skq 0.3.2

Scientific Toolkit for Quantum Computing

skq

Scientific Toolkit for Quantum Computing

This library is used in the q4p (Quantum Computing for Programmers) course.

NOTE: This library is developed for educational purposes. While we strive for correctness of everything, the code is provided as is and not guaranteed to be bug-free. For sensitive applications make sure you check computations.

Why SKQ?

• Exploration: Play with fundamental quantum building blocks using NumPy.
• Education: Learn quantum computing concepts and algorithms.
• Integration: Combine classical components with quantum components.
• Democratize quantum for Python programmers and data scientists: Develop quantum algorithms in your favorite environment and easily export to your favorite quantum computing platform for running on real quantum hardware.

Install

pip install -U skq

Quickstart

Circuit Conversion

Run this code snippet to initialize a Bell State and convert to Qiskit and OpenQASM.

from skq.circuits.entangled_states import BellStates

# Initialize Bell State skq Circuit
circuit = BellStates().get_bell_state(1)

# Conversion to Qiskit
qiskit_circuit = circuit.convert(framework="qiskit")
qiskit_circuit.draw()
#      ┌───┐     
# q_0: ┤ H ├──■──
#      ├───┤┌─┴─┐
# q_1: ┤ I ├┤ X ├
#      └───┘└───┘

# Conversion to OpenQASM
qasm_circuit = circuit.convert(framework="qasm")
print(qasm_circuit)
# h q[0];
# id q[1];
# cx q[0], q[1];

Circuits from scratch

You can also build your own custom circuits from scratch using individual gates.

from skq.gates import H, I, CX
from skq.circuits import Concat, Circuit

H() # Hadamard gate (NumPy array)
# H([[ 0.70710678+0.j,  0.70710678+0.j],
#    [ 0.70710678+0.j, -0.70710678+0.j]])

I() # Identity gate (NumPy array)
# I([[1.+0.j, 0.+0.j],
#    [0.+0.j, 1.+0.j]])

CX() # CNOT gate (NumPy array)
# CX([[1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
#     [0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j],
#     [0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j],
#     [0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j]])

# Initialize Bell State skq Circuit
circuit = Circuit([Concat([H(), I()]), CX()])

# Simulate circuit classically
state = np.array([1, 0, 0, 0]) # |00> state
circuit(state)
# array([0.70710678+0.j, 0, 0, 0.70710678+0.j])

# Conversion to Qiskit
qiskit_circuit = circuit.convert(framework="qiskit")
qiskit_circuit.draw()
#      ┌───┐     
# q_0: ┤ H ├──■──
#      ├───┤┌─┴─┐
# q_1: ┤ I ├┤ X ├
#      └───┘└───┘