_quantum)
self.classical_register = qk.ClassicalRegister(self.n_classic)
self.circuit = qk.QuantumCircuit(self.quantum_register, self.classical_register)
self.model()
job = qk.execute(self.circuit,
backend=qk.Aer.get_backend(self.backend),
shots=self.shots,
seed_simulator=self.seed
)
results = job.result().get_counts(self.circuit)
self.model_prediction = results['1'] / float(self.shots)
if printC:
print(self.circuit)
del(self.circuit)
return self.model_prediction
where the self.model() is among a set of implemented models. the current one is:
def basicModel(self):
"""
scaling with pi to avoid mapping 0 and 1 to the same rotation.
"""
for i, feature in enumerate(self.feature_vector):
#self.circuit.ry(np.pi*feature, self.quantum_register[i])
self.circuit.rx(np.pi*feature, self.quantum_register[i])
self.circuit.ry(self.theta[i], self.quantum_register[i])
for qubit in range(self.n_quantum - 1):
self.circuit.cx(self.quantum_register[qubit], self.quantum_register[qubit + 1])
self.circuit.ry(self.theta[-1], self.quantum_register[-1])
self.circuit.measure(self.quantum_register[-1], self.classical_register)
The output of the printed circuits are as follows. Note the increasing id's on the qubits.
┌─────────────┐ ┌────────────┐
q0_0: ┤ RX(0.93084) ├─┤ RY(3.8075) ├──■─────────────────────────────
└─┬──────────┬┘ ├────────────┤┌─┴─┐
q0_1: ──┤ RX(5π/8) ├──┤ RY(4.6079) ├┤ X ├──■────────────────────────
┌┴──────────┴┐┌┴────────────┤└───┘┌─┴─┐
q0_2: ─┤ RX(0.3065) ├┤ RY(0.87303) ├─────┤ X ├──■───────────────────
└┬──────────┬┘└┬────────────┤ └───┘┌─┴─┐┌────────────┐┌─┐
q0_3: ──┤ RX(π/17) ├──┤ RY(1.9646) ├──────────┤ X ├┤ RY(4.1605) ├┤M├
└──────────┘ └────────────┘ └───┘└────────────┘└╥┘
c0: 1/════════════════════════════════════════════════════════════╩═
0
0%| | 0/100 [00:00<?, ?it/s] ┌─────────────┐ ┌────────────┐
q7_0: ┤ RX(0.93084) ├─┤ RY(3.8075) ├──■─────────────────────────────
└─┬──────────┬┘ ├────────────┤┌─┴─┐
q7_1: ──┤ RX(5π/8) ├──┤ RY(4.6079) ├┤ X ├──■────────────────────────
┌┴──────────┴┐┌┴────────────┤└───┘┌─┴─┐
q7_2: ─┤ RX(0.3065) ├┤ RY(0.87303) ├─────┤ X ├──■───────────────────
└┬──────────┬┘└┬────────────┤ └───┘┌─┴─┐┌────────────┐┌─┐
q7_3: ──┤ RX(π/17) ├──┤ RY(1.9646) ├──────────┤ X ├┤ RY(4.1605) ├┤M├
└──────────┘ └────────────┘ └───┘└────────────┘└╥┘
c1: 1/════════════════════════════════════════════════════════════╩═
0
1%|█▎ | 1/100 [00:00<00:27, 3.58it/s] ┌──────────┐ ┌────────────┐
q140_0: ─┤ RX(2π/9) ├──┤ RY(3.8075) ├──■─────────────────────────────
┌┴──────────┤ ├────────────┤┌─┴─┐
q140_1: ┤ RX(5π/12) ├──┤ RY(4.6079) ├┤ X ├──■────────────────────────
├───────────┴┐┌┴────────────┤└───┘┌─┴─┐
q140_2: ┤ RX(0.3065) ├┤ RY(0.87303) ├─────┤ X ├──■───────────────────
└┬──────────┬┘└┬────────────┤ └───┘┌─┴─┐┌────────────┐┌─┐
q140_3: ─┤ RX(π/17) ├──┤ RY(1.9646) ├──────────┤ X ├┤ RY(4.1605) ├┤M├
└──────────┘ └────────────┘ └───┘└────────────┘└╥┘
c20: 1/═══════════════════════════════════════════════════════════╩═
0
2%|██▋ | 2/100 [00:00<00:27, 3.61it/s] ┌─────────────┐ ┌────────────┐
q273_0: ┤ RX(0.46542) ├─┤ RY(3.8075) ├──■─────────────────────────────
└─┬─────────┬─┘ ├────────────┤┌─┴─┐
q273_1: ──┤ RX(π/2) ├───┤ RY(4.6079) ├┤ X ├──■────────────────────────
┌─┴─────────┴─┐┌┴────────────┤└───┘┌─┴─┐
q273_2: ┤ RX(0.22987) ├┤ RY(0.87303) ├─────┤ X ├──■───────────────────
└─┬──────────┬┘└┬────────────┤ └───┘┌─┴─┐┌────────────┐┌─┐
q273_3: ──┤ RX(π/17) ├──┤ RY(1.9646) ├──────────┤ X ├┤ RY(4.1605) ├┤M├
└──────────┘ └────────────┘ └───┘└────────────┘└╥┘
c39: 1/════════════════════════════════════════════════════════════╩═
0
If anyone knows how I can just reset the circuit and keep the qubits to 4 through the simulations, I'd appreciate it.
Edit:
attempt at clarifying slow down. Some efforts to try and solve the issue as well as the error taking ~10 hrs to manifest makes finding the error difficult, but the rate of iterations starts at
4.84it/s at the first epoch, with abt. 100 iterations per epoch to
2.89s/it at the 20th. -> roughly 20s on the 1st epoch to ~300 seconds at the 20th, with more slowdown the further you go
the error persists between instantiation of the class as well, something noticed when reducing the number of epochs to run from 100 to 20 in order to try to get some data to work on.
Edit2: We were in talks with TA's regarding the issue, and they suggested naming the registers when initializing them. This sped up the process somewhat. The error thrown was likely a result of the job section, where the output relied on the ratio of '1' outcomes. adding an if test for this solved the error, though not the increase in computation time.
About an hour or so after the conversation, a partner found the code working fine on their windows laptop. mine is still slow on linux. I'll probably try to work out the issue through the week.
Edit3: I've revised the code on my end with removing the registers when creating the circuit. The code starts out quite a bit faster, but the performance deteriorates still. the new functions:
def encoder(self):
"""
mapping features scaled to 1 onto bloch sphere.
scaling with pi to avoid mapping 0 and 1 to the same rotation.
"""
for i, feature in enumerate(self.feature_vector):
self.circuit.rx(np.pi*feature, i)
for qubit in range(self.n_quantum - 1):
self.circuit.cx(qubit, qubit + 1)
def ansatz(self, iteration=0):
"""
Rotating qubit states by parameters theta around bloch sphere
to adjust model for prediction of encoded features
"""
for i in range(self.n_quantum):
self.circuit.ry(self.theta[iteration*self.n_quantum +i], i)
for qubit in range(self.n_quantum - 1):
self.circuit.cx(qubit, qubit + 1)
def measure(self):
"""
measuring the final qubit after applying the final model
parameter as a bias
"""
self.circuit.ry(self.theta[-1], -1)
self.circuit.measure(-1, 0)
def basicModel(self):
self.encoder()
self.ansatz()
self.measure()
for creating the model. the basicModel function is set as self.model during the initialization. Next comes
def modelCircuit(self, printC=False):#, backend='qasm_simulator', shots=1000):
"""
Set up and run the model with the predefined encoders and ansatzes for the circuit.
"""
self.circuit = qk.QuantumCircuit(self.n_quantum, self.n_classic)
self.model()
job = qk.execute(self.circuit,
backend=qk.Aer.get_backend(self.backend),
shots=self.shots,
seed_simulator=self.seed
)
results = job.result().get_counts(self.circuit)
counts = 0
for key, value in results.items():
if key=='1':
counts += value
self.model_prediction = counts / float(self.shots)
return self.model_prediction
the modelCircuit is called int the train function
def train(self, target, epochs=100, learning_rate=.1, debug=False):
"""
Uses the initial quess for an ansatz for the model to train and optimise the model ansatz for
the given cost/loss function.
"""
from tqdm import tqdm
mean_loss = np.zeros(epochs)
accuracy = np.zeros_like(mean_loss)
for epoch in range(epochs):
# setup of storage arrays
thetaShift = np.zeros([self.n_samples,len(self.theta)])
loss = np.ones(self.n_samples)
lossDerivative = np.zeros_like(loss)
acc = 0
for sample in tqdm(range(self.n_samples)):
#self.feature_vector = self.featureMatrix.iloc[sample]
self.feature_vector = self.featureMatrix[sample, :]
out = self.modelCircuit()
acc += np.round(out)==target[sample]
loss[sample] = self.lossFunction(out, target[sample])
lossDerivative[sample] = self.lossDerivative(out, target[sample])
theta_gradient = np.zeros_like(self.theta)
for i in range(self.n_model_parameters):
self.theta[i] += np.pi / 2
out_1 = self.modelCircuit()
self.theta[i] -= np.pi
out_2 = self.modelCircuit()
self.theta[i] += np.pi / 2
theta_gradient[i] = (out_1 - out_2) / 2
if debug:
print(f'output 1: {out_1}')
print(f'output 2: {out_2}')
#thetaShift[sample, i] = - learning_rate * theta_gradient * np.mean(lossDerivative) # theta gradient for vairable shift.
thetaShift[sample, i] = theta_gradient[i]
accuracy[epoch] = float(acc)/self.n_samples
mean_loss[epoch] = np.mean(loss)
self.theta -= learning_rate * np.mean((thetaShift * lossDerivative.reshape(-1,1)), axis=0)
print("mean loss per epoch: ", mean_loss[epoch])
print("accuracy per epoch: ", accuracy[epoch])
return self.theta, mean_loss, accuracy
printouts for the base model to terminal:
┌─────────────┐ ┌────────────┐
q_0: ┤ RX(0.93084) ├──■──┤ RY(2.3533) ├────────────────────■──────────────────────────────────
└─┬──────────┬┘┌─┴─┐└────────────┘┌────────────┐ ┌─┴─┐
q_1: ──┤ RX(5π/8) ├─┤ X ├──────■───────┤ RY(5.9735) ├────┤ X ├───────■────────────────────────
┌┴──────────┴┐└───┘ ┌─┴─┐ └────────────┘┌───┴───┴────┐┌─┴─┐
q_2: ─┤ RX(0.3065) ├─────────┤ X ├───────────■───────┤ RY(4.5993) ├┤ X ├──■───────────────────
└┬──────────┬┘ └───┘ ┌─┴─┐ ├────────────┤└───┘┌─┴─┐┌────────────┐┌─┐
q_3: ──┤ RX(π/17) ├────────────────────────┤ X ├─────┤ RY(3.7615) ├─────┤ X ├┤ RY(3.7769) ├┤M├
└──────────┘ └───┘ └────────────┘ └───┘└────────────┘└╥┘
c: 1/═══════════════════════════════════════════════════════════════════════════════════════╩═
0
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:13<00:00, 7.28it/s]
mean loss per epoch: 0.6493203309690068
accuracy per epoch: 0.78
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:27<00:00, 3.59it/s]
mean loss per epoch: 0.6415613508592234
accuracy per epoch: 0.8
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:42<00:00, 2.38it/s]
mean loss per epoch: 0.6337758611894215
accuracy per epoch: 0.8
over a few epochs. more directly, the iterations per second are
7.28it/s 3.59it/s 2.38it/s
Edit 4:
I tested yesterday with the results as above. Today I wanted to check for memory leaks with the suggested tracemalloc.
The program runs fine now. I don't know what happened, but I did do a system update and follow the recomendation bellow in regards to removing the registers. I made no further changes than those in the example above. The issue seems fixed, but I don't know why.
