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some little work here

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This commit is contained in:
Daniel Knüttel 2020-03-26 22:40:41 +01:00
parent 78ec3aa4c9
commit b7c123a3b2
2 changed files with 13 additions and 7 deletions
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5
thesis/chapters/appendix.tex
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@ -74,9 +74,12 @@ It has been mentioned several times that the implementation using
implementation. To support this statement a simple benchmark can be used. The implementation. To support this statement a simple benchmark can be used. The
relatively simple Pauli $X$ is used, more complicated gates like $CX$ or $H$ relatively simple Pauli $X$ is used, more complicated gates like $CX$ or $H$
have worse performance when implemented in \lstinline{python}. The performance have worse performance when implemented in \lstinline{python}. The performance
improvement when using the \lstinline{ufunc} is around $1.7$ in this tested improvement when using the \lstinline{ufunc} is a factor around $6.4$ in this tested
case. One must however note that the tested \lstinline{python} code is not case. One must however note that the tested \lstinline{python} code is not
realistic and in a possible applications there would be a significant overhead. realistic and in a possible applications there would be a significant overhead.
\lstinputlisting[title={Code to Benchmark \lstinline{ufunc} Gates against Python}, language=Python, breaklines=True]{extra_benchmark/benchmark.py} \lstinputlisting[title={Code to Benchmark \lstinline{ufunc} Gates against Python}, language=Python, breaklines=True]{extra_benchmark/benchmark.py}
When using \lstinline{result_py[0::2] = qm_state[1::2]} the result is identical and
the performance is only increased by a factor around $1.7$. This method is however not
applicable to general act-qbits and the bit mask has to be used.

15
thesis/extra_benchmark/benchmark.py
View File

@ -23,13 +23,15 @@ time_uf = timeit.repeat("result_uf = gate_uf(qm_state, cl_state)"
, number=1_000_000 , number=1_000_000
) )
time_py = timeit.repeat("result_py = np.zeros(2**10, dtype=np.cdouble);" time_py = timeit.repeat(
"result_py[0::2] = qm_state[1::2];" "result_py = np.zeros(2**10, dtype=np.cdouble);"
"result_py[1::2] = qm_state[0::2];" "result_py[~bit_mask] = qm_state[bit_mask];"
"cl_py = np.zeros(10, dtype=np.int8)" "result_py[bit_mask] = qm_state[~bit_mask];"
"cl_py = np.zeros(10, dtype=np.int8)"
, setup="import numpy as np;" , setup="import numpy as np;"
"qm_state = np.zeros(2**10, dtype=np.cdouble);" "qm_state = np.zeros(2**10, dtype=np.cdouble);"
"qm_state[0] = 1;" "qm_state[0] = 1;"
"bit_mask = np.array([1 if (i & (1 << 0)) else 0 for i in range(2**10)])"
, repeat=5 , repeat=5
, number=1_000_000 , number=1_000_000
) )
@ -38,10 +40,11 @@ print(" done")
print("running test ...", end="", flush=True) print("running test ...", end="", flush=True)
result_uf, cl, m = gate_uf(qm_state, cl_state); result_uf, cl, m = gate_uf(qm_state, cl_state);
bit_mask = np.array([1 if (i & (1 << 0)) else 0 for i in range(2**10)]).astype(np.bool)
result_py = np.zeros(2**10, dtype=np.cdouble) result_py = np.zeros(2**10, dtype=np.cdouble)
cl_py = np.zeros(10, dtype=np.int8) cl_py = np.zeros(10, dtype=np.int8)
result_py[0::2] = qm_state[1::2] result_py[~bit_mask] = qm_state[bit_mask]
result_py[1::2] = qm_state[0::2] result_py[bit_mask] = qm_state[~bit_mask]
assert np.allclose(result_py, result_uf) assert np.allclose(result_py, result_uf)
print(" done") print(" done")