# Deeq
Quantum Computing SDK
### Version
[](https://badge.fury.io/py/deeq)
### Tutorial
https://github.com/Deeq/Deeq-tutorials
### Notice
The back end has been changed to tensor network. The previous backend environment can still be used with .run(backend="numpy").
### Install
```
git clone https://github.com/Deeq/Deeq
cd Deeq
pip3 install -e .
```
or
```
pip3 install deeq
```
### Circuit
```python
from deeq import Circuit
import math
c = Circuit()
c = Circuit(50)
```
### Method Chain
```python
Circuit().h[0].x[0].z[0]
c = Circuit().h[0]
c.x[0].z[0]
```
### Slice
```python
Circuit().z[1:3] # Zgate on 1,2
Circuit().x[:3] # Xgate on (0, 1, 2)
Circuit().h[:] # Hgate on all qubits
Circuit().x[1, 2] # 1qubit gate with comma
```
### Rotation Gate
```python
Circuit().rz(math.pi / 4)[0]
```
### Run
```python
from deeq import Circuit
Circuit(50).h[:].run()
```
### Run(shots=n)
```python
Circuit(100).x[:].run(shots=100)
# => Counter({'1111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111': 100})
```
### Single Amplitude
```python
Circuit(4).h[:].run(amplitude="0101")
```
### Expectation value of hamiltonian
```python
from deeq.pauli import Z
hamiltonian = 1*Z[0]+1*Z[1]
Circuit(4).x[:].run(hamiltonian=hamiltonian)
# => -2.0
```
### Deeq to QASM
```python
Circuit().h[0].to_qasm()
```
### Hamiltonian
```python
from deeq.pauli import *
hamiltonian1 = (1.23 * Z[0] + 4.56 * X[1] * Z[2]) ** 2
hamiltonian2 = (2.46 * Y[0] + 5.55 * Z[1] * X[2] * X[1]) ** 2
hamiltonian = hamiltonian1 + hamiltonian2
print(hamiltonian)
```
### Simplify the Hamiltonian
```python
hamiltonian = hamiltonian.simplify()
print(hamiltonian)
```
### QUBO Hamiltonian
```python
from deeq.pauli import qubo_bit as q
hamiltonian = -3*q(0)-3*q(1)-3*q(2)-3*q(3)-3*q(4)+2*q(0)*q(1)+2*q(0)*q(2)+2*q(0)*q(3)+2*q(0)*q(4)
print(hamiltonian)
```
### Time Evolution
```python
hamiltonian = [1.0*Z(0), 1.0*X[0]]
a = [term.get_time_evolution() for term in hamiltonian]
time_evolution = Circuit().h[0]
for evo in a:
evo(time_evolution, np.random.rand())
print(time_evolution)
```
### QAOA
```python
from deeq import Circuit
from deeq.utils import qaoa
from deeq.pauli import qubo_bit as q
from deeq.pauli import X,Y,Z,I
hamiltonian = q(0)-q(1)
step = 1
result = qaoa(hamiltonian, step)
result.circuit.run(shots=100)
```
### Circuit Drawing Backend
```python
from deeq import vqe
from deeq.pauli import *
from deeq.pauli import qubo_bit as q
hamiltonian = Z[0]-3*Z[1]+2*Z[0]*Z[1]+3*Z[2]*Z[3]+Z[4]
step = 8
result = vqe.Vqe(vqe.QaoaAnsatz(hamiltonian, step)).run()
result.circuit.run(backend='draw')
```
### Document
https://deeq.readthedocs.io/en/latest/
### Contributors
[Contributors](https://github.com/Deeq/Deeq/graphs/contributors)
### Disclaimer
Copyright 2022 The DeepRecommend Developers.
# Deeq
