def fid(target_unitary, error_channel_operators, density_matrix, symbolic=1): """Fidelity between a unitary gate and a non-necessarily unitary gate, for a given initial density matrix. This is later used when calculating the worst case fidelity. Notice that the input format of the general channel is a list of Kraus operators instead of a process matrix. The input format of the target unitary is just the matrix itself, not its process matrix. symbolic = 1 is the case when the the input matrices are sympy, while symbolic = 0 is used when the input matrices are numpy. """ V, K, rho = target_unitary, error_channel_operators, density_matrix if symbolic: Tra = (((V.H)*K[0])*rho).trace() fid = Tra*(fun.conjugate(Tra)) for i in range(1,len(K)): Tra = (((V.H)*K[i])*rho).trace() fid += Tra*(fun.conjugate(Tra)) return fid.expand() else: Tra = np.trace((V.H)*K[0]*rho) fid = Tra*(Tra.conjugate()) for i in range(1,len(K)): Tra = np.trace((V.H)*K[i]*rho) fid += Tra*(Tra.conjugate()) return fid
def test_Function():
assert mcode(f(x, y, z)) == "f[x, y, z]"
assert mcode(sin(x)**cos(x)) == "Sin[x]^Cos[x]"
assert mcode(conjugate(x)) == "Conjugate[x]"
assert mcode(Max(x, y, z) * Min(y, z)) == "Max[x, y, z]*Min[y, z]"
assert mcode(fresnelc(x)) == "FresnelC[x]"
assert mcode(fresnels(x)) == "FresnelS[x]"
assert mcode(gamma(x)) == "Gamma[x]"
assert mcode(uppergamma(x, y)) == "Gamma[x, y]"
assert mcode(polygamma(x, y)) == "PolyGamma[x, y]"
assert mcode(loggamma(x)) == "LogGamma[x]"
assert mcode(erf(x)) == "Erf[x]"
assert mcode(erfc(x)) == "Erfc[x]"
assert mcode(erfi(x)) == "Erfi[x]"
assert mcode(erf2(x, y)) == "Erf[x, y]"
assert mcode(expint(x, y)) == "ExpIntegralE[x, y]"
assert mcode(erfcinv(x)) == "InverseErfc[x]"
assert mcode(erfinv(x)) == "InverseErf[x]"
assert mcode(erf2inv(x, y)) == "InverseErf[x, y]"
assert mcode(Ei(x)) == "ExpIntegralEi[x]"
assert mcode(Ci(x)) == "CosIntegral[x]"
assert mcode(li(x)) == "LogIntegral[x]"
assert mcode(Si(x)) == "SinIntegral[x]"
assert mcode(Shi(x)) == "SinhIntegral[x]"
assert mcode(Chi(x)) == "CoshIntegral[x]"
assert mcode(beta(x, y)) == "Beta[x, y]"
assert mcode(factorial(x)) == "Factorial[x]"
assert mcode(factorial2(x)) == "Factorial2[x]"
assert mcode(subfactorial(x)) == "Subfactorial[x]"
assert mcode(FallingFactorial(x, y)) == "FactorialPower[x, y]"
assert mcode(RisingFactorial(x, y)) == "Pochhammer[x, y]"
assert mcode(catalan(x)) == "CatalanNumber[x]"
assert mcode(harmonic(x)) == "HarmonicNumber[x]"
assert mcode(harmonic(x, y)) == "HarmonicNumber[x, y]"
def test_Trace():
assert isinstance(Trace(A), Trace)
assert not isinstance(Trace(A), MatrixExpr)
raises(ShapeError, lambda: Trace(C))
assert trace(eye(3)) == 3
assert trace(Matrix(3, 3, [1, 2, 3, 4, 5, 6, 7, 8, 9])) == 15
assert adjoint(Trace(A)) == trace(Adjoint(A))
assert conjugate(Trace(A)) == trace(Adjoint(A))
assert transpose(Trace(A)) == Trace(A)
A / Trace(A) # Make sure this is possible
# Some easy simplifications
assert trace(Identity(5)) == 5
assert trace(ZeroMatrix(5, 5)) == 0
assert trace(OneMatrix(1, 1)) == 1
assert trace(OneMatrix(2, 2)) == 2
assert trace(OneMatrix(n, n)) == n
assert trace(2*A*B) == 2*Trace(A*B)
assert trace(A.T) == trace(A)
i, j = symbols('i j')
F = FunctionMatrix(3, 3, Lambda((i, j), i + j))
assert trace(F) == (0 + 0) + (1 + 1) + (2 + 2)
raises(TypeError, lambda: Trace(S.One))
assert Trace(A).arg is A
assert str(trace(A)) == str(Trace(A).doit())
assert Trace(A).is_commutative is True
def test_transpose():
Sq = MatrixSymbol('Sq', n, n)
assert transpose(A) == Transpose(A)
assert Transpose(A).shape == (m, n)
assert Transpose(A * B).shape == (l, n)
assert transpose(Transpose(A)) == A
assert isinstance(Transpose(Transpose(A)), Transpose)
assert adjoint(Transpose(A)) == Adjoint(Transpose(A))
assert conjugate(Transpose(A)) == Adjoint(A)
assert Transpose(eye(3)).doit() == eye(3)
assert Transpose(S(5)).doit() == S(5)
assert Transpose(Matrix([[1, 2], [3, 4]])).doit() == Matrix([[1, 3],
[2, 4]])
assert transpose(trace(Sq)) == trace(Sq)
assert trace(Transpose(Sq)) == trace(Sq)
assert Transpose(Sq)[0, 1] == Sq[1, 0]
assert Transpose(A * B).doit() == Transpose(B) * Transpose(A)
def construct(self):
for i in range(self.size):
for j in range(i, self.size):
self.H[i,j] = sympy.symbols(self.entry_template.format(i,j,"Re"), real=True)
if j > i:
self.H[i,j] += sympy.I * sympy.symbols(self.entry_template.format(i,j,"Im"), real=True)
self.H[j,i] = conjugate(self.H[i,j])
def test_trace():
assert isinstance(Trace(A), Trace)
assert not isinstance(Trace(A), MatrixExpr)
raises(ShapeError, lambda: Trace(C))
assert Trace(eye(3)) == 3
assert Trace(Matrix(3, 3, [1, 2, 3, 4, 5, 6, 7, 8, 9])) == 15
assert adjoint(Trace(A)) == Trace(Adjoint(A))
assert conjugate(Trace(A)) == Trace(Adjoint(A))
assert transpose(Trace(A)) == Trace(A)
A / Trace(A) # Make sure this is possible
# Some easy simplifications
assert Trace(Identity(5)) == 5
assert Trace(ZeroMatrix(5, 5)) == 0
assert Trace(2 * A * B) == 2 * Trace(A * B)
assert Trace(A.T) == Trace(A)
i, j = symbols('i j')
F = FunctionMatrix(3, 3, Lambda((i, j), i + j))
assert Trace(F).doit() == (0 + 0) + (1 + 1) + (2 + 2)
raises(TypeError, lambda: Trace(S.One))
assert Trace(A).arg is A
def run():
A, lam, x, om, t = symbols("A lam x om t", real=True)
assumptions.assume.global_assumptions.add(Q.positive(lam))
psi = Function('psi')(x)
psi = A * exp(-lam * Abs(x) - I * om * t)
ret = simplify(conjugate(psi) * psi) # 简化
print(ret) # A**2*exp(-2*lam*Abs(x))
ret = integrate(conjugate(psi) * psi, (x, -oo, oo))
print(ret) # (A**2/lam, Abs(arg(lam)) < pi/2)
# 所以求得系数 A = sqrt(lam)
psi = sqrt(lam) * exp(-lam * Abs(x) - I * om * t)
# 已知波函数了，求解<x>和<x**2>
print(integrate(conjugate(psi) * x * psi, (x, -oo, oo))) # 0
print(integrate(conjugate(psi) * x * x * psi, (x, -oo, oo))) #
def test_adjoint():
Sq = MatrixSymbol('Sq', n, n)
assert Adjoint(A).shape == (m, n)
assert Adjoint(A*B).shape == (l, n)
assert adjoint(Adjoint(A)) == A
assert isinstance(Adjoint(Adjoint(A)), Adjoint)
assert conjugate(Adjoint(A)) == Transpose(A)
assert transpose(Adjoint(A)) == Adjoint(Transpose(A))
assert Adjoint(eye(3)).doit() == eye(3)
assert Adjoint(S(5)).doit() == S(5)
assert Adjoint(Matrix([[1, 2], [3, 4]])).doit() == Matrix([[1, 3], [2, 4]])
assert adjoint(Trace(Sq)) == conjugate(Trace(Sq))
assert Trace(adjoint(Sq)) == conjugate(Trace(Sq))
assert Adjoint(Sq)[0, 1] == conjugate(Sq[1, 0])
assert Adjoint(A*B).doit() == Adjoint(B) * Adjoint(A)
def test_adjoint():
Sq = MatrixSymbol("Sq", n, n)
assert Adjoint(A).shape == (m, n)
assert Adjoint(A * B).shape == (l, n)
assert adjoint(Adjoint(A)) == A
assert isinstance(Adjoint(Adjoint(A)), Adjoint)
assert conjugate(Adjoint(A)) == Transpose(A)
assert transpose(Adjoint(A)) == Adjoint(Transpose(A))
assert Adjoint(eye(3)).doit() == eye(3)
assert Adjoint(S(5)).doit() == S(5)
assert Adjoint(Matrix([[1, 2], [3, 4]])).doit() == Matrix([[1, 3], [2, 4]])
assert adjoint(trace(Sq)) == conjugate(trace(Sq))
assert trace(adjoint(Sq)) == conjugate(trace(Sq))
assert Adjoint(Sq)[0, 1] == conjugate(Sq[1, 0])
assert Adjoint(A * B).doit() == Adjoint(B) * Adjoint(A)
def test_Function_change_name():
assert mcode(abs(x)) == "abs(x)"
assert mcode(ceiling(x)) == "ceil(x)"
assert mcode(arg(x)) == "angle(x)"
assert mcode(im(x)) == "imag(x)"
assert mcode(re(x)) == "real(x)"
assert mcode(conjugate(x)) == "conj(x)"
assert mcode(chebyshevt(y, x)) == "chebyshevT(y, x)"
assert mcode(chebyshevu(y, x)) == "chebyshevU(y, x)"
assert mcode(laguerre(x, y)) == "laguerreL(x, y)"
assert mcode(Chi(x)) == "coshint(x)"
assert mcode(Shi(x)) == "sinhint(x)"
assert mcode(Ci(x)) == "cosint(x)"
assert mcode(Si(x)) == "sinint(x)"
assert mcode(li(x)) == "logint(x)"
assert mcode(loggamma(x)) == "gammaln(x)"
assert mcode(polygamma(x, y)) == "psi(x, y)"
assert mcode(RisingFactorial(x, y)) == "pochhammer(x, y)"
assert mcode(DiracDelta(x)) == "dirac(x)"
assert mcode(DiracDelta(x, 3)) == "dirac(3, x)"
assert mcode(Heaviside(x)) == "heaviside(x)"
assert mcode(Heaviside(x, y)) == "heaviside(x, y)"
def test_Function_change_name():
assert mcode(abs(x)) == "abs(x)"
assert mcode(ceiling(x)) == "ceil(x)"
assert mcode(arg(x)) == "angle(x)"
assert mcode(im(x)) == "imag(x)"
assert mcode(re(x)) == "real(x)"
assert mcode(conjugate(x)) == "conj(x)"
assert mcode(chebyshevt(y, x)) == "chebyshevT(y, x)"
assert mcode(chebyshevu(y, x)) == "chebyshevU(y, x)"
assert mcode(laguerre(x, y)) == "laguerreL(x, y)"
assert mcode(Chi(x)) == "coshint(x)"
assert mcode(Shi(x)) == "sinhint(x)"
assert mcode(Ci(x)) == "cosint(x)"
assert mcode(Si(x)) == "sinint(x)"
assert mcode(li(x)) == "logint(x)"
assert mcode(loggamma(x)) == "gammaln(x)"
assert mcode(polygamma(x, y)) == "psi(x, y)"
assert mcode(RisingFactorial(x, y)) == "pochhammer(x, y)"
assert mcode(DiracDelta(x)) == "dirac(x)"
assert mcode(DiracDelta(x, 3)) == "dirac(3, x)"
assert mcode(Heaviside(x)) == "heaviside(x)"
assert mcode(Heaviside(x, y)) == "heaviside(x, y)"
def conjugate(self):
return conjugate(self)
def _get_const_characteristic_eq_sols(r, func, order):
r"""
Returns the roots of characteristic equation of constant coefficient
linear ODE and list of collectterms which is later on used by simplification
to use collect on solution.
The parameter `r` is a dict of order:coeff terms, where order is the order of the
derivative on each term, and coeff is the coefficient of that derivative.
"""
x = func.args[0]
# First, set up characteristic equation.
chareq, symbol = S.Zero, Dummy('x')
for i in r.keys():
if type(i) == str or i < 0:
pass
else:
chareq += r[i] * symbol**i
chareq = Poly(chareq, symbol)
# Can't just call roots because it doesn't return rootof for unsolveable
# polynomials.
chareqroots = roots(chareq, multiple=True)
if len(chareqroots) != order:
chareqroots = [rootof(chareq, k) for k in range(chareq.degree())]
chareq_is_complex = not all(i.is_real for i in chareq.all_coeffs())
# Create a dict root: multiplicity or charroots
charroots = defaultdict(int)
for root in chareqroots:
charroots[root] += 1
# We need to keep track of terms so we can run collect() at the end.
# This is necessary for constantsimp to work properly.
collectterms = []
gensols = []
conjugate_roots = [] # used to prevent double-use of conjugate roots
# Loop over roots in theorder provided by roots/rootof...
for root in chareqroots:
# but don't repoeat multiple roots.
if root not in charroots:
continue
multiplicity = charroots.pop(root)
for i in range(multiplicity):
if chareq_is_complex:
gensols.append(x**i * exp(root * x))
collectterms = [(i, root, 0)] + collectterms
continue
reroot = re(root)
imroot = im(root)
if imroot.has(atan2) and reroot.has(atan2):
# Remove this condition when re and im stop returning
# circular atan2 usages.
gensols.append(x**i * exp(root * x))
collectterms = [(i, root, 0)] + collectterms
else:
if root in conjugate_roots:
collectterms = [(i, reroot, imroot)] + collectterms
continue
if imroot == 0:
gensols.append(x**i * exp(reroot * x))
collectterms = [(i, reroot, 0)] + collectterms
continue
conjugate_roots.append(conjugate(root))
gensols.append(x**i * exp(reroot * x) * sin(abs(imroot) * x))
gensols.append(x**i * exp(reroot * x) * cos(imroot * x))
# This ordering is important
collectterms = [(i, reroot, imroot)] + collectterms
return gensols, collectterms
def test_Function():
assert mcode(f(x, y, z)) == "f[x, y, z]"
assert mcode(sin(x)**cos(x)) == "Sin[x]^Cos[x]"
assert mcode(conjugate(x)) == "Conjugate[x]"
def _eval_adjoint(self):
return conjugate(self.arg)
def _entry(self, i, j, **kwargs):
return conjugate(self.arg._entry(j, i, **kwargs))
def _entry(self, i, j):
return conjugate(self.arg._entry(j, i))
def test_Function():
assert mcode(f(x, y, z)) == "f[x, y, z]"
assert mcode(sin(x) ** cos(x)) == "Sin[x]^Cos[x]"
assert mcode(conjugate(x)) == "Conjugate[x]"
def _eval_trace(self):
from sympy.matrices.expressions.trace import Trace
return conjugate(Trace(self.arg))
def _entry(self, i, j, **kwargs):
return conjugate(self.arg._entry(j, i, **kwargs))
def _eval_transpose(self):
return conjugate(self.arg)
def _eval_transpose(self):
return conjugate(self.arg)
def _entry(self, i, j):
return conjugate(self.arg._entry(j, i))
def _eval_trace(self):
from sympy.matrices.expressions.trace import Trace
return conjugate(Trace(self.arg))
def _eval_adjoint(self):
return conjugate(self.arg)
def conjugate(self):
return conjugate(self)
def ctranspose(matrix):
return matrix.transpose().applyfunc(lambda i: conjugate(i))
