some little work here
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@ -74,9 +74,12 @@ It has been mentioned several times that the implementation using
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implementation. To support this statement a simple benchmark can be used. The
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relatively simple Pauli $X$ is used, more complicated gates like $CX$ or $H$
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have worse performance when implemented in \lstinline{python}. The performance
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improvement when using the \lstinline{ufunc} is around $1.7$ in this tested
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improvement when using the \lstinline{ufunc} is a factor around $6.4$ in this tested
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case. One must however note that the tested \lstinline{python} code is not
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realistic and in a possible applications there would be a significant overhead.
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\lstinputlisting[title={Code to Benchmark \lstinline{ufunc} Gates against Python}, language=Python, breaklines=True]{extra_benchmark/benchmark.py}
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When using \lstinline{result_py[0::2] = qm_state[1::2]} the result is identical and
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the performance is only increased by a factor around $1.7$. This method is however not
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applicable to general act-qbits and the bit mask has to be used.
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@ -23,13 +23,15 @@ time_uf = timeit.repeat("result_uf = gate_uf(qm_state, cl_state)"
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, number=1_000_000
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)
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time_py = timeit.repeat("result_py = np.zeros(2**10, dtype=np.cdouble);"
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"result_py[0::2] = qm_state[1::2];"
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"result_py[1::2] = qm_state[0::2];"
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"cl_py = np.zeros(10, dtype=np.int8)"
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time_py = timeit.repeat(
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"result_py = np.zeros(2**10, dtype=np.cdouble);"
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"result_py[~bit_mask] = qm_state[bit_mask];"
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"result_py[bit_mask] = qm_state[~bit_mask];"
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"cl_py = np.zeros(10, dtype=np.int8)"
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, setup="import numpy as np;"
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"qm_state = np.zeros(2**10, dtype=np.cdouble);"
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"qm_state[0] = 1;"
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"bit_mask = np.array([1 if (i & (1 << 0)) else 0 for i in range(2**10)])"
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, repeat=5
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, number=1_000_000
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)
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@ -38,10 +40,11 @@ print(" done")
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print("running test ...", end="", flush=True)
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result_uf, cl, m = gate_uf(qm_state, cl_state);
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bit_mask = np.array([1 if (i & (1 << 0)) else 0 for i in range(2**10)]).astype(np.bool)
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result_py = np.zeros(2**10, dtype=np.cdouble)
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cl_py = np.zeros(10, dtype=np.int8)
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result_py[0::2] = qm_state[1::2]
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result_py[1::2] = qm_state[0::2]
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result_py[~bit_mask] = qm_state[bit_mask]
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result_py[bit_mask] = qm_state[~bit_mask]
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assert np.allclose(result_py, result_uf)
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print(" done")
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