diff --git a/doc/source/code-examples/advancedexamples.rst b/doc/source/code-examples/advancedexamples.rst
index 6c6d475a10..b9b5155bf5 100644
--- a/doc/source/code-examples/advancedexamples.rst
+++ b/doc/source/code-examples/advancedexamples.rst
@@ -702,6 +702,9 @@ the following script optimizes the parameters of two rotations so that the circu
 output matches a target state using the fidelity as the corresponding loss
 function.
 
+Note that, as in the following example, the rotation angles have to assume real values
+to ensure the rotational gates are representing unitary operators.
+
 .. code-block:: python
 
     import qibo
@@ -716,7 +719,7 @@ function.
 
     # Define circuit ansatz
     params = tf.Variable(
-        tf.random.uniform((2,), dtype=tf.float64).astype(tf.complex128)
+        tf.random.uniform((2,), dtype=tf.float64)
     )
     c = models.Circuit(2)
     c.add(gates.RX(0, params[0]))
@@ -729,7 +732,6 @@ function.
             fidelity = tf.math.abs(tf.reduce_sum(tf.math.conj(target_state) * final_state))
             loss = 1 - fidelity
         grads = tape.gradient(loss, params)
-        grads = tf.math.real(grads)
         optimizer.apply_gradients(zip([grads], [params]))
 
 
@@ -763,7 +765,6 @@ For example:
             fidelity = tf.math.abs(tf.reduce_sum(tf.math.conj(target_state) * final_state))
             loss = 1 - fidelity
         grads = tape.gradient(loss, params)
-        grads = tf.math.real(grads)
         optimizer.apply_gradients(zip([grads], [params]))
 
     for _ in range(nepochs):
diff --git a/poetry.lock b/poetry.lock
index f30dbe748e..dee4ecc7e0 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -35,13 +35,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.3.0"
+version = "4.4.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
-    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
 ]
 
 [package.dependencies]
@@ -121,13 +121,13 @@ tests-no-zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy"
 
 [[package]]
 name = "autoray"
-version = "0.6.9"
+version = "0.6.12"
 description = "Abstract your array operations."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "autoray-0.6.9-py3-none-any.whl", hash = "sha256:5685759f6e705f33cc3c614e57a55ba4822dc601969511465985159f2ea1573f"},
-    {file = "autoray-0.6.9.tar.gz", hash = "sha256:9f41759f6a286bc280c4f6aece436da1c87ce75eb00efe7dc7319860c43654fa"},
+    {file = "autoray-0.6.12-py3-none-any.whl", hash = "sha256:3ed7a4abcec052bcbb4f0447c426d0a0b9b9fa03ab71e76eaa77747ca43ac3e2"},
+    {file = "autoray-0.6.12.tar.gz", hash = "sha256:721328aa06fc3577155d988052614a7b4bd6e4d01b340695344031ee4abd2a1e"},
 ]
 
 [package.extras]
@@ -659,13 +659,13 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
 [[package]]
 name = "cotengra"
-version = "0.6.0"
+version = "0.6.2"
 description = "Hyper optimized contraction trees for large tensor networks and einsums."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "cotengra-0.6.0-py3-none-any.whl", hash = "sha256:39b564cecc71b4b54b555854aacb06c32c74555dbcfb4c102634130709b043a0"},
-    {file = "cotengra-0.6.0.tar.gz", hash = "sha256:dbd2d682d5eb40177246e8646dbabf0235650ca11f4986967b6ea9ee63549273"},
+    {file = "cotengra-0.6.2-py3-none-any.whl", hash = "sha256:524711515cfbaae22ae56a8c6bf3339dfe50485b9b615de52f8f7c330847b196"},
+    {file = "cotengra-0.6.2.tar.gz", hash = "sha256:a56b921d0339d1397e925a7b69029d3301636d09b99b509368751753bda39d24"},
 ]
 
 [package.dependencies]
@@ -673,68 +673,68 @@ autoray = "*"
 
 [package.extras]
 docs = ["astroid (<3.0.0)", "furo", "ipython (!=8.7.0)", "myst-nb", "setuptools-scm", "sphinx (>=2.0)", "sphinx-autoapi", "sphinx-copybutton", "sphinx-design"]
-recommended = ["cotengrust", "cytoolz", "kahypar", "networkx", "numpy", "opt-einsum", "optuna", "ray", "tqdm"]
+recommended = ["cotengrust (>=0.1.3)", "cytoolz", "kahypar", "networkx", "numpy", "opt-einsum", "optuna", "ray", "tqdm"]
 test = ["altair", "baytune", "chocolate", "dask", "distributed", "kahypar", "matplotlib", "networkx", "nevergrad", "numpy", "opt-einsum", "pytest", "seaborn", "skopt"]
 
 [[package]]
 name = "coverage"
-version = "7.5.1"
+version = "7.5.3"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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 python-versions = "*"
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 [[package]]
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 optional = false
 python-versions = "*"
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 [[package]]
 name = "fsspec"
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 description = "File-system specification"
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 adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
-devel = ["pytest", "pytest-cov"]
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 sftp = ["paramiko"]
 smb = ["smbprotocol"]
 ssh = ["paramiko"]
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 tqdm = ["tqdm"]
 
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 [[package]]
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@@ -1842,21 +1845,21 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pa
 
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 [package.extras]
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 [[package]]
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 [[package]]
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 [package.dependencies]
@@ -2378,12 +2382,15 @@ cycler = ">=0.10"
 fonttools = ">=4.22.0"
 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
 kiwisolver = ">=1.3.1"
-numpy = ">=1.21"
+numpy = ">=1.23"
 packaging = ">=20.0"
 pillow = ">=8"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
+[package.extras]
+dev = ["meson-python (>=0.13.1)", "numpy (>=1.25)", "pybind11 (>=2.6)", "setuptools (>=64)", "setuptools_scm (>=7)"]
+
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.7"
@@ -2898,13 +2905,13 @@ files = [
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.4.127"
+version = "12.5.40"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
 
 [[package]]
@@ -3035,7 +3042,6 @@ optional = false
 python-versions = ">=3.9"
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@@ -3248,13 +3253,13 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "4.2.1"
+version = "4.2.2"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
@@ -3290,13 +3295,13 @@ files = [
 
 [[package]]
 name = "prompt-toolkit"
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+version = "3.0.45"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
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 ]
 
 [package.dependencies]
@@ -3745,72 +3750,84 @@ six = ">=1.5"
 
 [[package]]
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-version = "1.16"
+version = "1.17"
 description = "Python wrapper around rapidjson"
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 python-versions = ">=3.6"
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 ]
 
 [[package]]
@@ -4031,7 +4048,7 @@ toml = ">=0.10.2,<0.11.0"
 
 [[package]]
 name = "qibojit"
-version = "0.1.4"
+version = "0.1.5"
 description = "Simulation tools based on numba and cupy."
 optional = false
 python-versions = "^3.9,<3.13"
@@ -4041,14 +4058,18 @@ develop = false
 [package.dependencies]
 numba = ">=0.59.0"
 psutil = "^5.9.5"
-qibo = {git = "https://github.com/qiboteam/qibo.git"}
+qibo = "^0.2.8"
 scipy = "^1.10.1"
 
+[package.extras]
+cupy = ["cupy-cuda12x (>=13.1.0,<14.0.0)"]
+cuquantum = ["cuquantum-python-cu12 (>=23.10.0,<24.0.0)"]
+
 [package.source]
 type = "git"
 url = "https://github.com/qiboteam/qibojit.git"
-reference = "clifford_simulator_numba"
-resolved_reference = "835bcd661286647f790f3cd3e8ddd2330147c1e8"
+reference = "HEAD"
+resolved_reference = "683a19467797c8a4d7deaf1a3a1f10a1baca66ca"
 
 [[package]]
 name = "qibotn"
@@ -4060,7 +4081,7 @@ files = []
 develop = false
 
 [package.dependencies]
-qibo = {git = "https://github.com/qiboteam/qibo.git"}
+qibo = "^0.2.8"
 quimb = {version = "^1.6.0", extras = ["tensor"]}
 
 [package.extras]
@@ -4070,17 +4091,17 @@ cuda = ["cupy-cuda11x (>=11.6.0,<12.0.0)", "cuquantum-python-cu11 (>=23.3.0,<24.
 type = "git"
 url = "https://github.com/qiboteam/qibotn.git"
 reference = "HEAD"
-resolved_reference = "6814fdbe19df19678299f9a4f662410256762ac1"
+resolved_reference = "abb0e363cedfcc2c3f522d360e04ac6f2b5a24f1"
 
 [[package]]
 name = "quimb"
-version = "1.8.0"
+version = "1.8.1"
 description = "Quantum information and many-body library."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4119,13 +4140,13 @@ sphinx = ">=1.3.1"
 
 [[package]]
 name = "requests"
-version = "2.31.0"
+version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
 ]
 
 [package.dependencies]
@@ -4307,78 +4328,81 @@ files = [
 
 [[package]]
 name = "scikit-learn"
-version = "1.4.2"
+version = "1.5.0"
 description = "A set of python modules for machine learning and data mining"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
 joblib = ">=1.2.0"
 numpy = ">=1.19.5"
 scipy = ">=1.6.0"
-threadpoolctl = ">=2.0.0"
+threadpoolctl = ">=3.1.0"
 
 [package.extras]
-benchmark = ["matplotlib (>=3.3.4)", "memory-profiler (>=0.57.0)", "pandas (>=1.1.5)"]
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 examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"]
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 [[package]]
 name = "scipy"
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 description = "Fundamental algorithms for scientific computing in Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "scipy-1.13.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a014c2b3697bde71724244f63de2476925596c24285c7a637364761f8710891c"},
+    {file = "scipy-1.13.1-cp39-cp39-win_amd64.whl", hash = "sha256:392e4ec766654852c25ebad4f64e4e584cf19820b980bc04960bca0b0cd6eaa2"},
+    {file = "scipy-1.13.1.tar.gz", hash = "sha256:095a87a0312b08dfd6a6155cbbd310a8c51800fc931b8c0b84003014b874ed3c"},
 ]
 
 [package.dependencies]
@@ -4720,17 +4744,17 @@ numpy = "*"
 
 [[package]]
 name = "sympy"
-version = "1.12"
+version = "1.12.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
-    {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
+    {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"},
+    {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
 ]
 
 [package.dependencies]
-mpmath = ">=0.19"
+mpmath = ">=1.1.0,<1.4.0"
 
 [[package]]
 name = "tabulate"
@@ -4761,17 +4785,18 @@ files = [
 
 [[package]]
 name = "tenacity"
-version = "8.2.3"
+version = "8.3.0"
 description = "Retry code until it succeeds"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "tenacity-8.2.3-py3-none-any.whl", hash = "sha256:ce510e327a630c9e1beaf17d42e6ffacc88185044ad85cf74c0a8887c6a0f88c"},
-    {file = "tenacity-8.2.3.tar.gz", hash = "sha256:5398ef0d78e63f40007c1fb4c0bff96e1911394d2fa8d194f77619c05ff6cc8a"},
+    {file = "tenacity-8.3.0-py3-none-any.whl", hash = "sha256:3649f6443dbc0d9b01b9d8020a9c4ec7a1ff5f6f3c6c8a036ef371f573fe9185"},
+    {file = "tenacity-8.3.0.tar.gz", hash = "sha256:953d4e6ad24357bceffbc9707bc74349aca9d245f68eb65419cf0c249a1949a2"},
 ]
 
 [package.extras]
-doc = ["reno", "sphinx", "tornado (>=4.5)"]
+doc = ["reno", "sphinx"]
+test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
 [[package]]
 name = "tensorboard"
@@ -4964,13 +4989,13 @@ files = [
 
 [[package]]
 name = "tomlkit"
-version = "0.12.4"
+version = "0.12.5"
 description = "Style preserving TOML library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tomlkit-0.12.4-py3-none-any.whl", hash = "sha256:5cd82d48a3dd89dee1f9d64420aa20ae65cfbd00668d6f094d7578a78efbb77b"},
-    {file = "tomlkit-0.12.4.tar.gz", hash = "sha256:7ca1cfc12232806517a8515047ba66a19369e71edf2439d0f5824f91032b6cc3"},
+    {file = "tomlkit-0.12.5-py3-none-any.whl", hash = "sha256:af914f5a9c59ed9d0762c7b64d3b5d5df007448eb9cd2edc8a46b1eafead172f"},
+    {file = "tomlkit-0.12.5.tar.gz", hash = "sha256:eef34fba39834d4d6b73c9ba7f3e4d1c417a4e56f89a7e96e090dd0d24b8fb3c"},
 ]
 
 [[package]]
@@ -5151,13 +5176,13 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.11.0"
+version = "4.12.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
-    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
+    {file = "typing_extensions-4.12.0-py3-none-any.whl", hash = "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"},
+    {file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"},
 ]
 
 [[package]]
@@ -5243,13 +5268,13 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
 
 [[package]]
 name = "widgetsnbextension"
-version = "4.0.10"
+version = "4.0.11"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"},
-    {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"},
+    {file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"},
+    {file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"},
 ]
 
 [[package]]
@@ -5350,18 +5375,18 @@ files = [
 
 [[package]]
 name = "zipp"
-version = "3.18.1"
+version = "3.19.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
-    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+    {file = "zipp-3.19.0-py3-none-any.whl", hash = "sha256:96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec"},
+    {file = "zipp-3.19.0.tar.gz", hash = "sha256:952df858fb3164426c976d9338d3961e8e8b3758e2e059e0f754b8c4262625ee"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
 qinfo = []
@@ -5371,4 +5396,4 @@ torch = ["torch"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "a81e1a9e6c7c67df5c8a2ac51a6af89d110b474c6a39d1e15c2a679f38ea8389"
+content-hash = "5bdaa2f0b5d72541884ea18fb07f539cb8f07a9fc6ab81bd2b39bccaea5d8131"
diff --git a/pyproject.toml b/pyproject.toml
index d968fe05c4..38cd627478 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "qibo"
-version = "0.2.8"
+version = "0.2.9"
 description = "A framework for quantum computing with hardware acceleration."
 authors = ["The Qibo team"]
 license = "Apache License 2.0"
@@ -65,11 +65,11 @@ cirq = "^1.1.0"
 ply = "^3.11"
 scikit-learn = "^1.2.1"
 pytest-cov = "^4.0.0"
-pylint = "^3.1.0"
+pylint = "3.1.0"
 matplotlib = "^3.7.0"
 tensorflow = { version = "^2.16.1", markers = "sys_platform == 'linux'" }
 torch = "^2.1.1"
-qibojit = { git = "https://github.com/qiboteam/qibojit.git", branch="clifford_simulator_numba" }
+qibojit = { git = "https://github.com/qiboteam/qibojit.git" }
 qibotn = { git = "https://github.com/qiboteam/qibotn.git" }
 stim = "^1.12.0"
 
@@ -88,7 +88,7 @@ optional = true
 [tool.poetry.group.cuda11.dependencies]
 cupy-cuda11x = "^12.0.0"
 cuquantum-python-cu11 = "^23.3.0"
-qibojit = { git = "https://github.com/qiboteam/qibojit.git", branch="clifford_simulator_numba" }
+qibojit = { git = "https://github.com/qiboteam/qibojit.git" }
 qibotn = { git = "https://github.com/qiboteam/qibotn.git" }
 
 [tool.poetry.group.cuda12]
@@ -97,7 +97,7 @@ optional = true
 [tool.poetry.group.cuda12.dependencies]
 cupy-cuda12x = "^12.0.0"
 cuquantum-python-cu12 = "^23.3.0"
-qibojit = { git = "https://github.com/qiboteam/qibojit.git", branch="clifford_simulator_numba" }
+qibojit = { git = "https://github.com/qiboteam/qibojit.git" }
 qibotn = { git = "https://github.com/qiboteam/qibotn.git" }
 
 [tool.poetry.extras]
diff --git a/src/qibo/__init__.py b/src/qibo/__init__.py
index b895e37b0f..bcd912f1cc 100644
--- a/src/qibo/__init__.py
+++ b/src/qibo/__init__.py
@@ -14,10 +14,12 @@
     solvers,
 )
 from qibo.backends import (
+    construct_backend,
     get_backend,
     get_device,
     get_precision,
     get_threads,
+    list_available_backends,
     matrices,
     set_backend,
     set_device,
diff --git a/src/qibo/backends/__init__.py b/src/qibo/backends/__init__.py
index a80c9932e1..a3076a5de4 100644
--- a/src/qibo/backends/__init__.py
+++ b/src/qibo/backends/__init__.py
@@ -1,4 +1,7 @@
 import os
+from importlib import import_module
+
+import numpy as np
 
 from qibo.backends.abstract import Backend
 from qibo.backends.clifford import CliffordBackend
@@ -8,66 +11,51 @@
 from qibo.backends.tensorflow import TensorflowBackend
 from qibo.config import log, raise_error
 
-
-def construct_backend(backend, **kwargs):
-    if backend == "qibojit":
-        from qibojit.backends import CupyBackend, CuQuantumBackend, NumbaBackend
-
-        platform = kwargs.get("platform")
-        if platform == "cupy":  # pragma: no cover
-            return CupyBackend()
-        elif platform == "cuquantum":  # pragma: no cover
-            return CuQuantumBackend()
-        elif platform == "numba":
-            return NumbaBackend()
-        else:  # pragma: no cover
+QIBO_NATIVE_BACKENDS = ("numpy", "tensorflow", "pytorch")
+QIBO_NON_NATIVE_BACKENDS = ("qibojit", "qibolab", "qibo-cloud-backends", "qibotn")
+
+
+class MetaBackend:
+    """Meta-backend class which takes care of loading the qibo backends."""
+
+    @staticmethod
+    def load(backend: str, **kwargs) -> Backend:
+        """Loads the native qibo backend.
+
+        Args:
+            backend (str): Name of the backend to load.
+            kwargs (dict): Additional arguments for the qibo backend.
+        Returns:
+            qibo.backends.abstract.Backend: The loaded backend.
+        """
+
+        if backend == "numpy":
+            return NumpyBackend()
+        elif backend == "tensorflow":
+            return TensorflowBackend()
+        elif backend == "pytorch":
+            return PyTorchBackend()
+        elif backend == "clifford":
+            engine = kwargs.pop("platform", None)
+            kwargs["engine"] = engine
+            return CliffordBackend(**kwargs)
+        else:
+            raise_error(
+                ValueError,
+                f"Backend {backend} is not available. The native qibo backends are {QIBO_NATIVE_BACKENDS}.",
+            )
+
+    def list_available(self) -> dict:
+        """Lists all the available native qibo backends."""
+        available_backends = {}
+        for backend in QIBO_NATIVE_BACKENDS:
             try:
-                return CupyBackend()
-            except (ModuleNotFoundError, ImportError):
-                return NumbaBackend()
-
-    elif backend == "tensorflow":
-        return TensorflowBackend()
-
-    elif backend == "pytorch":
-        return PyTorchBackend()
-
-    elif backend == "numpy":
-        return NumpyBackend()
-
-    elif backend == "qibolab":  # pragma: no cover
-        from qibolab.backends import QibolabBackend  # pylint: disable=E0401
-
-        return QibolabBackend(**kwargs)
-
-    elif backend == "qibotn":  # pragma: no cover
-
-        platform = kwargs.get("platform")
-        if platform == "cutensornet":  # pragma: no cover
-            from qibotn.backends.cutensornet import CuTensorNet  # pylint: disable=E0401
-
-            return CuTensorNet(kwargs["runcard"])
-        elif platform == "qutensornet":  # pragma: no cover
-            from qibotn.backends.quimb import QuimbBackend  # pylint: disable=E0401
-
-            return QuimbBackend(kwargs["runcard"])
-
-    elif backend == "clifford":
-        return CliffordBackend(kwargs["platform"])
-    elif backend == "qibo-client":  # pragma: no cover
-        from qibo_cloud_backends.qibo_client import (  # pylint: disable=E0401
-            QiboClientBackend,
-        )
-
-        return QiboClientBackend(**kwargs)
-    elif backend == "qiskit":  # pragma: no cover
-        from qibo_cloud_backends.qiskit_client import (  # pylint: disable=E0401
-            QiskitClientBackend,
-        )
-
-        return QiskitClientBackend(**kwargs)
-    else:  # pragma: no cover
-        raise_error(ValueError, f"Backend {backend} is not available.")
+                MetaBackend.load(backend)
+                available = True
+            except:  # pragma: no cover
+                available = False
+            available_backends[backend] = available
+        return available_backends
 
 
 class GlobalBackend(NumpyBackend):
@@ -200,3 +188,63 @@ def _check_backend(backend):
         return GlobalBackend()
 
     return backend
+
+
+def list_available_backends() -> dict:
+    """Lists all the backends that are available."""
+    available_backends = MetaBackend().list_available()
+    for backend in QIBO_NON_NATIVE_BACKENDS:
+        try:
+            module = import_module(backend.replace("-", "_"))
+            available = getattr(module, "MetaBackend")().list_available()
+        except:
+            available = False
+        available_backends.update({backend: available})
+    return available_backends
+
+
+def construct_backend(backend, **kwargs) -> Backend:
+    """Construct a generic native or non-native qibo backend.
+    Args:
+        backend (str): Name of the backend to load.
+        kwargs (dict): Additional arguments for constructing the backend.
+    Returns:
+        qibo.backends.abstract.Backend: The loaded backend.
+
+    """
+    if backend in QIBO_NATIVE_BACKENDS + ("clifford",):
+        return MetaBackend.load(backend, **kwargs)
+    elif backend in QIBO_NON_NATIVE_BACKENDS:
+        module = import_module(backend.replace("-", "_"))
+        return getattr(module, "MetaBackend").load(**kwargs)
+    else:
+        raise_error(
+            ValueError,
+            f"Backend {backend} is not available. To check which backends are installed use `qibo.list_available_backends()`.",
+        )
+
+
+def _check_backend_and_local_state(seed, backend):
+    if (
+        seed is not None
+        and not isinstance(seed, int)
+        and not isinstance(seed, np.random.Generator)
+    ):
+        raise_error(
+            TypeError, "seed must be either type int or numpy.random.Generator."
+        )
+
+    backend = _check_backend(backend)
+
+    if seed is None or isinstance(seed, int):
+        if backend.__class__.__name__ in [
+            "CupyBackend",
+            "CuQuantumBackend",
+        ]:  # pragma: no cover
+            local_state = backend.np.random.default_rng(seed)
+        else:
+            local_state = np.random.default_rng(seed)
+    else:
+        local_state = seed
+
+    return backend, local_state
diff --git a/src/qibo/backends/npmatrices.py b/src/qibo/backends/npmatrices.py
index c6e447e9ea..184ef9c546 100644
--- a/src/qibo/backends/npmatrices.py
+++ b/src/qibo/backends/npmatrices.py
@@ -64,7 +64,7 @@ def I(self, n=2):
         # _cast will take care of casting in the right dtype for all the backends
         return self._cast(self.np.eye(n, dtype=complex), dtype=self.dtype)
 
-    def Align(self, n=2):
+    def Align(self, delay, n=2):
         return self._cast(self.I(n), dtype=self.dtype)
 
     def M(self):  # pragma: no cover
diff --git a/src/qibo/backends/numpy.py b/src/qibo/backends/numpy.py
index c29bf91ea8..7b9488b8fc 100644
--- a/src/qibo/backends/numpy.py
+++ b/src/qibo/backends/numpy.py
@@ -641,19 +641,23 @@ def aggregate_shots(self, shots):
         return self.cast(shots, dtype=shots[0].dtype)
 
     def samples_to_binary(self, samples, nqubits):
-        qrange = self.np.arange(nqubits - 1, -1, -1, dtype=self.np.int32)
-        return self.np.mod(
-            self.np.right_shift(self.cast(samples[:, None], dtype="int32"), qrange), 2
-        )
+        ### This is faster just staying @ NumPy.
+        qrange = np.arange(nqubits - 1, -1, -1, dtype=np.int32)
+        samples = self.to_numpy(samples)
+        return np.mod(np.right_shift(samples[:, None], qrange), 2)
 
     def samples_to_decimal(self, samples, nqubits):
-        qrange = self.np.arange(nqubits - 1, -1, -1, dtype=self.np.int32)
+        ### This is faster just staying @ NumPy.
+        qrange = np.arange(nqubits - 1, -1, -1, dtype=np.int32)
         qrange = (2**qrange)[:, None]
-        return self.np.matmul(samples, qrange)[:, 0]
+        samples = np.asarray(samples.tolist())
+        return np.matmul(samples, qrange)[:, 0]
 
     def calculate_frequencies(self, samples):
         # Samples are a list of strings so there is no advantage in using other backends
         res, counts = np.unique(samples, return_counts=True)
+        res = self.to_numpy(res).tolist()
+        counts = self.to_numpy(counts).tolist()
         return collections.Counter(dict(zip(res, counts)))
 
     def update_frequencies(self, frequencies, probabilities, nsamples):
@@ -677,6 +681,7 @@ def sample_frequencies(self, probabilities, nshots):
         )
 
     def apply_bitflips(self, noiseless_samples, bitflip_probabilities):
+        noiseless_samples = self.cast(noiseless_samples, dtype=noiseless_samples.dtype)
         fprobs = self.cast(bitflip_probabilities, dtype="float64")
         sprobs = self.cast(np.random.random(noiseless_samples.shape), dtype="float64")
         flip_0 = self.cast(sprobs < fprobs[0], dtype=noiseless_samples.dtype)
diff --git a/src/qibo/backends/tensorflow.py b/src/qibo/backends/tensorflow.py
index f762b0e66c..3d10da18f6 100644
--- a/src/qibo/backends/tensorflow.py
+++ b/src/qibo/backends/tensorflow.py
@@ -32,9 +32,13 @@ def __init__(self):
         super().__init__()
         self.name = "tensorflow"
         os.environ["TF_CPP_MIN_LOG_LEVEL"] = str(TF_LOG_LEVEL)
+
         import tensorflow as tf  # pylint: disable=import-error
         import tensorflow.experimental.numpy as tnp  # pylint: disable=import-error
 
+        if TF_LOG_LEVEL >= 2:
+            tf.get_logger().setLevel("ERROR")
+
         tnp.experimental_enable_numpy_behavior()
         self.tf = tf
         self.np = tnp
diff --git a/src/qibo/gates/gates.py b/src/qibo/gates/gates.py
index dfbe628a00..435ce821a9 100644
--- a/src/qibo/gates/gates.py
+++ b/src/qibo/gates/gates.py
@@ -503,16 +503,16 @@ def qasm_label(self):
         return "id"
 
 
-class Align(Gate):
+class Align(ParametrizedGate):
     """Aligns proceeding qubit operations and (optionally) waits ``delay`` amount of time.
 
     Args:
-        *q (int): The qubit ID numbers.
+        q (int): The qubit ID.
         delay (int, optional): The time (in ns) for which to delay circuit execution on the specified qubits.
             Defaults to ``0`` (zero).
     """
 
-    def __init__(self, *q, delay: int = 0):
+    def __init__(self, q, delay=0, trainable=True):
         if not isinstance(delay, int):
             raise_error(
                 TypeError, f"delay must be type int, but it is type {type(delay)}."
@@ -520,13 +520,14 @@ def __init__(self, *q, delay: int = 0):
         if delay < 0.0:
             raise_error(ValueError, "Delay must not be negative.")
 
-        super().__init__()
+        super().__init__(trainable)
         self.name = "align"
-        self.delay = delay
         self.draw_label = f"A({delay})"
-        self.init_args = q
-        self.init_kwargs = {"delay": delay}
-        self.target_qubits = tuple(q)
+        self.init_args = [q]
+        self.init_kwargs = {"name": self.name, "delay": delay, "trainable": trainable}
+        self.target_qubits = (q,)
+        self._parameters = (delay,)
+        self.nparams = 1
 
 
 def _is_clifford_given_angle(angle):
diff --git a/src/qibo/models/error_mitigation.py b/src/qibo/models/error_mitigation.py
index 1b1252244b..606e822bc4 100644
--- a/src/qibo/models/error_mitigation.py
+++ b/src/qibo/models/error_mitigation.py
@@ -6,7 +6,7 @@
 from scipy.optimize import curve_fit
 
 from qibo import gates
-from qibo.backends import GlobalBackend, _check_backend
+from qibo.backends import GlobalBackend, _check_backend, _check_backend_and_local_state
 from qibo.config import raise_error
 
 
@@ -110,6 +110,7 @@ def ZNE(
     insertion_gate="CNOT",
     readout=None,
     qubit_map=None,
+    seed=None,
     backend=None,
 ):
     """Runs the Zero Noise Extrapolation method for error mitigation.
@@ -136,7 +137,11 @@ def ZNE(
             *    response_matrix: numpy.ndarray, used for applying a pre-computed response matrix for readout error mitigation.
             *    ibu_iters: int, specifies the number of iterations for the iterative Bayesian unfolding method of readout error mitigation. If provided, the corresponding readout error mitigation method is used. Defaults to {}.
 
-        qubit_map (list, optional): the qubit map. If None, a list of range of circuit's qubits is used. Defaults to ``None``.
+        qubit_map (list, optional): the qubit map. If None, a list of range of circuit's qubits is used.
+            Defaults to ``None``.
+        seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random
+            numbers or a fixed seed to initialize a generator. If ``None``, initializes
+            a generator with a random seed. Default: ``None``.
         backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used
             in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`.
             Defaults to ``None``.
@@ -148,7 +153,7 @@ def ZNE(
         1. K. Temme, S. Bravyi et al, *Error mitigation for short-depth quantum circuits*.
            `arXiv:1612.02058 [quant-ph] <https://arxiv.org/abs/1612.02058>`_.
     """
-    backend = _check_backend(backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     if readout is None:
         readout = {}
@@ -165,6 +170,7 @@ def ZNE(
             nshots,
             readout,
             qubit_map,
+            seed=local_state,
             backend=backend,
         )
         expected_values.append(val)
@@ -178,6 +184,7 @@ def sample_training_circuit_cdr(
     circuit,
     replacement_gates: list = None,
     sigma: float = 0.5,
+    seed=None,
     backend=None,
 ):
     """Samples a training circuit for CDR by susbtituting some of the non-Clifford gates.
@@ -190,6 +197,9 @@ def sample_training_circuit_cdr(
             form (``gates.XYZ``, ``kwargs``). For example, phase gates are used by default:
             ``list((RZ, {'theta':0}), (RZ, {'theta':pi/2}), (RZ, {'theta':pi}), (RZ, {'theta':3*pi/2}))``.
         sigma (float, optional): standard devation of the Gaussian distribution used for sampling.
+        seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random
+            numbers or a fixed seed to initialize a generator. If ``None``, initializes
+            a generator with a random seed. Default: ``None``.
         backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used
             in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`.
             Defaults to ``None``.
@@ -197,7 +207,7 @@ def sample_training_circuit_cdr(
     Returns:
         :class:`qibo.models.Circuit`: The sampled circuit.
     """
-    backend = _check_backend(backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     if replacement_gates is None:
         replacement_gates = [(gates.RZ, {"theta": n * np.pi / 2}) for n in range(4)]
@@ -221,7 +231,7 @@ def sample_training_circuit_cdr(
         distance.append(
             np.linalg.norm(
                 gate.matrix(backend)
-                - [rep_gate.matrix(backend) for rep_gate in rep_gates],
+                - backend.cast([rep_gate.matrix(backend) for rep_gate in rep_gates]),
                 ord="fro",
                 axis=(1, 2),
             )
@@ -229,20 +239,23 @@ def sample_training_circuit_cdr(
 
     distance = np.vstack(distance)
     prob = np.exp(-(distance**2) / sigma**2)
+    prob = backend.cast(prob, dtype=prob.dtype)
 
-    index = np.random.choice(
+    index = local_state.choice(
         range(len(gates_to_replace)),
         size=min(int(len(gates_to_replace) / 2), 50),
         replace=False,
-        p=prob.sum(-1) / prob.sum(),
+        p=backend.to_numpy(backend.np.sum(prob, -1) / backend.np.sum(prob)),
     )
 
     gates_to_replace = np.array([gates_to_replace[i] for i in index])
     prob = [prob[i] for i in index]
+    prob = backend.cast(prob, dtype=prob[0].dtype)
+    prob = backend.to_numpy(prob)
 
     replacement = np.array([replacement[i] for i in index])
     replacement = [
-        replacement[i][np.random.choice(range(len(p)), size=1, p=p / p.sum())[0]]
+        replacement[i][local_state.choice(range(len(p)), size=1, p=p / np.sum(p))[0]]
         for i, p in enumerate(prob)
     ]
     replacement = {i[0]: g for i, g in zip(gates_to_replace, replacement)}
@@ -264,6 +277,7 @@ def CDR(
     full_output: bool = False,
     readout=None,
     qubit_map=None,
+    seed=None,
     backend=None,
 ):
     """Runs the Clifford Data Regression error mitigation method.
@@ -287,7 +301,11 @@ def CDR(
             *    response_matrix: numpy.ndarray, used for applying a pre-computed response matrix for readout error mitigation.
             *    ibu_iters: int, specifies the number of iterations for the iterative Bayesian unfolding method of readout error mitigation. If provided, the corresponding readout error mitigation method is used. Defaults to {}.
 
-        qubit_map (list, optional): the qubit map. If None, a list of range of circuit's qubits is used. Defaults to ``None``.
+        qubit_map (list, optional): the qubit map. If None, a list of range of circuit's qubits is used.
+            Defaults to ``None``.
+        seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random
+            numbers or a fixed seed to initialize a generator. If ``None``, initializes
+            a generator with a random seed. Default: ``None``.
         backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used
             in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`.
             Defaults to ``None``.
@@ -302,13 +320,14 @@ def CDR(
         1. P. Czarnik, A. Arrasmith et al, *Error mitigation with Clifford quantum-circuit data*.
            `arXiv:2005.10189 [quant-ph] <https://arxiv.org/abs/2005.10189>`_.
     """
-    backend = _check_backend(backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     if readout is None:
         readout = {}
 
     training_circuits = [
-        sample_training_circuit_cdr(circuit) for _ in range(n_training_samples)
+        sample_training_circuit_cdr(circuit, seed=local_state, backend=backend)
+        for _ in range(n_training_samples)
     ]
 
     train_val = {"noise-free": [], "noisy": []}
@@ -317,18 +336,32 @@ def CDR(
         val = result.expectation_from_samples(observable)
         train_val["noise-free"].append(val)
         val = get_expectation_val_with_readout_mitigation(
-            circ, observable, noise_model, nshots, readout, qubit_map, backend=backend
+            circ,
+            observable,
+            noise_model,
+            nshots,
+            readout,
+            qubit_map,
+            seed=local_state,
+            backend=backend,
         )
         train_val["noisy"].append(val)
 
     optimal_params = curve_fit(model, train_val["noisy"], train_val["noise-free"])[0]
 
     val = get_expectation_val_with_readout_mitigation(
-        circuit, observable, noise_model, nshots, readout, qubit_map, backend=backend
+        circuit,
+        observable,
+        noise_model,
+        nshots,
+        readout,
+        qubit_map,
+        seed=local_state,
+        backend=backend,
     )
     mit_val = model(val, *optimal_params)
 
-    if full_output is True:
+    if full_output:
         return mit_val, val, optimal_params, train_val
 
     return mit_val
@@ -346,6 +379,7 @@ def vnCDR(
     full_output: bool = False,
     readout=None,
     qubit_map=None,
+    seed=None,
     backend=None,
 ):
     """Runs the variable-noise Clifford Data Regression error mitigation method.
@@ -374,6 +408,9 @@ def vnCDR(
             *    ibu_iters: int, specifies the number of iterations for the iterative Bayesian unfolding method of readout error mitigation. If provided, the corresponding readout error mitigation method is used. Defaults to {}.
 
         qubit_map (list, optional): the qubit map. If None, a list of range of circuit's qubits is used. Defaults to ``None``.
+        seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random
+            numbers or a fixed seed to initialize a generator. If ``None``, initializes
+            a generator with a random seed. Default: ``None``.
         backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used
             in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`.
             Defaults to ``None``.
@@ -389,13 +426,14 @@ def vnCDR(
         1. A. Lowe, MH. Gordon et al, *Unified approach to data-driven quantum error mitigation*.
            `arXiv:2011.01157 [quant-ph] <https://arxiv.org/abs/2011.01157>`_.
     """
-    backend = _check_backend(backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     if readout is None:
         readout = {}
 
     training_circuits = [
-        sample_training_circuit_cdr(circuit) for _ in range(n_training_samples)
+        sample_training_circuit_cdr(circuit, seed=local_state, backend=backend)
+        for _ in range(n_training_samples)
     ]
     train_val = {"noise-free": [], "noisy": []}
 
@@ -412,13 +450,14 @@ def vnCDR(
                 nshots,
                 readout,
                 qubit_map,
+                seed=local_state,
                 backend=backend,
             )
             train_val["noisy"].append(val)
 
     noisy_array = np.array(train_val["noisy"]).reshape(-1, len(noise_levels))
 
-    params = np.random.rand(len(noise_levels))
+    params = local_state.random(len(noise_levels))
     optimal_params = curve_fit(model, noisy_array.T, train_val["noise-free"], p0=params)
 
     val = []
@@ -431,13 +470,14 @@ def vnCDR(
             nshots,
             readout,
             qubit_map,
+            seed=local_state,
             backend=backend,
         )
         val.append(expval)
 
     mit_val = model(np.array(val).reshape(-1, 1), *optimal_params[0])[0]
 
-    if full_output is True:
+    if full_output:
         return mit_val, val, optimal_params, train_val
 
     return mit_val
@@ -567,9 +607,14 @@ def apply_randomized_readout_mitigation(
     nshots: int = 10000,
     ncircuits: int = 10,
     qubit_map=None,
+    seed=None,
     backend=None,
 ):
-    """Readout mitigation method that transforms the bias in an expectation value into a measurable multiplicative factor. This factor can be eliminated at the expense of increased sampling complexity for the observable.
+    """Readout mitigation method that transforms the bias in an expectation value into a
+    measurable multiplicative factor.
+
+    This factor can be eliminated at the expense of increased sampling complexity
+    for the observable.
 
     Args:
         circuit (:class:`qibo.models.Circuit`): input circuit.
@@ -578,7 +623,11 @@ def apply_randomized_readout_mitigation(
         nshots (int, optional): number of shots. Defaults to :math:`10000`.
         ncircuits (int, optional): number of randomized circuits. Each of them uses
             ``int(nshots / ncircuits)`` shots. Defaults to 10.
-        qubit_map (list, optional): the qubit map. If None, a list of range of circuit's qubits is used. Defaults to ``None``.
+        qubit_map (list, optional): the qubit map. If None, a list of range of circuit's qubits is used.
+            Defaults to ``None``.
+        seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random
+            numbers or a fixed seed to initialize a generator. If ``None``, initializes
+            a generator with a random seed. Default: ``None``.
         backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used
             in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`.
             Defaults to ``None``.
@@ -589,7 +638,8 @@ def apply_randomized_readout_mitigation(
 
 
     Reference:
-        1. Ewout van den Berg, Zlatko K. Minev et al, *Model-free readout-error mitigation for quantum expectation values*.
+        1. Ewout van den Berg, Zlatko K. Minev et al,
+        *Model-free readout-error mitigation for quantum expectation values*.
            `arXiv:2012.09738 [quant-ph] <https://arxiv.org/abs/2012.09738>`_.
     """
     from qibo import Circuit  # pylint: disable=import-outside-toplevel
@@ -597,7 +647,7 @@ def apply_randomized_readout_mitigation(
         random_pauli,
     )
 
-    backend = _check_backend(backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     meas_qubits = circuit.measurements[0].qubits
     nshots_r = int(nshots / ncircuits)
@@ -607,7 +657,9 @@ def apply_randomized_readout_mitigation(
         circuit_c.queue.pop()
         cal_circuit = Circuit(circuit.nqubits, density_matrix=True)
 
-        x_gate = random_pauli(circuit.nqubits, 1, subset=["I", "X"]).queue
+        x_gate = random_pauli(
+            circuit.nqubits, 1, subset=["I", "X"], seed=local_state
+        ).queue
 
         error_map = {}
         for j, gate in enumerate(x_gate):
@@ -646,6 +698,7 @@ def get_expectation_val_with_readout_mitigation(
     nshots: int = 10000,
     readout=None,
     qubit_map=None,
+    seed=None,
     backend=None,
 ):
     """
@@ -662,21 +715,30 @@ def get_expectation_val_with_readout_mitigation(
             *    response_matrix: numpy.ndarray, used for applying a pre-computed response matrix for readout error mitigation.
             *    ibu_iters: int, specifies the number of iterations for the iterative Bayesian unfolding method of readout error mitigation. If provided, the corresponding readout error mitigation method is used. Defaults to {}.
 
-        qubit_map (list, optional): the qubit map. If None, a list of range of circuit's qubits is used. Defaults to ``None``.
+        qubit_map (list, optional): the qubit map. If None, a list of range of circuit's qubits is used.
+            Defaults to ``None``.
+        seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random
+            numbers or a fixed seed to initialize a generator. If ``None``, initializes
+            a generator with a random seed. Default: ``None``.
         backend (qibo.backends.abstract.Backend, optional): the backend to be used in the execution.
             If None, it uses the global backend. Defaults to ``None``.
 
     Returns:
         float: the mitigated expectation value of the observable.
     """
-    backend = _check_backend(backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     if readout is None:  # pragma: no cover
         readout = {}
 
     if "ncircuits" in readout:
         circuit_result, circuit_result_cal = apply_randomized_readout_mitigation(
-            circuit, noise_model, nshots, readout["ncircuits"], backend
+            circuit,
+            noise_model,
+            nshots,
+            readout["ncircuits"],
+            seed=local_state,
+            backend=backend,
         )
     else:
         circuit_result = _execute_circuit(
@@ -699,12 +761,16 @@ def get_expectation_val_with_readout_mitigation(
 
 def sample_clifford_training_circuit(
     circuit,
+    seed=None,
     backend=None,
 ):
     """Samples a training circuit for CDR by susbtituting all the non-Clifford gates.
 
     Args:
         circuit (:class:`qibo.models.Circuit`): circuit to sample from.
+        seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random
+            numbers or a fixed seed to initialize a generator. If ``None``, initializes
+            a generator with a random seed. Default: ``None``.
         backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used
             in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`.
             Defaults to ``None``.
@@ -716,7 +782,7 @@ def sample_clifford_training_circuit(
         random_clifford,
     )
 
-    backend = _check_backend(backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     non_clifford_gates_indices = [
         i
@@ -733,7 +799,9 @@ def sample_clifford_training_circuit(
         if isinstance(gate, gates.M):
             for q in gate.qubits:
                 gate_rand = gates.Unitary(
-                    random_clifford(1, backend=backend, return_circuit=False),
+                    random_clifford(
+                        1, return_circuit=False, seed=local_state, backend=backend
+                    ),
                     q,
                 )
                 gate_rand.clifford = True
@@ -743,7 +811,10 @@ def sample_clifford_training_circuit(
             if i in non_clifford_gates_indices:
                 gate = gates.Unitary(
                     random_clifford(
-                        len(gate.qubits), backend=backend, return_circuit=False
+                        len(gate.qubits),
+                        return_circuit=False,
+                        seed=local_state,
+                        backend=backend,
                     ),
                     *gate.qubits,
                 )
@@ -753,13 +824,17 @@ def sample_clifford_training_circuit(
     return sampled_circuit
 
 
-def error_sensitive_circuit(circuit, observable, backend=None):
+def error_sensitive_circuit(circuit, observable, seed=None, backend=None):
     """
     Generates a Clifford circuit that preserves the same circuit frame as the input circuit, and stabilizes the specified Pauli observable.
 
     Args:
         circuit (:class:`qibo.models.Circuit`): input circuit.
-        observable (:class:`qibo.hamiltonians.Hamiltonian/:class:`qibo.hamiltonians.SymbolicHamiltonian`): Pauli observable to be measured.
+        observable (:class:`qibo.hamiltonians.Hamiltonian` or :class:`qibo.hamiltonians.SymbolicHamiltonian`):
+            Pauli observable to be measured.
+        seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random
+            numbers or a fixed seed to initialize a generator. If ``None``, initializes
+            a generator with a random seed. Default: ``None``.
         backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used
             in the execution. if ``None``, it uses :class:`qibo.backends.GlobalBackend`.
             Defaults to ``None``.
@@ -773,15 +848,18 @@ def error_sensitive_circuit(circuit, observable, backend=None):
         1. Dayue Qin, Yanzhu Chen et al, *Error statistics and scalability of quantum error mitigation formulas*.
            `arXiv:2112.06255 [quant-ph] <https://arxiv.org/abs/2112.06255>`_.
     """
+    from qibo import matrices  # pylint: disable=import-outside-toplevel
     from qibo.quantum_info import (  # pylint: disable=import-outside-toplevel
         comp_basis_to_pauli,
         random_clifford,
         vectorization,
     )
 
-    backend = _check_backend(backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
-    sampled_circuit = sample_clifford_training_circuit(circuit, backend=backend)
+    sampled_circuit = sample_clifford_training_circuit(
+        circuit, seed=local_state, backend=backend
+    )
     unitary_matrix = sampled_circuit.unitary(backend=backend)
     num_qubits = sampled_circuit.nqubits
 
@@ -803,10 +881,10 @@ def error_sensitive_circuit(circuit, observable, backend=None):
     observable_pauli = list(product(["I", "X", "Y", "Z"], repeat=num_qubits))[index]
 
     pauli_gates = {
-        "I": backend.cast(gates.I(0).matrix(backend=backend)),
-        "X": backend.cast(gates.X(0).matrix(backend=backend)),
-        "Y": backend.cast(gates.Y(0).matrix(backend=backend)),
-        "Z": backend.cast(gates.Z(0).matrix(backend=backend)),
+        "I": backend.cast(matrices.I, dtype=matrices.I.dtype),
+        "X": backend.cast(matrices.X, dtype=matrices.X.dtype),
+        "Y": backend.cast(matrices.Y, dtype=matrices.Y.dtype),
+        "Z": backend.cast(matrices.Z, dtype=matrices.Z.dtype),
     }
 
     adjustment_gates = []
@@ -816,7 +894,9 @@ def error_sensitive_circuit(circuit, observable, backend=None):
         while backend.np.any(
             backend.np.abs(observable_i - pauli_gates["Z"]) > 1e-5
         ) and backend.np.any(abs(observable_i - pauli_gates["I"]) > 1e-5):
-            random_init = random_clifford(1, backend=backend, return_circuit=False)
+            random_init = random_clifford(
+                1, return_circuit=False, seed=local_state, backend=backend
+            )
             observable_i = (
                 backend.np.conj(backend.np.transpose(random_init, (1, 0)))
                 @ pauli_gates[observable_pauli[i]]
@@ -846,6 +926,7 @@ def ICS(
     nshots=int(1e4),
     n_training_samples=10,
     full_output=False,
+    seed=None,
     backend=None,
 ):
     """
@@ -860,12 +941,16 @@ def ICS(
             *    response_matrix: numpy.ndarray, used for applying a pre-computed response matrix for readout error mitigation.
             *    ibu_iters: int, specifies the number of iterations for the iterative Bayesian unfolding method of readout error mitigation. If provided, the corresponding readout error mitigation method is used. Defaults to {}.
 
-        qubit_map (list, optional): the qubit map. If ``None``, a list of range of circuit's qubits is used. Defaults to ``None``.
+        qubit_map (list, optional): the qubit map. If ``None``, a list of range of circuit's qubits is used.
+            Defaults to ``None``.
         noise_model (qibo.models.noise.Noise, optional): the noise model to be applied. Defaults to ``None``.
         nshots (int, optional): the number of shots for the circuit execution. Defaults to :math:`10000`.
         n_training_samples (int, optional): the number of training samples. Defaults to 10.
         full_output (bool, optional): if ``True``, this function returns additional
             information: ``val``, ``optimal_params``, ``train_val``. Defaults to ``False``.
+        seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random
+            numbers or a fixed seed to initialize a generator. If ``None``, initializes
+            a generator with a random seed. Default: ``None``.
         backend (qibo.backends.abstract.Backend, optional): the backend to be used in the execution.
             If None, it uses the global backend. Defaults to ``None``.
 
@@ -881,7 +966,7 @@ def ICS(
         1. Dayue Qin, Yanzhu Chen et al, *Error statistics and scalability of quantum error mitigation formulas*.
            `arXiv:2112.06255 [quant-ph] <https://arxiv.org/abs/2112.06255>`_.
     """
-    backend = _check_backend(backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     if readout is None:
         readout = {}
@@ -890,7 +975,9 @@ def ICS(
         qubit_map = list(range(circuit.nqubits))
 
     training_circuits = [
-        error_sensitive_circuit(circuit, observable, backend=backend)[0]
+        error_sensitive_circuit(circuit, observable, seed=local_state, backend=backend)[
+            0
+        ]
         for _ in range(n_training_samples)
     ]
 
@@ -908,6 +995,7 @@ def ICS(
             nshots,
             readout,
             qubit_map,
+            seed=local_state,
             backend=backend,
         )
 
@@ -919,7 +1007,14 @@ def ICS(
     dep_param_std = np.std(lambda_list)
 
     noisy_expectation = get_expectation_val_with_readout_mitigation(
-        circuit, observable, noise_model, nshots, readout, qubit_map, backend=backend
+        circuit,
+        observable,
+        noise_model,
+        nshots,
+        readout,
+        qubit_map,
+        seed=local_state,
+        backend=backend,
     )
     one_dep_squared = (1 - dep_param) ** 2
     dep_std_squared = dep_param_std**2
diff --git a/src/qibo/quantum_info/random_ensembles.py b/src/qibo/quantum_info/random_ensembles.py
index 26e6932f1b..e6d9faf49b 100644
--- a/src/qibo/quantum_info/random_ensembles.py
+++ b/src/qibo/quantum_info/random_ensembles.py
@@ -8,8 +8,8 @@
 import numpy as np
 from scipy.stats import rv_continuous
 
-from qibo import Circuit, gates
-from qibo.backends import NumpyBackend, _check_backend
+from qibo import Circuit, gates, matrices
+from qibo.backends import NumpyBackend, _check_backend_and_local_state
 from qibo.config import MAX_ITERATIONS, PRECISION_TOL, raise_error
 from qibo.quantum_info.basis import comp_basis_to_pauli
 from qibo.quantum_info.superoperator_transformations import (
@@ -67,7 +67,7 @@ def uniform_sampling_U3(ngates: int, seed=None, backend=None):
     elif ngates <= 0:
         raise_error(ValueError, f"ngates must be non-negative, but it is {ngates}.")
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     sampler = _probability_distribution_sin(a=0, b=np.pi, seed=local_state)
     phases = local_state.random((ngates, 3))
@@ -133,7 +133,7 @@ def random_gaussian_matrix(
     if stddev is not None and stddev <= 0.0:
         raise_error(ValueError, "stddev must be a positive float.")
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     dims = (dims, rank)
 
@@ -179,7 +179,7 @@ def random_hermitian(
     if not isinstance(semidefinite, bool) or not isinstance(normalize, bool):
         raise_error(TypeError, "semidefinite and normalize must be type bool.")
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     matrix = random_gaussian_matrix(dims, dims, seed=local_state, backend=backend)
 
@@ -226,7 +226,7 @@ def random_unitary(dims: int, measure: Optional[str] = None, seed=None, backend=
         if measure != "haar":
             raise_error(ValueError, f"measure {measure} not implemented.")
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     if measure == "haar":
         unitary = random_gaussian_matrix(dims, dims, seed=local_state, backend=backend)
@@ -342,7 +342,7 @@ def random_quantum_channel(
             NotImplementedError, f"order {order} not implemented for measure {measure}."
         )
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     if measure == "bcsz":
         super_op = _super_op_from_bcsz_measure(
@@ -434,7 +434,7 @@ def random_statevector(dims: int, seed=None, backend=None):
             TypeError, "seed must be either type int or numpy.random.Generator."
         )
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     state = local_state.standard_normal(dims).astype(complex)
     state += 1.0j * local_state.standard_normal(dims)
@@ -539,7 +539,7 @@ def random_density_matrix(
     elif normalize is True and basis is None:
         raise_error(ValueError, "normalize cannot be True when basis=None.")
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     if metric == "hilbert-schmidt":
         rank = None
@@ -628,7 +628,7 @@ def random_clifford(
             f"return_circuit must be type bool, but it is type {type(return_circuit)}.",
         )
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     hadamards, permutations = _sample_from_quantum_mallows_distribution(
         nqubits, local_state=local_state
@@ -809,9 +809,13 @@ def random_pauli(
             "subset argument must be a subset of strings in the set ['I', 'X', 'Y', 'Z'].",
         )
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
-    complete_set = {"I": gates.I, "X": gates.X, "Y": gates.Y, "Z": gates.Z}
+    complete_set = (
+        {"I": gates.I, "X": gates.X, "Y": gates.Y, "Z": gates.Z}
+        if return_circuit
+        else {"I": matrices.I, "X": matrices.X, "Y": matrices.Y, "Z": matrices.Z}
+    )
 
     if subset is None:
         subset = complete_set
@@ -840,13 +844,9 @@ def random_pauli(
                 if subset[column_item] != gates.I:
                     gate_grid.add(subset[column_item](qubit))
     else:
-        gate_grid = np.array(
-            [
-                [subset[column_item](qubit).matrix(backend) for column_item in row]
-                for qubit, row in zip(qubits, indexes)
-            ]
+        gate_grid = backend.cast(
+            [[subset[column_item] for column_item in row] for row in indexes]
         )
-        gate_grid = backend.cast(gate_grid, dtype=gate_grid.dtype)
 
     return gate_grid
 
@@ -912,7 +912,7 @@ def random_pauli_hamiltonian(
             "when normalize=True, gap is = 1, thus max_eigenvalue must be > 1.",
         )
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     d = 2**nqubits
 
@@ -1016,7 +1016,7 @@ def random_stochastic_matrix(
         if max_iterations <= 0.0:
             raise_error(ValueError, "max_iterations must be a positive int.")
 
-    backend, local_state = _set_backend_and_local_state(seed, backend)
+    backend, local_state = _check_backend_and_local_state(seed, backend)
 
     if precision_tol is None:
         precision_tol = PRECISION_TOL
@@ -1104,17 +1104,17 @@ def _sample_from_quantum_mallows_distribution(nqubits: int, local_state):
 
 @cache
 def _create_S(q):
-    return gates.S(q)
+    return gates.S(int(q))
 
 
 @cache
 def _create_CZ(cq, tq):
-    return gates.CZ(cq, tq)
+    return gates.CZ(int(cq), int(tq))
 
 
 @cache
 def _create_CNOT(cq, tq):
-    return gates.CNOT(cq, tq)
+    return gates.CNOT(int(cq), int(tq))
 
 
 def _operator_from_hadamard_free_group(
@@ -1219,29 +1219,3 @@ def _super_op_from_bcsz_measure(dims: int, rank: int, order: str, seed, backend)
     super_op = operator @ super_op @ operator
 
     return super_op
-
-
-def _set_backend_and_local_state(seed, backend):
-    if (
-        seed is not None
-        and not isinstance(seed, int)
-        and not isinstance(seed, np.random.Generator)
-    ):
-        raise_error(
-            TypeError, "seed must be either type int or numpy.random.Generator."
-        )
-
-    backend = _check_backend(backend)
-
-    if seed is None or isinstance(seed, int):
-        if backend.__class__.__name__ in [
-            "CupyBackend",
-            "CuQuantumBackend",
-        ]:  # pragma: no cover
-            local_state = backend.np.random.default_rng(seed)
-        else:
-            local_state = np.random.default_rng(seed)
-    else:
-        local_state = seed
-
-    return backend, local_state
diff --git a/tests/test_backends.py b/tests/test_backends.py
index 61ed409f49..876e931b2f 100644
--- a/tests/test_backends.py
+++ b/tests/test_backends.py
@@ -1,7 +1,10 @@
+import platform
+
 import numpy as np
 import pytest
 
-from qibo import gates
+from qibo import construct_backend, gates, list_available_backends, set_backend
+from qibo.backends import MetaBackend
 
 ####################### Test `matrix` #######################
 GATES = [
@@ -122,3 +125,33 @@ def test_plus_density_matrix(backend):
     matrix = backend.plus_density_matrix(4)
     target_matrix = np.ones((16, 16)) / 16
     backend.assert_allclose(matrix, target_matrix)
+
+
+def test_set_backend_error():
+    with pytest.raises(ValueError):
+        set_backend("non-existing-backend")
+
+
+def test_metabackend_load_error():
+    with pytest.raises(ValueError):
+        MetaBackend.load("non-existing-backend")
+
+
+def test_construct_backend(backend):
+    assert isinstance(
+        construct_backend(backend.name, platform=backend.platform), backend.__class__
+    )
+
+
+def test_list_available_backends():
+    tensorflow = False if platform.system() == "Windows" else True
+    available_backends = {
+        "numpy": True,
+        "tensorflow": tensorflow,
+        "pytorch": True,
+        "qibojit": {"numba": True, "cupy": False, "cuquantum": False},
+        "qibolab": False,
+        "qibo-cloud-backends": False,
+        "qibotn": {"cutensornet": False, "qutensornet": True},
+    }
+    assert available_backends == list_available_backends()
diff --git a/tests/test_global_backend.py b/tests/test_backends_global.py
similarity index 100%
rename from tests/test_global_backend.py
rename to tests/test_backends_global.py
diff --git a/tests/test_gates_gates.py b/tests/test_gates_gates.py
index d407e72e9e..dbd04d9be5 100644
--- a/tests/test_gates_gates.py
+++ b/tests/test_gates_gates.py
@@ -212,12 +212,12 @@ def test_align(backend):
     with pytest.raises(ValueError):
         gates.Align(0, delay=-1)
 
-    nqubits = 2
+    nqubits = 1
 
-    gate = gates.Align(0, 1)
-    gate_list = [gates.H(0), gates.H(1), gate]
+    gate = gates.Align(0, 0)
+    gate_list = [gates.H(0), gate]
 
-    final_state = apply_gates(backend, gate_list, nqubits=2)
+    final_state = apply_gates(backend, gate_list, nqubits=nqubits)
     target_state = backend.plus_state(nqubits)
 
     backend.assert_allclose(final_state, target_state)
@@ -229,8 +229,8 @@ def test_align(backend):
     with pytest.raises(NotImplementedError):
         gate.qasm_label
 
-    assert not gates.Align(0, 1).clifford
-    assert not gates.Align(0, 1).unitary
+    assert not gates.Align(0, delay=0).clifford
+    assert not gates.Align(0, delay=0).unitary
 
 
 # :class:`qibo.core.cgates.M` is tested seperately in `test_measurement_gate.py`
diff --git a/tests/test_measurements_collapse.py b/tests/test_measurements_collapse.py
index 2ff1c78428..12eb051828 100644
--- a/tests/test_measurements_collapse.py
+++ b/tests/test_measurements_collapse.py
@@ -18,7 +18,9 @@ def test_measurement_collapse(backend, nqubits, targets):
         c.add(gates.H(q))
     r = c.add(gates.M(*targets, collapse=True))
     c.add(gates.M(*targets))
-    outcome = backend.execute_circuit(c, np.copy(initial_state), nshots=1)
+    outcome = backend.execute_circuit(
+        c, backend.cast(initial_state, copy=True), nshots=1
+    )
     samples = r.samples()[0]
     backend.assert_allclose(samples, outcome.samples()[0])
 
@@ -128,7 +130,7 @@ def test_measurement_result_parameters_repeated_execution(backend, use_loop):
                 c, initial_state=np.copy(initial_state), nshots=1
             )
             final_states.append(final_state.state())
-        final_states = np.asarray(final_states).mean(0)
+        final_states = backend.np.mean(backend.cast(final_states), 0)
     else:
         final_states = backend.execute_circuit(
             c, initial_state=np.copy(initial_state), nshots=20
@@ -160,7 +162,9 @@ def test_measurement_result_parameters_repeated_execution_final_measurements(bac
     c.add(gates.RY(0, theta=np.pi * r.symbols[0] / 3))
     c.add(gates.RY(2, theta=np.pi * r.symbols[0] / 4))
     c.add(gates.M(0, 1, 2, 3))
-    result = backend.execute_circuit(c, initial_state=np.copy(initial_state), nshots=30)
+    result = backend.execute_circuit(
+        c, initial_state=backend.cast(initial_state, copy=True), nshots=30
+    )
     final_samples = result.samples(binary=False)
 
     backend.set_seed(123)
diff --git a/tests/test_measurements_probabilistic.py b/tests/test_measurements_probabilistic.py
index 6b018c3f08..13381865f2 100644
--- a/tests/test_measurements_probabilistic.py
+++ b/tests/test_measurements_probabilistic.py
@@ -43,6 +43,8 @@ def test_sample_frequency_agreement(backend):
     target_frequencies = result.frequencies(binary=False)
     samples = result.samples(binary=False)
     outcomes, counts = np.unique(samples, return_counts=True)
+    outcomes = backend.to_numpy(outcomes)
+    counts = backend.to_numpy(counts)
     frequencies = dict(zip(outcomes, counts))
     assert frequencies == target_frequencies
 
diff --git a/tests/test_models_circuit_backpropagation.py b/tests/test_models_circuit_backpropagation.py
index 12bccc215a..49ac6a77c5 100644
--- a/tests/test_models_circuit_backpropagation.py
+++ b/tests/test_models_circuit_backpropagation.py
@@ -22,7 +22,7 @@ def test_variable_backpropagation():
     backend = construct_tensorflow_backend()
     import tensorflow as tf
 
-    theta = tf.Variable(0.1234, dtype=tf.complex128)
+    theta = tf.Variable(0.1234, dtype=tf.float64)
     # TODO: Fix parametrized gates so that `Circuit` can be defined outside
     # of the gradient tape
     with tf.GradientTape() as tape:
@@ -45,7 +45,7 @@ def test_two_variables_backpropagation():
     backend = construct_tensorflow_backend()
     import tensorflow as tf
 
-    theta = tf.Variable([0.1234, 0.4321], dtype=tf.complex128)
+    theta = tf.Variable([0.1234, 0.4321], dtype=tf.float64)
     # TODO: Fix parametrized gates so that `Circuit` can be defined outside
     # of the gradient tape
     with tf.GradientTape() as tape:
diff --git a/tests/test_models_circuit_parametrized.py b/tests/test_models_circuit_parametrized.py
index c9ac4990bc..6bc1698321 100644
--- a/tests/test_models_circuit_parametrized.py
+++ b/tests/test_models_circuit_parametrized.py
@@ -227,8 +227,8 @@ def test_variable_theta():
 
     import tensorflow as tf  # pylint: disable=import-error
 
-    theta1 = tf.Variable(0.1234, dtype=tf.complex128)
-    theta2 = tf.Variable(0.4321, dtype=tf.complex128)
+    theta1 = tf.Variable(0.1234, dtype=tf.float64)
+    theta2 = tf.Variable(0.4321, dtype=tf.float64)
     cvar = Circuit(2)
     cvar.add(gates.RX(0, theta1))
     cvar.add(gates.RY(1, theta2))
diff --git a/tests/test_models_circuit_qasm.py b/tests/test_models_circuit_qasm.py
index 39c4e4c44f..43d15fd8c5 100644
--- a/tests/test_models_circuit_qasm.py
+++ b/tests/test_models_circuit_qasm.py
@@ -533,8 +533,10 @@ def alice(q0, q1):
         gate.append(bob(-np.pi / 4, np.pi / 2, q0, q1))
         return gate
 
-    backend.assert_allclose(c.queue[0].matrix(), bob(-np.pi / 2, np.pi, 0, 2).matrix())
-    backend.assert_allclose(c.queue[1].matrix(), alice(1, 0).matrix())
+    backend.assert_allclose(
+        c.queue[0].matrix(backend), bob(-np.pi / 2, np.pi, 0, 2).matrix(backend)
+    )
+    backend.assert_allclose(c.queue[1].matrix(backend), alice(1, 0).matrix(backend))
 
 
 def test_from_qasm_unsupported_statement():
diff --git a/tests/test_quantum_info_clifford.py b/tests/test_quantum_info_clifford.py
index 9166d3ad0c..c9e8654df9 100644
--- a/tests/test_quantum_info_clifford.py
+++ b/tests/test_quantum_info_clifford.py
@@ -4,7 +4,7 @@
 import numpy as np
 import pytest
 
-from qibo import Circuit, gates
+from qibo import Circuit, gates, matrices
 from qibo.backends import CliffordBackend, PyTorchBackend, TensorflowBackend
 from qibo.backends.clifford import _get_engine_name
 from qibo.quantum_info._clifford_utils import (
@@ -152,9 +152,10 @@ def test_clifford_stabilizers(backend, symplectic, return_array):
     obj = Clifford.from_circuit(c, engine=_get_engine_name(backend))
     if return_array:
         true_generators = [
-            reduce(np.kron, [getattr(gates, gate)(0).matrix() for gate in generator])
+            reduce(np.kron, [getattr(matrices, gate) for gate in generator])
             for generator in ["XII", "IZI", "IIZ"]
         ]
+        true_generators = backend.cast(true_generators, dtype=true_generators[0].dtype)
     else:
         true_generators = ["XII", "IZI", "IIZ"]
     true_phases = [1, 1, -1]
@@ -181,12 +182,13 @@ def test_clifford_stabilizers(backend, symplectic, return_array):
             "-IIZ",
             "III",
         ]:
-            tmp = reduce(
-                np.kron, [getattr(gates, s)(0).matrix() for s in stab.replace("-", "")]
-            )
+            tmp = reduce(np.kron, [getattr(matrices, s) for s in stab.replace("-", "")])
             if "-" in stab:
                 tmp *= -1
             true_stabilizers.append(tmp)
+        true_stabilizers = backend.cast(
+            true_stabilizers, dtype=true_stabilizers[0].dtype
+        )
     elif not symplectic and not return_array:
         true_stabilizers = [
             "-XZZ",
@@ -220,10 +222,10 @@ def test_clifford_destabilizers(backend, symplectic, return_array):
     obj = Clifford.from_circuit(c, engine=_get_engine_name(backend))
     if return_array:
         true_generators = [
-            reduce(np.kron, [getattr(gates, gate)(0).matrix() for gate in generator])
+            reduce(np.kron, [getattr(matrices, gate) for gate in generator])
             for generator in ["ZII", "IXI", "IIX"]
         ]
-        true_generators = backend.cast(true_generators)
+        true_generators = backend.cast(true_generators, dtype=true_generators[0].dtype)
     else:
         true_generators = ["ZII", "IXI", "IIX"]
     true_phases = [1, 1, 1]
@@ -252,11 +254,14 @@ def test_clifford_destabilizers(backend, symplectic, return_array):
         ]:
             tmp = reduce(
                 np.kron,
-                [getattr(gates, s)(0).matrix() for s in destab.replace("-", "")],
+                [getattr(matrices, s) for s in destab.replace("-", "")],
             )
             if "-" in destab:
                 tmp *= -1
             true_destabilizers.append(tmp)
+        true_destabilizers = backend.cast(
+            true_destabilizers, dtype=true_destabilizers[0].dtype
+        )
     elif not symplectic and not return_array:
         true_destabilizers = [
             "ZXX",
diff --git a/tests/test_quantum_info_random.py b/tests/test_quantum_info_random.py
index 27c250765e..f974f57dd3 100644
--- a/tests/test_quantum_info_random.py
+++ b/tests/test_quantum_info_random.py
@@ -9,7 +9,6 @@
 from qibo.config import PRECISION_TOL
 from qibo.quantum_info.metrics import purity
 from qibo.quantum_info.random_ensembles import (
-    _probability_distribution_sin,
     random_clifford,
     random_density_matrix,
     random_gaussian_matrix,
diff --git a/tests/test_result.py b/tests/test_result.py
index 53d274d896..822006ba80 100644
--- a/tests/test_result.py
+++ b/tests/test_result.py
@@ -91,12 +91,11 @@ def test_circuitresult_dump_load(backend, agnostic_load):
     # set probabilities to trigger the warning
     result._probs = result.probabilities()
     result.dump("tmp.npy")
-    if agnostic_load:
-        loaded_res = load_result("tmp.npy")
-    else:
-        loaded_res = CircuitResult.load("tmp.npy")
+    loaded_res = (
+        load_result("tmp.npy") if agnostic_load else CircuitResult.load("tmp.npy")
+    )
     loaded_freq = loaded_res.frequencies()
     for state, f in freq.items():
         assert loaded_freq[state] == f
-    assert backend.np.sum(result.state() - loaded_res.state()) == 0
+    assert backend.np.sum(result.state() - backend.cast(loaded_res.state())) == 0
     remove("tmp.npy")
diff --git a/tests/test_transpiler_unitary_decompositions.py b/tests/test_transpiler_unitary_decompositions.py
index c72591c72c..6b6c0f4e3e 100644
--- a/tests/test_transpiler_unitary_decompositions.py
+++ b/tests/test_transpiler_unitary_decompositions.py
@@ -80,7 +80,7 @@ def test_v_decomposition(backend, seed):
             calculate_psi(unitary, backend=backend)
     else:
         psi, _ = calculate_psi(unitary, backend=backend)
-        va, vb = calculate_single_qubit_unitaries(psi)
+        va, vb = calculate_single_qubit_unitaries(psi, backend=backend)
         assert_single_qubits(backend, psi, va, vb)
 
 
@@ -94,7 +94,9 @@ def test_u_decomposition(backend, seed):
     else:
         psi, eigvals = calculate_psi(unitary, backend=backend)
         psi_tilde = np.conj(np.sqrt(eigvals)) * np.dot(unitary, psi)
-        ua_dagger, ub_dagger = calculate_single_qubit_unitaries(psi_tilde)
+        ua_dagger, ub_dagger = calculate_single_qubit_unitaries(
+            psi_tilde, backend=backend
+        )
         assert_single_qubits(backend, psi_tilde, ua_dagger, ub_dagger)
 
 
@@ -140,7 +142,7 @@ def test_cnot_decomposition(backend):
     hx, hy, hz = np.random.random(3)
     target_matrix = bell_unitary(hx, hy, hz)
     c = Circuit(2)
-    c.add(cnot_decomposition(0, 1, hx, hy, hz))
+    c.add(cnot_decomposition(0, 1, hx, hy, hz, backend))
     final_matrix = c.unitary(backend)
     backend.assert_allclose(final_matrix, target_matrix, atol=PRECISION_TOL)
 
@@ -149,7 +151,7 @@ def test_cnot_decomposition_light(backend):
     hx, hy = np.random.random(2)
     target_matrix = bell_unitary(hx, hy, 0)
     c = Circuit(2)
-    c.add(cnot_decomposition_light(0, 1, hx, hy))
+    c.add(cnot_decomposition_light(0, 1, hx, hy, backend))
     final_matrix = c.unitary(backend)
     backend.assert_allclose(final_matrix, target_matrix, atol=PRECISION_TOL)