Optimizing quantum circuit using boolean algebra
Since the latest v0.1.18 version, the Qlasskit library offers two useful tool for circuit analysis and optimization.
- Decompiler: given a quantum circuit is able to detect section that can be represented as boolean expressions
- circuit_boolean_optimizer: a pipeline that given a quantum circuit, decompose it in boolean expressions form and optimize it using boolean algebra
Let’s have a look on how to use these features. We first create the following quantum circuit:
Now, using the decompile function we can detect “classical parts” and decode them as boolean expression; this is the result for the previous quantum circuit.
dc = Decompiler().decompile(qcircuit)DecompiledResults[
(
(0, 7)
(X, [2], None), (CCX, [0, 2, 3], None), (CCX, [1, 3, 4], None), (CX, [4, 5], None), (CCX, [1, 3, 4], None), (CCX, [0, 2, 3], None), (X, [2], None)
(q5, q4 ^ q5 ^ (q1 & (q3 ^ (q0 & ~q2))))
)
]Reading the resulting boolean expression (q5, q4 ^ q5 ^ (q1 & (q3 ^ (q0 & ~q2)))), and knowing that q0 and q1 are inputs, while the other qubits are used as ancilla and output qubits, it is clear that the circuit can be optimized.
qc = circuit_boolean_optimizer(qf.circuit(), preserve=[0, 1])The circuit_boolean_optimizer allows us to perform boolean optimizations in a quantum circuit; from the previous unoptimized example, we then get the following optimized circuit:
Useful Links:
The Python notebook for this blog post is available here:
