pyqpanda3.quantum_info.quantum_info Namespace Reference

Classes
class	Chi
class	Choi
class	DensityMatrix
class	Kraus
class	Matrix
class	PTM
class	QuantumChannel
class	StateSystemType
class	StateVector
class	SuperOp
class	Unitary

Functions
float	KL_divergence (list[float] p, list[float] q)
	KL_divergence(*args, **kwargs) Overloaded function.
float	KL_divergence (Callable[[float], float] p_pdf, Callable[[float], float] q_pdf, float x_start, float x_end, float dx=...)
	KL_divergence(*args, **kwargs) Overloaded function.
float	hellinger_distance (dict[int, float] p, dict[int, float] q)
	hellinger_distance(*args, **kwargs) Overloaded function.
float	hellinger_distance (dict[str, float] p, dict[str, float] q)
	hellinger_distance(*args, **kwargs) Overloaded function.
float	hellinger_fidelity (dict[int, float] p, dict[int, float] q)
	hellinger_fidelity(*args, **kwargs) Overloaded function.
float	hellinger_fidelity (dict[str, float] p, dict[str, float] q)
	hellinger_fidelity(*args, **kwargs) Overloaded function.

Function Documentation

hellinger_distance() [1/2]

float pyqpanda3.quantum_info.quantum_info.hellinger_distance	(	dict[int, float]	p,
		dict[int, float]	q )

hellinger_distance(*args, **kwargs) Overloaded function.

1. hellinger_distance(p: dict[int, float], q: dict[int, float]) -> float

Template function to calculate the Hellinger distance between two probability distributions.

Parameters

dist_p	A reference to the first probability distribution, represented as an unordered map with keys of type long long and values of type double.
dist_q	A reference to the second probability distribution, represented as an unordered map with keys of type long long and values of type double.

Returns

double The Hellinger distance between the two distributions.

1. hellinger_distance(p: dict[str, float], q: dict[str, float]) -> float

Template function to calculate the Hellinger distance between two probability distributions.

Parameters

dist_p	A reference to the first probability distribution, represented as an unordered map with keys of type string and values of type double.
dist_q	A reference to the second probability distribution, represented as an unordered map with keys of type string and values of type double.

Returns

double The Hellinger distance between the two distributions.

hellinger_distance() [2/2]

float pyqpanda3.quantum_info.quantum_info.hellinger_distance	(	dict[str, float]	p,
		dict[str, float]	q )

hellinger_distance(*args, **kwargs) Overloaded function.

1. hellinger_distance(p: dict[int, float], q: dict[int, float]) -> float

Template function to calculate the Hellinger distance between two probability distributions.

Parameters

dist_p	A reference to the first probability distribution, represented as an unordered map with keys of type long long and values of type double.
dist_q	A reference to the second probability distribution, represented as an unordered map with keys of type long long and values of type double.

Returns

double The Hellinger distance between the two distributions.

1. hellinger_distance(p: dict[str, float], q: dict[str, float]) -> float

Template function to calculate the Hellinger distance between two probability distributions.

Parameters

dist_p	A reference to the first probability distribution, represented as an unordered map with keys of type string and values of type double.
dist_q	A reference to the second probability distribution, represented as an unordered map with keys of type string and values of type double.

Returns

double The Hellinger distance between the two distributions.

hellinger_fidelity() [1/2]

float pyqpanda3.quantum_info.quantum_info.hellinger_fidelity	(	dict[int, float]	p,
		dict[int, float]	q )

hellinger_fidelity(*args, **kwargs) Overloaded function.

1. hellinger_fidelity(p: dict[int, float], q: dict[int, float]) -> float

Calculates the Hellinger fidelity between two probability distributions represented as unordered maps.

Parameters

dist_p	A constant reference to the first probability distribution.
dist_q	A constant reference to the second probability distribution.

Returns

double The Hellinger fidelity between the two distributions.

1. hellinger_fidelity(p: dict[str, float], q: dict[str, float]) -> float

Calculates the Hellinger fidelity between two probability distributions represented as unordered maps.

Parameters

dist_p	A constant reference to the first probability distribution.
dist_q	A constant reference to the second probability distribution.

Returns

double The Hellinger fidelity between the two distributions.

hellinger_fidelity() [2/2]

float pyqpanda3.quantum_info.quantum_info.hellinger_fidelity	(	dict[str, float]	p,
		dict[str, float]	q )

hellinger_fidelity(*args, **kwargs) Overloaded function.

1. hellinger_fidelity(p: dict[int, float], q: dict[int, float]) -> float

Calculates the Hellinger fidelity between two probability distributions represented as unordered maps.

Parameters

dist_p	A constant reference to the first probability distribution.
dist_q	A constant reference to the second probability distribution.

Returns

double The Hellinger fidelity between the two distributions.

1. hellinger_fidelity(p: dict[str, float], q: dict[str, float]) -> float

Calculates the Hellinger fidelity between two probability distributions represented as unordered maps.

Parameters

dist_p	A constant reference to the first probability distribution.
dist_q	A constant reference to the second probability distribution.

Returns

double The Hellinger fidelity between the two distributions.

KL_divergence() [1/2]

float pyqpanda3.quantum_info.quantum_info.KL_divergence	(	Callable[[float], float]	p_pdf,
		Callable[[float], float]	q_pdf,
		float	x_start,
		float	x_end,
		float	dx = ... )

KL_divergence(*args, **kwargs) Overloaded function.

1. KL_divergence(p: list[float], q: list[float]) -> float

Calculates the Kullback-Leibler (KL) divergence between two discrete probability distributions.

The KL divergence is defined as: \[ \mathrm{KL}(\mathrm{p} \| \mathrm{q})=\sum \mathrm{p}(\mathrm{x}) \log \frac{\mathrm{p}(\mathrm{x})}{\mathrm{q}(\mathrm{x})} \]

Note

The KL divergence is not commutative, i.e., \(\mathrm{KL}(\mathrm{p} \| \mathrm{q}) \neq \mathrm{KL}(\mathrm{q} \| \mathrm{p})\).

Parameters

p	A constant reference to the first probability distribution.
q	A constant reference to the second probability distribution.

Returns

double The KL divergence between the two distributions.

1. KL_divergence(p_pdf: Callable[[float], float], q_pdf: Callable[[float], float], x_start: float, x_end: float, dx: float = 0.0001) -> float

Calculates the KL divergence for continuous probability distributions using function pointers.

The KL divergence is given by the formula: \f[ \mathrm{KL}(\mathrm{p} \| \mathrm{q})=\int \mathrm{p}(\mathrm{x}) \log \frac{\mathrm{p}(\mathrm{x})}{\mathrm{q}(\mathrm{x})} \mathrm{dx} \f]

Parameters

p_pdf	A pointer to a function representing the probability distribution p(x).
q_pdf	A pointer to a function representing the probability distribution q(x).
x_start	The starting point of the integration.
x_end	The end point of the integration.
dx	The step size for the numerical integration (default is 1e-4).

Returns

double The calculated KL divergence.

Note

The KL divergence is not commutative.

KL_divergence() [2/2]

float pyqpanda3.quantum_info.quantum_info.KL_divergence	(	list[float]	p,
		list[float]	q )

KL_divergence(*args, **kwargs) Overloaded function.

1. KL_divergence(p: list[float], q: list[float]) -> float

Calculates the Kullback-Leibler (KL) divergence between two discrete probability distributions.

The KL divergence is defined as: \[ \mathrm{KL}(\mathrm{p} \| \mathrm{q})=\sum \mathrm{p}(\mathrm{x}) \log \frac{\mathrm{p}(\mathrm{x})}{\mathrm{q}(\mathrm{x})} \]

Note

The KL divergence is not commutative, i.e., \(\mathrm{KL}(\mathrm{p} \| \mathrm{q}) \neq \mathrm{KL}(\mathrm{q} \| \mathrm{p})\).

Parameters

p	A constant reference to the first probability distribution.
q	A constant reference to the second probability distribution.

Returns

double The KL divergence between the two distributions.

1. KL_divergence(p_pdf: Callable[[float], float], q_pdf: Callable[[float], float], x_start: float, x_end: float, dx: float = 0.0001) -> float

Calculates the KL divergence for continuous probability distributions using function pointers.

The KL divergence is given by the formula: \f[ \mathrm{KL}(\mathrm{p} \| \mathrm{q})=\int \mathrm{p}(\mathrm{x}) \log \frac{\mathrm{p}(\mathrm{x})}{\mathrm{q}(\mathrm{x})} \mathrm{dx} \f]

Parameters

p_pdf	A pointer to a function representing the probability distribution p(x).
q_pdf	A pointer to a function representing the probability distribution q(x).
x_start	The starting point of the integration.
x_end	The end point of the integration.
dx	The step size for the numerical integration (default is 1e-4).

Returns

double The calculated KL divergence.

Note

The KL divergence is not commutative.