> ## Documentation Index
> Fetch the complete documentation index at: https://docs.haiqu.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Vector loading

haiqu.vector\_loading()

Use `haiqu.vector_loading()` to generate an arbitrary quantum state from a vector of amplitudes.

For a 12-qubit sin wave, traditional amplitude encoding requires 4083 CNOT gates and 4083 two-qubit gate depth. Haiqu's method prepares the same state with 21 CNOT gates (194.4x improvement) and 11 two-qubit gate depth (371.2x improvement).

haiqu.vector\_loading()

What does it do? Vector loading prepares a quantum state, which amplitudes match a given data vector. How do I use it? Pass a real or complex vector to create a data loading job, then retrieve results with job.result(). What are the options? Optional parameters include various hyperparameters for circuit synthesis such as num\_layers, truncation\_cutoff and fine\_tuning\_iterations. Which option do you recommend? Start with the default settings for basic use cases. Observe the state fidelity returned by the job result and increase number of layers and fine tuning iterations if necessary to achieve a better precision.

Having a bug or an issue?

Submit feedback

Initialize the benchmark

Import the necessary libraries, initialize the Haiqu SDK, create a desired quantum state.

```python theme={null}
import qiskit
import numpy as np
import pandas as pd
from haiqu.sdk import haiqu
import matplotlib.pyplot as plt
from qiskit.circuit.library import StatePreparation

haiqu.login()
haiqu.init("Vector Loading Tutorial")

# we load a sin wave on 12 qubits
num_qubits = 12
interval = np.linspace(0, 10, 2**num_qubits)
data = np.sin(interval)

plt.plot(interval, data)
plt.title("Data vector to encode")
```

Run benchmark scenarios

Prepare a quantum state with Qiskit's default and Haiqu's methods

```python theme={null}
# Scenario 1: standard method
circuit_qiskit = qiskit.QuantumCircuit(num_qubits, name="Qiskit")
circuit_qiskit.compose(StatePreparation(data, normalize=True), inplace=True)

# Scenario 2: Haiqu vector loading
circuit_haiqu = qiskit.QuantumCircuit(num_qubits, name="Haiqu")
state_gate, fidelity = haiqu.vector_loading(data=data).result()
circuit_haiqu.compose(state_gate, inplace=True)
print(f"Haiqu state is loaded with fidelity: {fidelity:.3f}")
```

Haiqu's vector loading significantly outperforms standard methods as shown in the comparison table below:

```python theme={null}
# we consider an ideal device with all-to-all connectivity
device_ideal = haiqu.get_device("aer_simulator")

circuit_qiskit_transpiled = haiqu.transpile(circuit_qiskit, device=device_ideal)

circuit_haiqu_transpiled = haiqu.transpile(circuit_haiqu, device=device_ideal)

haiqu.compare_metrics(circuit_qiskit_transpiled, circuit_haiqu_transpiled)
```

💡 Good to Know: Vector loading method is approximate for an arbitrary quantum state, it performs better for more smooth input data vector.

Get in Touch

Documentation portal [docs.haiqu.ai](https://docs.haiqu.ai)

Contact Support [feedback.haiqu.ai](https://feedback.haiqu.ai)

Follow Us on LinkedIn latest news on [LinkedIn](https://www.linkedin.com/company/haiquai/)

Visit Our Website Learn more about Haiqu Inc. on [haiqu.ai](https://haiqu.ai)

Business Inquiries Contact us at [info@haiqu.ai](mailto:info@haiqu.ai)