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test_circuit.py
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# pylint: disable=invalid-name
import os
import sys
from functools import partial
import numpy as np
import opt_einsum as oem
import pytest
from pytest_lazyfixture import lazy_fixture as lf
# see https://stackoverflow.com/questions/56307329/how-can-i-parametrize-tests-to-run-with-different-fixtures-in-pytest
thisfile = os.path.abspath(__file__)
modulepath = os.path.dirname(os.path.dirname(thisfile))
sys.path.insert(0, modulepath)
import tensorcircuit as tc
@pytest.mark.parametrize("backend", [lf("npb"), lf("cpb")])
def test_wavefunction(backend):
qc = tc.Circuit(2)
qc.unitary(
0,
1,
unitary=tc.gates.Gate(np.arange(16).reshape(2, 2, 2, 2).astype(np.complex64)),
)
assert np.real(qc.wavefunction()[2]) == 8
qc = tc.Circuit(2)
qc.unitary(
1,
0,
unitary=tc.gates.Gate(np.arange(16).reshape(2, 2, 2, 2).astype(np.complex64)),
)
qc.wavefunction()
assert np.real(qc.wavefunction()[2]) == 4
qc = tc.Circuit(2)
qc.unitary(
0, unitary=tc.gates.Gate(np.arange(4).reshape(2, 2).astype(np.complex64))
)
qc.wavefunction()
assert np.real(qc.wavefunction()[2]) == 2
@pytest.mark.parametrize("backend", [lf("npb"), lf("cpb")])
def test_basics(backend):
c = tc.Circuit(2)
c.x(0)
np.testing.assert_allclose(tc.backend.numpy(c.amplitude("10")), np.array(1.0))
c.CNOT(0, 1)
np.testing.assert_allclose(tc.backend.numpy(c.amplitude("11")), np.array(1.0))
@pytest.mark.parametrize("backend", [lf("npb"), lf("cpb")])
def test_measure(backend):
c = tc.Circuit(3)
c.H(0)
c.h(1)
c.toffoli(0, 1, 2)
assert c.measure(2)[0] in [0, 1]
def test_gates_in_circuit():
c = tc.Circuit(2, inputs=np.eye(2**2))
c.iswap(0, 1)
ans = tc.gates.iswap_gate().tensor.reshape([4, 4])
np.testing.assert_allclose(c.state().reshape([4, 4]), ans, atol=1e-5)
def test_control_vgate():
c = tc.Circuit(2)
c.x(1)
c.crx(1, 0, theta=0.3)
np.testing.assert_allclose(
c.expectation([tc.gates._z_matrix, 0]), 0.95533645, atol=1e-5
)
def test_adjoint_gate_circuit():
c = tc.Circuit(1)
c.X(0)
c.SD(0)
np.testing.assert_allclose(c.state(), np.array([0.0, -1.0j]))
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_jittable_measure(backend):
@partial(tc.backend.jit, static_argnums=(2, 3))
def f(param, key, n=6, nlayers=3):
if key is not None:
tc.backend.set_random_state(key)
c = tc.Circuit(n)
for i in range(n):
c.H(i)
for j in range(nlayers):
for i in range(n - 1):
c.exp1(i, i + 1, theta=param[2 * j, i], hamiltonian=tc.gates._zz_matrix)
for i in range(n):
c.rx(i, theta=param[2 * j + 1, i])
return c.measure_jit(0, 1, 2, with_prob=True)
if tc.backend.name == "tensorflow":
import tensorflow as tf
print(f(tc.backend.ones([6, 6]), None))
print(f(tc.backend.ones([6, 6]), None))
print(f(tc.backend.ones([6, 6]), tf.random.Generator.from_seed(23)))
print(f(tc.backend.ones([6, 6]), tf.random.Generator.from_seed(24)))
elif tc.backend.name == "jax":
import jax
print(f(tc.backend.ones([6, 6]), jax.random.PRNGKey(23)))
print(f(tc.backend.ones([6, 6]), jax.random.PRNGKey(24)))
# As seen here, though I have tried the best, the random API is still not that consistent under jit
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_jittable_depolarizing(backend):
@tc.backend.jit
def f1(key):
n = 5
if key is not None:
tc.backend.set_random_state(key)
c = tc.Circuit(n)
for i in range(n):
c.H(i)
for i in range(n):
c.cnot(i, (i + 1) % n)
for i in range(n):
c.unitary_kraus(
[
tc.gates._x_matrix,
tc.gates._y_matrix,
tc.gates._z_matrix,
tc.gates._i_matrix,
],
i,
prob=[0.2, 0.2, 0.2, 0.4],
)
for i in range(n):
c.cz(i, (i + 1) % n)
return c.wavefunction()
@tc.backend.jit
def f2(key):
n = 5
if key is not None:
tc.backend.set_random_state(key)
c = tc.Circuit(n)
for i in range(n):
c.H(i)
for i in range(n):
c.cnot(i, (i + 1) % n)
for i in range(n):
c.unitary_kraus(
tc.channels.depolarizingchannel(0.2, 0.2, 0.2),
i,
)
for i in range(n):
c.X(i)
return c.wavefunction()
@tc.backend.jit
def f3(key):
n = 5
if key is not None:
tc.backend.set_random_state(key)
c = tc.Circuit(n)
for i in range(n):
c.H(i)
for i in range(n):
c.cnot(i, (i + 1) % n)
for i in range(n):
c.depolarizing(i, px=0.2, py=0.2, pz=0.2)
for i in range(n):
c.X(i)
return c.wavefunction()
@tc.backend.jit
def f4(key):
n = 5
if key is not None:
tc.backend.set_random_state(key)
c = tc.Circuit(n)
for i in range(n):
c.H(i)
for i in range(n):
c.cnot(i, (i + 1) % n)
for i in range(n):
c.depolarizing2(i, px=0.2, py=0.2, pz=0.2)
for i in range(n):
c.X(i)
return c.wavefunction()
@tc.backend.jit
def f5(key):
n = 5
if key is not None:
tc.backend.set_random_state(key)
c = tc.Circuit(n)
for i in range(n):
c.H(i)
for i in range(n):
c.cnot(i, (i + 1) % n)
for i in range(n):
c.unitary_kraus2(
tc.channels.depolarizingchannel(0.2, 0.2, 0.2),
i,
)
for i in range(n):
c.X(i)
return c.wavefunction()
for f in [f1, f2, f3, f4, f5]:
if tc.backend.name == "tensorflow":
import tensorflow as tf
np.testing.assert_allclose(tc.backend.norm(f(None)), 1.0, atol=1e-4)
np.testing.assert_allclose(
tc.backend.norm(f(tf.random.Generator.from_seed(23))), 1.0, atol=1e-4
)
np.testing.assert_allclose(
tc.backend.norm(f(tf.random.Generator.from_seed(24))), 1.0, atol=1e-4
)
elif tc.backend.name == "jax":
import jax
np.testing.assert_allclose(
tc.backend.norm(f(jax.random.PRNGKey(23))), 1.0, atol=1e-4
)
np.testing.assert_allclose(
tc.backend.norm(f(jax.random.PRNGKey(24))), 1.0, atol=1e-4
)
@pytest.mark.parametrize("backend", [lf("npb"), lf("cpb")])
def test_expectation(backend):
c = tc.Circuit(2)
c.H(0)
np.testing.assert_allclose(
tc.backend.numpy(c.expectation((tc.gates.z(), [0]))), 0, atol=1e-7
)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb"), lf("cpb")])
def test_exp1(backend):
@partial(tc.backend.jit, jit_compile=True)
def sf():
c = tc.Circuit(2)
xx = np.array(
[[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0]], dtype=np.complex64
)
c.exp1(0, 1, unitary=xx, theta=tc.num_to_tensor(0.2))
s = c.state()
return s
@tc.backend.jit
def s1f():
c = tc.Circuit(2)
xx = np.array(
[[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0]], dtype=np.complex64
)
c.exp(0, 1, unitary=xx, theta=tc.num_to_tensor(0.2))
s1 = c.state()
return s1
s = tc.backend.numpy(sf())
s1 = tc.backend.numpy(s1f())
np.testing.assert_allclose(s, s1, atol=1e-4)
def test_complex128(highp, tfb):
c = tc.Circuit(2)
c.H(1)
c.rx(0, theta=tc.gates.num_to_tensor(1j))
c.wavefunction()
np.testing.assert_allclose(c.expectation((tc.gates.z(), [1])), 0)
# def test_qcode():
# qcode = """
# 4
# x 0
# cnot 0 1
# r 2 theta 1.0 alpha 1.57
# """
# c = tc.Circuit.from_qcode(qcode)
# assert c.measure(1)[0] == "1"
# assert c.to_qcode() == qcode[1:]
def universal_ad():
@tc.backend.jit
def forward(theta):
c = tc.Circuit(2)
c.R(0, theta=theta, alpha=0.5, phi=0.8)
return tc.backend.real(c.expectation((tc.gates.z(), [0])))
gg = tc.backend.grad(forward)
vg = tc.backend.value_and_grad(forward)
gg = tc.backend.jit(gg)
vg = tc.backend.jit(vg)
theta = tc.gates.num_to_tensor(1.0)
grad1 = gg(theta)
v2, grad2 = vg(theta)
assert grad1 == grad2
return v2, grad2
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_ad(backend):
# this amazingly shows how to code once and run in very different AD-ML engines
print(universal_ad())
def test_single_qubit():
c = tc.Circuit(1)
c.H(0)
w = c.state()[0]
np.testing.assert_allclose(w, np.array([1, 1]) / np.sqrt(2), atol=1e-4)
@pytest.mark.parametrize("backend", [lf("npb"), lf("cpb")])
def test_expectation_between_two_states(backend):
zp = np.array([1.0, 0.0])
zd = np.array([0.0, 1.0])
assert tc.expectation((tc.gates.y(), [0]), ket=zp, bra=zd) == 1j
c = tc.Circuit(3)
c.H(0)
c.ry(1, theta=tc.num_to_tensor(0.8))
c.cnot(1, 2)
state = c.wavefunction()
x1z2 = [(tc.gates.x(), [0]), (tc.gates.z(), [1])]
e1 = c.expectation(*x1z2)
e2 = tc.expectation(*x1z2, ket=state, bra=state, normalization=True)
np.testing.assert_allclose(tc.backend.numpy(e2), tc.backend.numpy(e1))
c = tc.Circuit(3)
c.H(0)
c.ry(1, theta=tc.num_to_tensor(0.8 + 0.7j))
c.cnot(1, 2)
state = c.wavefunction()
x1z2 = [(tc.gates.x(), [0]), (tc.gates.z(), [1])]
e1 = c.expectation(*x1z2) / tc.backend.norm(state) ** 2
e2 = tc.expectation(*x1z2, ket=state, normalization=True)
np.testing.assert_allclose(tc.backend.numpy(e2), tc.backend.numpy(e1))
c = tc.Circuit(2)
c.X(1)
s1 = c.state()
c2 = tc.Circuit(2)
c2.X(0)
s2 = c2.state()
c3 = tc.Circuit(2)
c3.H(1)
s3 = c3.state()
x1x2 = [(tc.gates.x(), [0]), (tc.gates.x(), [1])]
e = tc.expectation(*x1x2, ket=s1, bra=s2)
np.testing.assert_allclose(tc.backend.numpy(e), 1.0)
e2 = tc.expectation(*x1x2, ket=s3, bra=s2)
np.testing.assert_allclose(tc.backend.numpy(e2), 1.0 / np.sqrt(2))
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb"), lf("cpb")])
def test_any_inputs_state(backend):
c = tc.Circuit(2, inputs=tc.array_to_tensor(np.array([0.0, 0.0, 0.0, 1.0])))
c.X(0)
z0 = c.expectation((tc.gates.z(), [0]))
assert z0 == 1.0
c = tc.Circuit(2, inputs=tc.array_to_tensor(np.array([0.0, 0.0, 1.0, 0.0])))
c.X(0)
z0 = c.expectation((tc.gates.z(), [0]))
assert z0 == 1.0
c = tc.Circuit(2, inputs=tc.array_to_tensor(np.array([1.0, 0.0, 0.0, 0.0])))
c.X(0)
z0 = c.expectation((tc.gates.z(), [0]))
assert z0 == -1.0
c = tc.Circuit(
2,
inputs=tc.array_to_tensor(np.array([1 / np.sqrt(2), 0.0, 1 / np.sqrt(2), 0.0])),
)
c.X(0)
z0 = c.expectation((tc.gates.z(), [0]))
np.testing.assert_allclose(tc.backend.numpy(z0), 0.0, rtol=1e-4, atol=1e-4)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("cpb")])
def test_postselection(backend):
c = tc.Circuit(3)
c.H(1)
c.H(2)
c.mid_measurement(1, 1)
c.mid_measurement(2, 1)
s = c.wavefunction()
np.testing.assert_allclose(tc.backend.numpy(s[3]).real, 0.5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("cpb")])
def test_unitary(backend):
c = tc.Circuit(2, inputs=np.eye(4))
c.X(0)
c.Y(1)
answer = tc.backend.numpy(np.kron(tc.gates.x().tensor, tc.gates.y().tensor))
np.testing.assert_allclose(
tc.backend.numpy(c.wavefunction().reshape([4, 4])), answer, atol=1e-4
)
@pytest.mark.parametrize("backend", [lf("npb"), lf("cpb")])
def test_expectation_ps(backend):
c = tc.Circuit(2)
c.X(0)
r = c.expectation_ps(z=[0, 1])
np.testing.assert_allclose(tc.backend.numpy(r), -1, atol=1e-5)
c = tc.Circuit(2)
c.H(0)
r = c.expectation_ps(z=[1], x=[0])
np.testing.assert_allclose(tc.backend.numpy(r), 1, atol=1e-5)
r1 = c.expectation_ps(ps=[1, 3])
np.testing.assert_allclose(tc.backend.numpy(r1), 1, atol=1e-5)
r1 = c.expectation_ps(z=[1, 2], ps=[1, 3])
np.testing.assert_allclose(tc.backend.numpy(r1), 1, atol=1e-5)
def test_probability():
for c_cls in [tc.Circuit, tc.DMCircuit]:
c = c_cls(2)
c.h(0)
c.h(1)
np.testing.assert_allclose(
c.probability(), np.array([1, 1, 1, 1]) / 4, atol=1e-5
)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_dqas_type_circuit(backend):
eye = tc.gates.i().tensor
x = tc.gates.x().tensor
y = tc.gates.y().tensor
z = tc.gates.z().tensor
def f(params, structures):
paramsc = tc.backend.cast(params, dtype="complex64")
structuresc = tc.backend.softmax(structures, axis=-1)
structuresc = tc.backend.cast(structuresc, dtype="complex64")
c = tc.Circuit(5)
for i in range(5):
c.H(i)
for j in range(2):
for i in range(4):
c.cz(i, i + 1)
for i in range(5):
c.any(
i,
unitary=structuresc[i, j, 0]
* (
tc.backend.cos(paramsc[i, j, 0]) * eye
+ tc.backend.sin(paramsc[i, j, 0]) * x
)
+ structuresc[i, j, 1]
* (
tc.backend.cos(paramsc[i, j, 1]) * eye
+ tc.backend.sin(paramsc[i, j, 1]) * y
)
+ structuresc[i, j, 2]
* (
tc.backend.cos(paramsc[i, j, 2]) * eye
+ tc.backend.sin(paramsc[i, j, 2]) * z
),
)
return tc.backend.real(c.expectation([tc.gates.z(), (2,)]))
structures = tc.array_to_tensor(
np.random.normal(size=[16, 5, 2, 3]), dtype="float32"
)
params = tc.array_to_tensor(np.random.normal(size=[5, 2, 3]), dtype="float32")
vf = tc.backend.vmap(f, vectorized_argnums=(1,))
np.testing.assert_allclose(vf(params, structures).shape, [16])
vvag = tc.backend.vvag(f, argnums=0, vectorized_argnums=1)
vvag = tc.backend.jit(vvag)
value, grad = vvag(params, structures)
np.testing.assert_allclose(value.shape, [16])
np.testing.assert_allclose(grad.shape, [5, 2, 3])
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_mixed_measurement_circuit(backend):
n = 4
def f(params, structures):
structuresc = tc.backend.cast(structures, dtype="complex64")
c = tc.Circuit(n)
for i in range(n):
c.H(i)
for j in range(2):
for i in range(n):
c.cnot(i, (i + 1) % n)
for i in range(n):
c.rz(i, theta=params[j, i])
obs = []
for i in range(n):
obs.append(
[
tc.gates.Gate(
sum(
[
structuresc[i, k] * g.tensor
for k, g in enumerate(tc.gates.pauli_gates)
]
)
),
(i,),
]
)
loss = c.expectation(*obs, reuse=False)
return tc.backend.real(loss)
# measure X0 to X3
structures = tc.backend.cast(tc.backend.eye(n), "int32")
structures = tc.backend.onehot(structures, num=4)
f_vvag = tc.backend.jit(tc.backend.vvag(f, vectorized_argnums=1, argnums=0))
v, g = f_vvag(tc.backend.ones([2, n], dtype="float32"), structures)
np.testing.assert_allclose(
v,
np.array(
[
0.157729,
0.157729,
0.157728,
0.085221,
]
),
atol=1e-5,
)
np.testing.assert_allclose(
g[0],
np.array([-0.378372, -0.624019, -0.491295, -0.378372]),
atol=1e-5,
)
def test_circuit_add_demo():
# to be refactored for better API
c = tc.Circuit(2)
c.x(0)
c2 = tc.Circuit(2, mps_inputs=c.quvector())
c2.X(0)
answer = np.array([1.0, 0, 0, 0])
np.testing.assert_allclose(c2.wavefunction(), answer, atol=1e-4)
c3 = tc.Circuit(2)
c3.X(0)
c3.replace_mps_inputs(c.quvector())
np.testing.assert_allclose(c3.wavefunction(), answer, atol=1e-4)
def test_circuit_replace_inputs():
n = 3
c = tc.Circuit(n, inputs=np.zeros([2**n]))
for i in range(n):
c.H(i)
evenstate = np.ones([2**n])
evenstate /= np.linalg.norm(evenstate)
c.replace_inputs(evenstate)
for i in range(n):
np.testing.assert_allclose(c.expectation_ps(z=[i]), 1.0, atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_circuit_matrix(backend):
c = tc.Circuit(2)
c.x(1)
c.cnot(0, 1)
np.testing.assert_allclose(c.matrix()[3], np.array([0.0, 0.0, 0.0, 1.0]), atol=1e-5)
np.testing.assert_allclose(c.state(), np.array([0, 1.0, 0, 0]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_circuit_split(backend):
n = 4
def f(param, max_singular_values=None, max_truncation_err=None, fixed_choice=None):
if (max_singular_values is None) and (max_truncation_err is None):
split = None
else:
split = {
"max_singular_values": max_singular_values,
"max_truncation_err": max_truncation_err,
"fixed_choice": fixed_choice,
}
c = tc.Circuit(
n,
split=split,
)
for i in range(n):
c.H(i)
for j in range(2):
for i in range(n - 1):
c.exp1(i, i + 1, theta=param[2 * j, i], hermitian=tc.gates._zz_matrix)
for i in range(n):
c.rx(i, theta=param[2 * j + 1, i])
loss = c.expectation(
(
tc.gates.z(),
[1],
),
(
tc.gates.z(),
[2],
),
)
return tc.backend.real(loss)
s1 = f(tc.backend.ones([4, n]))
s2 = f(tc.backend.ones([4, n]), max_truncation_err=1e-5)
s3 = f(tc.backend.ones([4, n]), max_singular_values=2, fixed_choice=1)
np.testing.assert_allclose(s1, s2, atol=1e-5)
np.testing.assert_allclose(s1, s3, atol=1e-5)
f_jit = tc.backend.jit(f, static_argnums=(1, 2, 3))
s1 = f_jit(tc.backend.ones([4, n]))
# s2 = f_jit(tc.backend.ones([4, n]), max_truncation_err=1e-5) # doesn't work now
# this cannot be done anyway, since variable size tensor network will fail opt einsum
s3 = f_jit(tc.backend.ones([4, n]), max_singular_values=2, fixed_choice=1)
# np.testing.assert_allclose(s1, s2, atol=1e-5)
np.testing.assert_allclose(s1, s3, atol=1e-5)
f_vg = tc.backend.jit(
tc.backend.value_and_grad(f, argnums=0), static_argnums=(1, 2, 3)
)
s1, g1 = f_vg(tc.backend.ones([4, n]))
s3, g3 = f_vg(tc.backend.ones([4, n]), max_singular_values=2, fixed_choice=1)
np.testing.assert_allclose(s1, s3, atol=1e-5)
# DONE(@refraction-ray): nan on jax backend?
# i see, complex value SVD is not supported on jax for now :)
# I shall further customize complex SVD, finally it has applications
# tf 2.6.2 also doesn't support complex valued SVD AD, weird...
# if tc.backend.name == "tensorflow":
np.testing.assert_allclose(g1, g3, atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_gate_split(backend):
n = 4
def f(param, max_singular_values=None, max_truncation_err=None, fixed_choice=None):
if (max_singular_values is None) and (max_truncation_err is None):
split = None
else:
split = {
"max_singular_values": max_singular_values,
"max_truncation_err": max_truncation_err,
"fixed_choice": fixed_choice,
}
c = tc.Circuit(
n,
)
for i in range(n):
c.H(i)
for j in range(2):
for i in range(n - 1):
c.exp1(
i,
i + 1,
theta=param[2 * j, i],
unitary=tc.gates._zz_matrix,
split=split,
)
for i in range(n):
c.rx(i, theta=param[2 * j + 1, i])
loss = c.expectation(
(
tc.gates.x(),
[1],
),
)
return tc.backend.real(loss)
s1 = f(tc.backend.ones([4, n]))
s2 = f(tc.backend.ones([4, n]), max_truncation_err=1e-5)
s3 = f(tc.backend.ones([4, n]), max_singular_values=2, fixed_choice=1)
np.testing.assert_allclose(s1, s2, atol=1e-5)
np.testing.assert_allclose(s1, s3, atol=1e-5)
def test_toqir():
split = {
"max_singular_values": 2,
"fixed_choice": 1,
}
c = tc.Circuit(3)
c.H(0)
c.rx(1, theta=tc.array_to_tensor(0.7))
c.exp1(
0, 1, unitary=tc.gates._zz_matrix, theta=tc.array_to_tensor(-0.2), split=split
)
z1 = c.expectation((tc.gates.z(), [1]))
qirs = c.to_qir()
c = tc.Circuit.from_qir(qirs, circuit_params={"nqubits": 3})
assert len(c._nodes) == 7
z2 = c.expectation((tc.gates.z(), [1]))
np.testing.assert_allclose(z1, z2, atol=1e-5)
c.append_from_qir(qirs)
z3 = c.expectation((tc.gates.z(), [1]))
assert len(c._nodes) == 11
np.testing.assert_allclose(z3, 0.202728, atol=1e-5)
def test_vis_tex():
c = tc.Circuit(3)
for i in range(3):
c.H(i)
for i in range(3):
c.any(i, (i + 1) % 3, unitary=tc.backend.ones([4, 4]), name="hihi")
c.any(2, unitary=tc.backend.ones([2, 2]), name="invisible")
c.cz(1, 2)
c.any(1, 0, 2, unitary=tc.backend.ones([8, 8]), name="ccha")
c.z(2)
c.cnot(0, 1)
c.cz(2, 1)
print(c.vis_tex(init=["0", "1", ""], measure=["x", "y", "z"]))
def test_debug_contract():
n = 10
d = 4
try:
import cotengra # pylint: disable=unused-import
except ImportError:
pytest.skip("cotengra is not installed")
@tc.set_function_contractor(
"custom_stateful",
optimizer=oem.RandomGreedy,
max_time=10,
max_repeats=64,
minimize="size",
debug_level=2,
contraction_info=True,
)
def small_tn():
param = tc.backend.ones([2 * d, n])
c = tc.Circuit(n)
c = tc.templates.blocks.example_block(c, param, nlayers=d)
return c.state()
np.testing.assert_allclose(small_tn(), np.zeros([2**n]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_teleportation(backend):
key = tc.backend.get_random_state(42)
@tc.backend.jit
def f(key):
tc.backend.set_random_state(key)
c = tc.Circuit(2)
c.H(0)
r = c.cond_measurement(0)
c.conditional_gate(r, [tc.gates.i(), tc.gates.x()], 1)
return r, c.expectation([tc.gates.z(), [1]])
keys = []
for _ in range(6):
key, subkey = tc.backend.random_split(key)
keys.append(subkey)
rs = [f(k) for k in keys]
for r, e in rs:
if tc.backend.numpy(r) > 0.5:
np.testing.assert_allclose(e, -1, atol=1e-5)
else:
np.testing.assert_allclose(e, 1, atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_append_circuit(backend):
c = tc.Circuit(2)
c.cnot(0, 1)
c1 = tc.Circuit(2)
c1.x(0)
c.append(c1)
np.testing.assert_allclose(c.expectation_ps(z=[1]), 1.0)
c = tc.Circuit(2)
c.cnot(0, 1)
c1 = tc.Circuit(2)
c1.x(0)
c.prepend(c1)
np.testing.assert_allclose(c.expectation_ps(z=[1]), -1.0)
c = tc.Circuit(2)
c1 = tc.Circuit(1)
c1.x(0)
c.append(c1, [1])
np.testing.assert_allclose(c.state(), [0, 1, 0, 0])
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_apply_mpo_gate(backend):
gate = tc.gates.multicontrol_gate(tc.gates._x_matrix, ctrl=[1, 0])
ans = np.array(
[
[1.0, 0, 0, 0, 0, 0, 0, 0],
[0, 1.0, 0, 0, 0, 0, 0, 0],
[0, 0, 1.0, 0, 0, 0, 0, 0],
[0, 0, 0, 1.0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1.0, 0, 0],
[0, 0, 0, 0, 1.0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1.0, 0],
[0, 0, 0, 0, 0, 0, 0, 1.0],
]
)
c = tc.Circuit(3)
c.X(0)
c.mpo(0, 1, 2, mpo=gate.copy())
np.testing.assert_allclose(c.expectation([tc.gates.z(), [2]]), -1, atol=1e-5)
c = tc.Circuit(3)
c.X(1)
c.mpo(0, 1, 2, mpo=gate.copy())
np.testing.assert_allclose(c.expectation([tc.gates.z(), [2]]), 1, atol=1e-5)
np.testing.assert_allclose(gate.eval_matrix(), ans, atol=1e-5)
def test_apply_multicontrol_gate():
c = tc.Circuit(3)
c.X(2)
c.multicontrol(0, 2, 1, ctrl=[0, 1], unitary=tc.gates._x_matrix)
np.testing.assert_allclose(c.expectation([tc.gates.z(), [1]]), -1, atol=1e-5)
c = tc.Circuit(3)
c.X(0)
c.multicontrol(0, 2, 1, ctrl=[0, 1], unitary=tc.gates._x_matrix)
np.testing.assert_allclose(c.expectation([tc.gates.z(), [1]]), 1, atol=1e-5)
c = tc.Circuit(4)
c.X(0)
c.X(2)
c.multicontrol(0, 1, 2, 3, ctrl=[1, 0], unitary=tc.gates.swap())
np.testing.assert_allclose(c.expectation([tc.gates.z(), [3]]), -1, atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_circuit_quoperator(backend):
c = tc.Circuit(3)
c.x(0)
c.cnot(0, 1)
c.cz(1, 2)
c.y(2)
c.exp1(0, 2, theta=1.0, unitary=tc.gates._xx_matrix)
c.H(1)
c.multicontrol(0, 2, 1, ctrl=[1, 0], unitary=tc.gates.z())
qo = c.quoperator()
np.testing.assert_allclose(qo.eval_matrix(), c.matrix(), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_qir2cirq(backend):
try:
import cirq
except ImportError:
pytest.skip("cirq is not installed")
n = 6
c = tc.Circuit(n)
for i in range(n):
c.H(i)
zz = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
for i in range(n):
c.exp(
i,
(i + 1) % n,
theta=tc.array_to_tensor(np.random.uniform()),
unitary=tc.array_to_tensor(zz),
name="zz",
)
c.exp1(
1, 3, theta=tc.array_to_tensor(0.0j), unitary=tc.array_to_tensor(zz), name="zz"
)
c.fredkin(0, 1, 2)
c.cswap(1, 2, 3)
c.ccnot(1, 2, 3)
c.cx(2, 3)
c.swap(0, 1)
c.iswap(0, 1)
c.iswap(1, 3, theta=-1.9)
c.toffoli(0, 1, 2)
c.s(1)
c.t(1)
c.sd(1)
c.td(1)
c.x(2)
c.y(2)
c.z(2)
c.wroot(3)
c.cnot(0, 1)
c.cy(0, 1)
c.cz(0, 1)
c.oy(4, 3)
c.oz(4, 3)
c.ox(4, 3)
c.oy(4, 3)
c.oz(4, 3)
c.ox(3, 4)
c.phase(2, theta=0.3)
c.cphase(1, 0, theta=-1.2)
c.rxx(0, 2, theta=0.9)
c.ryy(1, 4, theta=-2.0)
c.rzz(1, 3, theta=0.5)
c.u(2, theta=0, lbd=4.6, phi=-0.3)
c.cu(4, 1, theta=1.2)
c.rx(1, theta=tc.array_to_tensor(np.random.uniform()))
c.r(5, theta=tc.array_to_tensor(np.random.uniform()))
c.cr(
1,
2,
theta=tc.array_to_tensor(np.random.uniform()),
alpha=tc.array_to_tensor(np.random.uniform()),
phi=tc.array_to_tensor(np.random.uniform()),
)
c.ry(1, theta=tc.array_to_tensor(np.random.uniform()))
c.rz(1, theta=tc.array_to_tensor(np.random.uniform()))
c.crz(2, 3, theta=tc.array_to_tensor(np.random.uniform()))
c.crx(5, 3, theta=tc.array_to_tensor(np.random.uniform()))
c.cry(1, 3, theta=tc.array_to_tensor(np.random.uniform()))
c.orx(5, 3, theta=tc.array_to_tensor(np.random.uniform()))
c.ory(5, 3, theta=tc.array_to_tensor(np.random.uniform()))
c.orz(5, 3, theta=tc.array_to_tensor(np.random.uniform()))
c.any(1, 3, unitary=tc.array_to_tensor(np.reshape(zz, [2, 2, 2, 2])))
gate = tc.gates.multicontrol_gate(
tc.array_to_tensor(tc.gates._x_matrix), ctrl=[1, 0]
)
c.mpo(0, 1, 2, mpo=gate.copy())
c.multicontrol(
0,
2,
4,
1,
5,
ctrl=[0, 1, 0],
unitary=tc.array_to_tensor(tc.gates._zz_matrix),
name="zz",
)
tc_unitary = c.matrix()
tc_unitary = np.reshape(tc_unitary, [2**n, 2**n])
cirq = c.to_cirq()
cirq_unitary = cirq.unitary()
cirq_unitary = np.reshape(cirq_unitary, [2**n, 2**n])
np.testing.assert_allclose(tc_unitary, cirq_unitary, atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_qir2qiskit(backend):
try:
import qiskit.quantum_info as qi
from tensorcircuit.translation import perm_matrix
except ImportError:
pytest.skip("qiskit is not installed")
n = 6
c = tc.Circuit(n, inputs=tc.array_to_tensor(np.eye(2**n)))
for i in range(n):
c.H(i)
zz = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
for i in range(n):
c.exp(
i,
(i + 1) % n,
theta=tc.array_to_tensor(np.random.uniform()),
unitary=tc.array_to_tensor(zz),
name="zz",
)
c.exp1(
1, 3, theta=tc.array_to_tensor(0.0j), unitary=tc.array_to_tensor(zz), name="zz"
)