72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
"""
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TODO: DEPRECATE THIS ONE IN FAVOR OF lib_q_computer.py
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"""
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import random
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import numpy as np
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# |0> and |1>
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_0 = np.array([[1],
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[0]])
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_1 = np.array([[0],
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[1]])
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# |+> and |->
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_p = np.array([[1 / np.sqrt(2)],
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[1 / np.sqrt(2)]])
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_m = np.array([[1 / np.sqrt(2)],
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[-1 / np.sqrt(2)]])
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# Gates - Identity, Pauli X and Hadamard
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I = np.array([[1, 0],
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[0, 1]])
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X = np.array([[0, 1],
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[1, 0]])
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H = np.array([[1 / np.sqrt(2), 1 / np.sqrt(2)],
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[1 / np.sqrt(2), -1 / np.sqrt(2)]])
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def measure_probability(qbit):
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"""
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In a qbit [a, b] normalized: |a|^2 + |b|^2 = 1
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Probability of 0 is |a|^2 and 1 with prob |b|^2
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:returns: tuple of probabilities to measure 0 or 1"""
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return np.abs(qbit[0][0]) ** 2, np.abs(qbit[1][0]) ** 2
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def measure(qbit):
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"""
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This gets a random choice of either 0 and 1 with weights
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based on the probabilities of the qbit
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:returns: classical bit based on qbit probabilities"""
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return random.choices([0, 1], measure_probability(qbit))[0]
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def run_qbit_tests():
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# asserts are sets of tests to check if mathz workz
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# Identity: verify that I|0> == |0> and I|1> == |0>
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assert np.array_equal(I.dot(_0), _0)
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assert np.array_equal(I.dot(_1), _1)
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# Pauli X: verify that X|0> == |1> and X|1> == |0>
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assert np.array_equal(X.dot(_0), _1)
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assert np.array_equal(X.dot(_1), _0)
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# measure probabilities in sigma_x of |0> and |1>
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# using allclose since dealing with floats
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assert np.allclose(measure_probability(_0), (1.0, 0.0))
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assert np.allclose(measure_probability(_1), (0.0, 1.0))
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# applying Hadamard puts the qbit in orthogonal +/- basis
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assert np.array_equal(H.dot(_0), _p)
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assert np.array_equal(H.dot(_1), _m)
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# measure probabilities in sigma_x of |+> and |->
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# using allclose since dealing with floats
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assert np.allclose(measure_probability(_p), (0.5, 0.5))
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assert np.allclose(measure_probability(_m), (0.5, 0.5))
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