Quantum circuit simplification is an essential part of quantum computing. While designing quantum algorithms, due to the sensitivity and resource-intensive nature of quantum operations, we most certainly strive for the minimum number of quantum gate operations. Having fewer operations generally reduces computation time and lessens the likelihood of quantum decoherence. In this blog post, we will be discussing how to simplify quantum circuits using IBM’s Qiskit, an open-source, Python-based library for quantum computing.
The following command installs Qiskit. Please make sure to have Python 3.5 or later installed and running.
pip install qiskit
Using Qiskit we can create and simulate quantum circuits. Let's construct a simple quantum circuit.
import qiskit as qk # Create a Quantum Register with 2 qubits. qr = qk.QuantumRegister(2) # Create a Classical Register with 2 bits. cr = qk.ClassicalRegister(2) # Create a Quantum Circuit acting on the q register circuit = qk.QuantumCircuit(qr, cr) # Add a H gate on qubit 0 circuit.h(qr[0]) # Add a CX (CNOT) gate on control qubit 0 and target qubit 1 circuit.cx(qr[0], qr[1]) # Map the quantum measurement to the classical bits circuit.measure(qr, cr) print(circuit)
The above circuit is already pretty simplified. But, in many real-world problems we deal with more complicated circuits. The transpiler of IBM's Qiskit library includes several quantum circuit optimization levels. We can use these to automatically simplify quantum circuits.
from qiskit.compiler import transpile # Define your backend backend = qk.BasicAer.get_backend('qasm_simulator') # Transpile the circuit using the highest optimization level optimized_circuit = transpile(circuit, backend=backend, optimization_level=3) print(optimized_circuit)
In the code above, we use the highest optimization_level (3) to simplify the circuit. The optimization levels vary between 0 (no optimization) to 3 (heavy optimization). Depending on the context, one operation may have equivalent, but shorter implementations. The Qiskit transpiler takes care to replace these and minimize quantum gate usage, thus simplifying the given quantum circuit.
Optimizing and simplifying quantum circuits is crucial for better performance and successful implementation of quantum algorithms. It is an ongoing challenge in quantum computing. Using utilities like Qiskit’s transpiler, quantum developers can wrangle quantum circuits to achieve more efficient results.
Note: The mentioned optimizations do not always guarantee a more efficient solution and can sometimes lead to more quantum gate usage depending on the context. Therefore, manual optimizations are encouraged where possible.