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Make all matrices dtype np.complex128 #1822

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101 changes: 60 additions & 41 deletions pyquil/simulation/matrices.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,39 +118,45 @@

import numpy as np

I = np.array([[1.0, 0.0], [0.0, 1.0]]) # noqa: E741
I = np.array([[1.0, 0.0], [0.0, 1.0]], dtype=np.complex128) # noqa: E741

X = np.array([[0.0, 1.0], [1.0, 0.0]])
X = np.array([[0.0, 1.0], [1.0, 0.0]], dtype=np.complex128)

Y = np.array([[0.0, 0.0 - 1.0j], [0.0 + 1.0j, 0.0]])
Y = np.array([[0.0, 0.0 - 1.0j], [0.0 + 1.0j, 0.0]], dtype=np.complex128)

Z = np.array([[1.0, 0.0], [0.0, -1.0]])
Z = np.array([[1.0, 0.0], [0.0, -1.0]], dtype=np.complex128)

H = (1.0 / np.sqrt(2.0)) * np.array([[1.0, 1.0], [1.0, -1.0]])
H = (1.0 / np.sqrt(2.0)) * np.array([[1.0, 1.0], [1.0, -1.0]], dtype=np.complex128)

S = np.array([[1.0, 0.0], [0.0, 1.0j]])
S = np.array([[1.0, 0.0], [0.0, 1.0j]], dtype=np.complex128)

T = np.array([[1.0, 0.0], [0.0, cmath.exp(1.0j * np.pi / 4.0)]])
T = np.array([[1.0, 0.0], [0.0, cmath.exp(1.0j * np.pi / 4.0)]], dtype=np.complex128)


def PHASE(phi: float) -> np.ndarray:
return np.array([[1.0, 0.0], [0.0, np.exp(1j * phi)]])
return np.array([[1.0, 0.0], [0.0, np.exp(1j * phi)]], dtype=np.complex128)


def RX(phi: float) -> np.ndarray:
return np.array([[np.cos(phi / 2.0), -1j * np.sin(phi / 2.0)], [-1j * np.sin(phi / 2.0), np.cos(phi / 2.0)]])
return np.array(
[[np.cos(phi / 2.0), -1j * np.sin(phi / 2.0)], [-1j * np.sin(phi / 2.0), np.cos(phi / 2.0)]],
dtype=np.complex128,
)


def RY(phi: float) -> np.ndarray:
return np.array([[np.cos(phi / 2.0), -np.sin(phi / 2.0)], [np.sin(phi / 2.0), np.cos(phi / 2.0)]])
return np.array(
[[np.cos(phi / 2.0), -np.sin(phi / 2.0)], [np.sin(phi / 2.0), np.cos(phi / 2.0)]], dtype=np.complex128
)


def RZ(phi: float) -> np.ndarray:
return np.array(
[
[np.cos(phi / 2.0) - 1j * np.sin(phi / 2.0), 0],
[0, np.cos(phi / 2.0) + 1j * np.sin(phi / 2.0)],
]
],
dtype=np.complex128,
)


Expand All @@ -159,13 +165,14 @@ def U(theta: float, phi: float, lam: float) -> np.ndarray:
[
[np.cos(theta / 2.0), -1 * np.exp(1j * lam) * np.sin(theta / 2.0)],
[np.exp(1j * phi) * np.sin(theta / 2.0), np.exp(1j * (phi + lam)) * np.cos(theta / 2.0)],
]
],
dtype=np.complex128,
)


CZ = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, -1]])
CZ = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, -1]], dtype=np.complex128)

CNOT = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]])
CNOT = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]], dtype=np.complex128)

CCNOT = np.array(
[
Expand All @@ -177,24 +184,25 @@ def U(theta: float, phi: float, lam: float) -> np.ndarray:
[0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 1, 0],
]
],
dtype=np.complex128,
)


def CPHASE00(phi: float) -> np.ndarray:
return np.diag([np.exp(1j * phi), 1.0, 1.0, 1.0])
return np.diag([np.exp(1j * phi), 1.0, 1.0, 1.0]).astype(np.complex128)


def CPHASE01(phi: float) -> np.ndarray:
return np.diag([1.0, np.exp(1j * phi), 1.0, 1.0])
return np.diag([1.0, np.exp(1j * phi), 1.0, 1.0]).astype(np.complex128)


def CPHASE10(phi: float) -> np.ndarray:
return np.diag([1.0, 1.0, np.exp(1j * phi), 1.0])
return np.diag([1.0, 1.0, np.exp(1j * phi), 1.0]).astype(np.complex128)


def CPHASE(phi: float) -> np.ndarray:
return np.diag([1.0, 1.0, 1.0, np.exp(1j * phi)])
return np.diag([1.0, 1.0, 1.0, np.exp(1j * phi)]).astype(np.complex128)


SWAP = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
Expand All @@ -209,14 +217,17 @@ def CPHASE(phi: float) -> np.ndarray:
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
]
],
dtype=np.complex128,
)

ISWAP = np.array([[1, 0, 0, 0], [0, 0, 1j, 0], [0, 1j, 0, 0], [0, 0, 0, 1]])
ISWAP = np.array([[1, 0, 0, 0], [0, 0, 1j, 0], [0, 1j, 0, 0], [0, 0, 0, 1]], dtype=np.complex128)


def PSWAP(phi: float) -> np.ndarray:
return np.array([[1, 0, 0, 0], [0, 0, np.exp(1j * phi), 0], [0, np.exp(1j * phi), 0, 0], [0, 0, 0, 1]])
return np.array(
[[1, 0, 0, 0], [0, 0, np.exp(1j * phi), 0], [0, np.exp(1j * phi), 0, 0], [0, 0, 0, 1]], dtype=np.complex128
)


def XY(phi: float) -> np.ndarray:
Expand All @@ -226,7 +237,8 @@ def XY(phi: float) -> np.ndarray:
[0, np.cos(phi / 2), 1j * np.sin(phi / 2), 0],
[0, 1j * np.sin(phi / 2), np.cos(phi / 2), 0],
[0, 0, 0, 1],
]
],
dtype=np.complex128,
)


Expand All @@ -237,7 +249,8 @@ def FSIM(theta: float, phi: float) -> np.ndarray:
[0, np.cos(theta / 2), 1j * np.sin(theta / 2), 0],
[0, 1j * np.sin(theta / 2), np.cos(theta / 2), 0],
[0, 0, 0, np.exp(1j * phi)],
]
],
dtype=np.complex128,
)


Expand All @@ -258,7 +271,8 @@ def PHASEDFSIM(theta: float, zeta: float, chi: float, gamma: float, phi: float)
0,
],
[0, 0, 0, np.exp(1j * phi - 2j * gamma)],
]
],
dtype=np.complex128,
)


Expand All @@ -269,7 +283,8 @@ def RZZ(phi: float) -> np.ndarray:
[0, np.exp(+1j * phi / 2), 0, 0],
[0, 0, np.exp(+1j * phi / 2), 0],
[0, 0, 0, np.exp(-1j * phi / 2)],
]
],
dtype=np.complex128,
)


Expand All @@ -280,7 +295,8 @@ def RXX(phi: float) -> np.ndarray:
[0, np.cos(phi / 2), -1j * np.sin(phi / 2), 0],
[0, -1j * np.sin(phi / 2), np.cos(phi / 2), 0],
[-1j * np.sin(phi / 2), 0, 0, np.cos(phi / 2)],
]
],
dtype=np.complex128,
)


Expand All @@ -291,7 +307,8 @@ def RYY(phi: float) -> np.ndarray:
[0, np.cos(phi / 2), -1j * np.sin(phi / 2), 0],
[0, -1j * np.sin(phi / 2), np.cos(phi / 2), 0],
[+1j * np.sin(phi / 2), 0, 0, np.cos(phi / 2)],
]
],
dtype=np.complex128,
)


Expand All @@ -301,13 +318,14 @@ def RYY(phi: float) -> np.ndarray:
[0, 1 / np.sqrt(2), 1j / np.sqrt(2), 0],
[0, 1j / np.sqrt(2), 1 / np.sqrt(2), 0],
[0, 0, 0, 1],
]
],
dtype=np.complex128,
)

# Utility gates for internal QVM use
P0 = np.array([[1, 0], [0, 0]])
P0 = np.array([[1, 0], [0, 0]], dtype=np.complex128)

P1 = np.array([[0, 0], [0, 1]])
P1 = np.array([[0, 0], [0, 1]], dtype=np.complex128)


# Specialized useful gates; not officially in standard gate set
Expand All @@ -316,7 +334,8 @@ def BARENCO(alpha: float, phi: float, theta: float) -> np.ndarray:
[
[np.exp(1j * phi) * np.cos(theta), -1j * np.exp(1j * (alpha - phi)) * np.sin(theta)],
[-1j * np.exp(1j * (alpha + phi)) * np.sin(theta), np.exp(1j * alpha) * np.cos(theta)],
]
],
dtype=np.complex128,
)
return np.kron(P0, np.eye(2)) + np.kron(P1, lower_unitary)

Expand Down Expand Up @@ -357,8 +376,8 @@ def BARENCO(alpha: float, phi: float, theta: float) -> np.ndarray:

def relaxation_operators(p: float) -> tuple[np.ndarray, np.ndarray]:
"""Return the amplitude damping Kraus operators."""
k0 = np.array([[1.0, 0.0], [0.0, np.sqrt(1 - p)]])
k1 = np.array([[0.0, np.sqrt(p)], [0.0, 0.0]])
k0 = np.array([[1.0, 0.0], [0.0, np.sqrt(1 - p)]], dtype=np.complex128)
k1 = np.array([[0.0, np.sqrt(p)], [0.0, 0.0]], dtype=np.complex128)
return k0, k1


Expand Down Expand Up @@ -408,10 +427,10 @@ def bitphase_flip_operators(p: float) -> tuple[np.ndarray, np.ndarray]:
"bitphase_flip": bitphase_flip_operators,
}

SIC0 = np.array([1, 0])
SIC1 = np.array([1, np.sqrt(2)]) / np.sqrt(3)
SIC2 = np.array([1, np.exp(-np.pi * 2j / 3) * np.sqrt(2)]) / np.sqrt(3)
SIC3 = np.array([1, np.exp(np.pi * 2j / 3) * np.sqrt(2)]) / np.sqrt(3)
SIC0 = np.array([1, 0], dtype=np.complex128)
SIC1 = np.array([1, np.sqrt(2)], dtype=np.complex128) / np.sqrt(3)
SIC2 = np.array([1, np.exp(-np.pi * 2j / 3) * np.sqrt(2)], dtype=np.complex128) / np.sqrt(3)
SIC3 = np.array([1, np.exp(np.pi * 2j / 3) * np.sqrt(2)], dtype=np.complex128) / np.sqrt(3)
"""
The symmetric informationally complete POVMs for a qubit.

Expand All @@ -420,9 +439,9 @@ def bitphase_flip_operators(p: float) -> tuple[np.ndarray, np.ndarray]:
"""

STATES = {
"X": [np.array([1, 1]) / np.sqrt(2), np.array([1, -1]) / np.sqrt(2)],
"Y": [np.array([1, 1j]) / np.sqrt(2), np.array([1, -1j]) / np.sqrt(2)],
"Z": [np.array([1, 0]), np.array([0, 1])],
"X": [np.array([1, 1], dtype=np.complex128) / np.sqrt(2), np.array([1, -1], dtype=np.complex128) / np.sqrt(2)],
"Y": [np.array([1, 1j], dtype=np.complex128) / np.sqrt(2), np.array([1, -1j], dtype=np.complex128) / np.sqrt(2)],
"Z": [np.array([1, 0], dtype=np.complex128), np.array([0, 1], dtype=np.complex128)],
"SIC": [SIC0, SIC1, SIC2, SIC3],
}

Expand Down
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