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 Grover's algorithm and convert to Qiskit to run on quantum hardware. The algorithm can also be run within skq as a classical simulation.

from skq.circuits import Grover

# Initialize Grover's search skq Circuit
circuit = Grover().circuit(n_qubits=3, target_state=np.array([0, 0, 0, 0, 1, 0, 0, 0]), n_iterations=1)

# Conversion to Qiskit
qiskit_circuit = circuit.convert(framework="qiskit")
qiskit_circuit.draw()
#      ┌───┐┌──────────────┐┌──────────────────┐┌─┐      
# q_0: ┤ H ├┤0             ├┤0                 ├┤M├──────
#      ├───┤│              ││                  │└╥┘┌─┐   
# q_1: ┤ H ├┤1 PhaseOracle ├┤1 GroverDiffusion ├─╫─┤M├───
#      ├───┤│              ││                  │ ║ └╥┘┌─┐
# q_2: ┤ H ├┤2             ├┤2                 ├─╫──╫─┤M├
#      └───┘└──────────────┘└──────────────────┘ ║  ║ └╥┘
# c: 3/══════════════════════════════════════════╩══╩══╩═
#                                                0  1  2 

# Run circuit as classical simulation
print(grover([1,0,0,0,0,0,0,0]))
# array([0.03125, 0.03125, 0.03125, 0.03125, 0.78125, 0.03125, 0.03125, 0.03125])

Circuits from scratch

You can also build your own custom circuits from scratch using individual gates. All gates can be converted to popular frameworks like Qiskit and OpenQASM.

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 (Identity gates are removed)
qiskit_circuit = circuit.convert(framework="qiskit")
qiskit_circuit.draw()
#      ┌───┐     
# q_0: ┤ H ├──■──
#      └───┘┌─┴─┐
# q_1: ─────┤ X ├
#           └───┘

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