def main_fail():
print()
print("==" * 70)
H = """
012345678901234
0 YXZZ...........
1 X..X.XX.X......
2 ZZ..ZZ.......Z.
3 .X..Y....XZ....
4 .ZX....Y.Z.....
5 .....ZZZ.Z....Z
6 ..Z...ZZ....ZZ.
7 ...Z....Y.XX...
8 ..XX.......ZY..
9 .........ZXX..Y
10 .....X.....XXXX
11 ....X.X.X.X..X.
"""
H = syparse(H)
print()
print(shortstr(H))
m = len(H)
n = H.shape[1] // 2
assert n == 15
F = symplectic(n)
C = dot2(H, dot2(F, H.transpose()))
print(C.shape)
for i in range(m):
for j in range(i + 1, m):
if C[i, j]:
print("fail:", i, j)
print(rank(H))
H = linear_independent(H)
print("H=", H.shape)
print(shortstr(H))
HF = dot2(H, F)
K = array2(find_kernel(HF))
print("K=", K.shape)
print(shortstr(K))
HK = numpy.concatenate((H, K))
L = linear_independent(HK)
print()
print(shortstr(L))
def classical_distance(H, max_dist=0):
n = H.shape[1]
dist = n
K = find_kernel(H)
K = numpy.array(K, dtype=numpy.int32)
Kt = K.transpose()
for u in numpy.ndindex((2, ) * K.shape[0]):
v = dot2(Kt, u)
if 0 < v.sum() < dist:
dist = v.sum()
#if dist<=max_dist:
# break
return dist
def find_homology_2(g, f, *dims):
print("find_homology")
print(dims[2], "--f-->", dims[1], "--g-->", dims[0])
F = numpy.zeros((dims[1], dims[2]))
for row, col in list(f.keys()):
v = f[row, col]
F[row, col] = v % 2
#print shortstr(F.transpose())
#print
G = numpy.zeros((dims[0], dims[1]))
for row, col in list(g.keys()):
v = g[row, col]
G[row, col] = v % 2
#print shortstr(G)
if argv.ldpc:
from qupy.ldpc.css import CSSCode
code = CSSCode(Hx=F.transpose(), Hz=G)
code.build()
code = code.prune_deadbits()
print(code)
name = argv.get("name")
if name.endswith(".ldpc"):
print("saving", name)
code.save(name)
if argv.logops:
L = find_logops(G, F.transpose())
w = min([v.sum() for v in L])
print("logops weight:", w)
L = find_logops(F, G.transpose())
w = min([v.sum() for v in L])
print("logops weight:", w)
GF = numpy.dot(G, F) % 2
#print shortstr(GF)
assert numpy.abs(GF).sum() == 0
F = F.astype(numpy.int32)
G = G.astype(numpy.int32)
print("rank(f)=%d, rank(g)=%d" % (rank(F), rank(G)))
if g:
kerg = find_kernel(G)
print("ker(g):", len(kerg))
def __init__(self, G, H=None, d=None, desc="", check=True):
assert len(G.shape)==2
self.G = G.copy()
self.k, self.n = G.shape
self.d = d
self.desc = desc
if H is None:
H = list(find_kernel(G))
H = array2(H)
if H.shape == (0,):
H.shape = (0, self.n)
self.H = H.copy()
if check:
self.check()
def main_torus():
n = 8
# ZZZZZZZZ|XXXXXXXX
# 12345678|12345678
H = parse("""
111..1..|........
1..1....|1.1.....
........|11.11...
.1..1...|.1...1..
..1...1.|...1..1.
...11.11|........
.....1.1|....1..1
........|..1..111
""".replace("|", ""))
print()
print("H=")
print(shortstr(H))
F = symplectic(n)
C = dot2(H, dot2(F, H.transpose()))
for i in range(n):
for j in range(i + 1, n):
if C[i, j]:
print("fail:", i + 1, j + 1)
print(rank(H))
H = linear_independent(H)
print("H=")
print(shortstr(H))
HF = dot2(H, F)
K = array2(find_kernel(HF))
print("K=")
print(shortstr(K))
HK = numpy.concatenate((H, K))
L = linear_independent(HK)
print()
print(shortstr(L))
def intersect(G1, G2):
G = numpy.concatenate((G1, G2))
#print("intersect")
#print(G1, G2)
#print(G)
G = G.transpose()
#print("find_kernel", G.shape)
K = find_kernel(G)
if not K:
K = numpy.array(K)
K.shape = 0, G.shape[1]
else:
K = numpy.array(K)
#print("K:")
#print(K, K.shape)
G = dot2(K[:, :len(G1)], G1)
#print("G:")
#print(G, G.shape)
#print()
G = normal_form(G)
return G
def search():
# Bravyi, Haah, 1209.2426v1 sec IX.
# https://arxiv.org/pdf/1209.2426.pdf
verbose = argv.get("verbose")
m = argv.get("m", 6) # _number of rows
k = argv.get("k", None) # _number of odd-weight rows
# these are the variables N_x
xs = list(cross([(0, 1)]*m))
maxweight = argv.maxweight
minweight = argv.get("minweight", 1)
xs = [x for x in xs if minweight <= sum(x)]
if maxweight:
xs = [x for x in xs if sum(x) <= maxweight]
N = len(xs)
lhs = []
rhs = []
# bi-orthogonality
for a in range(m):
for b in range(a+1, m):
v = zeros2(N)
for i, x in enumerate(xs):
if x[a] == x[b] == 1:
v[i] = 1
if v.sum():
lhs.append(v)
rhs.append(0)
# tri-orthogonality
for a in range(m):
for b in range(a+1, m):
for c in range(b+1, m):
v = zeros2(N)
for i, x in enumerate(xs):
if x[a] == x[b] == x[c] == 1:
v[i] = 1
if v.sum():
lhs.append(v)
rhs.append(0)
# # dissallow columns with weight <= 1
# for i, x in enumerate(xs):
# if sum(x)<=1:
# v = zeros2(N)
# v[i] = 1
# lhs.append(v)
# rhs.append(0)
if k is not None:
# constrain to k _number of odd-weight rows
assert 0<=k<m
for a in range(m):
v = zeros2(N)
for i, x in enumerate(xs):
if x[a] == 1:
v[i] = 1
lhs.append(v)
if a<k:
rhs.append(1)
else:
rhs.append(0)
A = array2(lhs)
rhs = array2(rhs)
#print(shortstr(A))
B = pseudo_inverse(A)
soln = dot2(B, rhs)
if not eq2(dot2(A, soln), rhs):
print("no solution")
return
if verbose:
print("soln:")
print(shortstr(soln))
soln.shape = (N, 1)
rhs.shape = A.shape[0], 1
K = array2(list(find_kernel(A)))
#print(K)
#print( dot2(A, K.transpose()))
#sols = []
#for v in span(K):
best = None
density = 1.0
size = 99*N
trials = argv.get("trials", 1024)
count = 0
for trial in range(trials):
u = rand2(len(K), 1)
v = dot2(K.transpose(), u)
#print(v)
v = (v+soln)%2
assert eq2(dot2(A, v), rhs)
if v.sum() > size:
continue
size = v.sum()
Gt = []
for i, x in enumerate(xs):
if v[i]:
Gt.append(x)
if not Gt:
continue
Gt = array2(Gt)
G = Gt.transpose()
assert is_morthogonal(G, 3)
if G.shape[1]<m:
continue
if 0 in G.sum(1):
continue
if argv.strong_morthogonal and not strong_morthogonal(G, 3):
continue
#print(shortstr(G))
# for g in G:
# print(shortstr(g), g.sum())
# print()
_density = float(G.sum()) / (G.shape[0]*G.shape[1])
#if best is None or _density < density:
if best is None or G.shape[1] <= size:
best = G
size = G.shape[1]
density = _density
if 0:
#sols.append(G)
Gx = even_rows(G)
assert is_morthogonal(Gx, 3)
if len(Gx)==0:
continue
GGx = array2(list(span(Gx)))
assert is_morthogonal(GGx, 3)
count += 1
print("found %d solutions" % count)
if best is None:
return
G = best
#print(shortstr(G))
for g in G:
print(shortstr(g), g.sum())
print()
print("density:", density)
print("shape:", G.shape)
G = linear_independent(G)
A = list(span(G))
print(strong_morthogonal(A, 1))
print(strong_morthogonal(A, 2))
print(strong_morthogonal(A, 3))
#print(shortstr(dot2(G, G.transpose())))
if 0:
B = pseudo_inverse(A)
v = dot2(B, rhs)
print("B:")
print(shortstr(B))
print("v:")
print(shortstr(v))
assert eq2(dot2(B, v), rhs)
def search_selfdual():
verbose = argv.get("verbose")
m = argv.get("m", 6) # _number of rows
k = argv.get("k", None) # _number of odd-weight rows
maxweight = argv.get("maxweight", m)
minweight = argv.get("minweight", 1)
# these are the variables N_x
print("building xs...")
if 0:
xs = cross([(0, 1)]*m)
xs = [x for x in xs if minweight <= sum(x) <= maxweight]
prune = argv.get("prune", 0.5)
xs = [x for x in xs if random() < prune]
xs = []
N = argv.get("N", m*100)
colweight = argv.get("colweight", maxweight)
assert colweight <= m
for i in range(N):
x = [0]*m
total = 0
while total < colweight:
idx = randint(0, m-1)
if x[idx] == 0:
x[idx] = 1
total += 1
xs.append(x)
N = len(xs)
lhs = []
rhs = []
# bi-orthogonality
for a in range(m):
for b in range(a+1, m):
v = zeros2(N)
for i, x in enumerate(xs):
if x[a] == x[b] == 1:
v[i] = 1
if v.sum():
lhs.append(v)
rhs.append(0)
k = 0 # all rows must have even weight
# constrain to k _number of odd-weight rows
assert 0<=k<m
for a in range(m):
v = zeros2(N)
for i, x in enumerate(xs):
if x[a] == 1:
v[i] = 1
lhs.append(v)
if a<k:
rhs.append(1)
else:
rhs.append(0)
logops = argv.logops
A = array2(lhs)
rhs = array2(rhs)
#print(shortstr(A))
print("solve...")
B = pseudo_inverse(A)
soln = dot2(B, rhs)
if not eq2(dot2(A, soln), rhs):
print("no solution")
return
if verbose:
print("soln:")
print(shortstr(soln))
soln.shape = (N, 1)
rhs.shape = A.shape[0], 1
K = array2(list(find_kernel(A)))
print("kernel:", K.shape)
if len(K)==0:
return
#print(K)
#print( dot2(A, K.transpose()))
#sols = []
#for v in span(K):
best = None
density = 1.0
size = 99*N
trials = argv.get("trials", 1024)
count = 0
print("trials...")
for trial in range(trials):
u = rand2(len(K), 1)
v = dot2(K.transpose(), u)
#print(v)
v = (v+soln)%2
assert eq2(dot2(A, v), rhs)
if v.sum() >= size:
continue
if v.sum() < m:
continue
if v.sum():
print(v.sum(), end=" ", flush=True)
size = v.sum()
if logops is not None and size != 2*m+logops:
continue
Gt = []
for i, x in enumerate(xs):
if v[i]:
Gt.append(x)
Gt = array2(Gt)
G = Gt.transpose()
if dot2(G, Gt).sum() != 0:
# not self-dual
print(shortstr(dot2(G, Gt)))
assert 0
return
#if G.shape[1]<m:
# continue
if 0 in G.sum(1):
print(".", end="", flush=True)
continue
#print(shortstr(G))
# for g in G:
# print(shortstr(g), g.sum())
# print()
_density = float(G.sum()) / (G.shape[0]*G.shape[1])
#if best is None or _density < density:
if best is None or G.shape[1] <= size:
best = G
size = G.shape[1]
density = _density
if 0:
#sols.append(G)
Gx = even_rows(G)
assert is_morthogonal(Gx, 3)
if len(Gx)==0:
continue
GGx = array2(list(span(Gx)))
assert is_morthogonal(GGx, 3)
count += 1
print("found %d solutions" % count)
if best is None:
return
G = best
#print(shortstr(G))
f = open("selfdual.ldpc", "w")
for spec in ["Hx =", "Hz ="]:
print(spec, file=f)
for g in G:
print(shortstr(g), file=f)
f.close()
print()
print("density:", density)
print("shape:", G.shape)
if 0:
B = pseudo_inverse(A)
v = dot2(B, rhs)
print("B:")
print(shortstr(B))
print("v:")
print(shortstr(v))
assert eq2(dot2(B, v), rhs)
def search_extend():
# Extend the checks of a random code to make it triorthogonal.
# Based on the search function above.
verbose = argv.get("verbose")
m = argv.get("m", 6)
n = argv.get("n", m+2)
k = argv.get("k") # odd _numbered rows ( logical operators)
code = argv.get("code", "rand")
if code == "rand":
while 1:
G0 = rand2(m, n)
counts = G0.sum(0)
if min(counts)==2 and rank(G0) == m:
cols = set()
for i in range(n):
cols.add(tuple(G0[:, i]))
if len(cols) == n: # no repeated cols
break
elif code == "toric":
G0 = parse("""
11.11...
.111..1.
1...11.1
""") # l=2 toric code X logops + X stabs
l = argv.get("l", 3)
G0 = build_toric(l)
m, n = G0.shape
else:
return
code = Code(G0, check=False)
print(shortstr(G0))
print("is_triorthogonal:", code.is_triorthogonal())
# these are the variables N_x
xs = list(cross([(0, 1)]*m))
N = len(xs)
lookup = {}
for i, x in enumerate(xs):
lookup[x] = i
lhs = []
rhs = []
taken = set()
for i in range(n):
x = G0[:, i]
idx = lookup[tuple(x)]
assert idx not in taken
taken.add(idx)
if verbose:
for idx in range(N):
print(idx, xs[idx], "*" if idx in taken else "")
for idx in taken:
v = zeros2(N)
v[idx] = 1
lhs.append(v)
rhs.append(1)
# bi-orthogonality
for a in range(m):
for b in range(a+1, m):
v = zeros2(N)
for i, x in enumerate(xs):
if x[a] == x[b] == 1:
v[i] += 1
assert v.sum()
lhs.append(v)
rhs.append(0)
# tri-orthogonality
for a in range(m):
for b in range(a+1, m):
for c in range(b+1, m):
v = zeros2(N)
for i, x in enumerate(xs):
if x[a] == x[b] == x[c] == 1:
v[i] += 1
assert v.sum()
lhs.append(v)
rhs.append(0)
# dissallow columns with weight <= 1
for i, x in enumerate(xs):
if sum(x)<=1:
v = zeros2(N)
v[i] = 1
lhs.append(v)
rhs.append(0)
if k is not None:
# constrain to k _number of odd-weight rows
assert 0<=k<m
for a in range(m):
v = zeros2(N)
for i, x in enumerate(xs):
if x[a] == 1:
v[i] = 1
lhs.append(v)
if a<k:
rhs.append(1)
else:
rhs.append(0)
A = array2(lhs)
rhs = array2(rhs)
if verbose:
print("lhs:")
print(shortstr(A))
print("rhs:")
print(shortstr(rhs))
B = pseudo_inverse(A)
soln = dot2(B, rhs)
if not eq2(dot2(A, soln), rhs):
print("no solution")
return
if verbose:
print("soln:")
print(shortstr(soln))
soln.shape = (N, 1)
rhs.shape = A.shape[0], 1
K = array2(list(find_kernel(A)))
best = None
density = 1.0
size = 9999*n
trials = argv.get("trials", 1024)
count = 0
for trial in range(trials):
u = rand2(len(K), 1)
v = dot2(K.transpose(), u)
#print(v)
assert dot2(A, v).sum()==0
#if v.sum() != n:
# continue
assert v[0]==0
v = (v+soln)%2
assert eq2(dot2(A, v), rhs)
Gt = list(G0.transpose())
for i, x in enumerate(xs):
if v[i] and not i in taken:
Gt.append(x)
if not Gt:
continue
Gt = array2(Gt)
G = Gt.transpose()
if verbose:
print("G0")
print(shortstr(G0))
print("solution:")
print(shortstr(G))
assert is_morthogonal(G, 3)
if G.shape[1]<m:
continue
if 0 in G.sum(1):
continue
#print(shortstr(G))
# for g in G:
# print(shortstr(g), g.sum())
# print()
_density = float(G.sum()) / (G.shape[0]*G.shape[1])
#if best is None or _density < density:
if best is None or G.shape[1] < size:
best = G
density = _density
size = G.shape[1]
if 0:
#sols.append(G)
Gx = even_rows(G)
assert is_morthogonal(Gx, 3)
if len(Gx)==0:
continue
GGx = array2(list(span(Gx)))
assert is_morthogonal(GGx, 3)
count += 1
print("found %d solutions" % count)
G = best
#print(shortstr(G))
for g in G:
print(shortstr(g), g.sum())
print()
print("density:", density)
