Free Maps on Garmin eTrex 10 (and all Garmin GPS)
Despite it is sold as a non-cartographic handled GPS device, with limited storage capacity of 10 MB and the inability to expand it, the eTrex 10 GPS, like al...
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:
Despite it is sold as a non-cartographic handled GPS device, with limited storage capacity of 10 MB and the inability to expand it, the eTrex 10 GPS, like al...
Since the latest v0.1.18 version, the Qlasskit library offers two useful tool for circuit analysis and optimization.
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