diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 0000bc1f4..413a7802d 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -12,7 +12,7 @@ on:
 
 jobs:
   tests:
-    runs-on: ubuntu-latest
+    runs-on: macos-latest
     timeout-minutes: 30
     steps:
       - uses: actions/checkout@v4
diff --git a/circuit_knitting/utils/conversion.py b/circuit_knitting/utils/conversion.py
deleted file mode 100644
index 63a88ef6a..000000000
--- a/circuit_knitting/utils/conversion.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""
-Code for converting types of distributions.
-
-.. currentmodule:: circuit_knitting.utils.conversion
-
-.. autosummary::
-   :toctree: ../stubs/
-
-   quasi_to_real
-   nearest_probability_distribution
-   naive_probability_distribution
-   dict_to_array
-"""
-
-import numpy as np
-from qiskit.utils.deprecation import deprecate_func
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def quasi_to_real(quasiprobability, mode):
-    """
-    Convert a quasi probability to a valid probability distribution.
-
-    Args:
-        quasiprobability: The array of quasiprobabilities
-        mode: How to compute the new distribution, either 'nearest' or 'naive'
-
-    Returns:
-        The converted probability distribution
-    """
-    if mode == "nearest":
-        return nearest_probability_distribution(quasiprobability=quasiprobability)
-    elif mode == "naive":
-        return naive_probability_distribution(quasiprobability=quasiprobability)
-    else:
-        raise NotImplementedError("%s conversion is not implemented" % mode)
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def nearest_probability_distribution(quasiprobability):
-    """
-    Convert quasiprobability distribution to the nearest probability distribution.
-
-    Takes a quasiprobability distribution and maps
-    it to the closest probability distribution as defined by
-    the L2-norm.
-
-    Method from Smolin et al., Phys. Rev. Lett. 108, 070502 (2012).
-
-    Args:
-        quasiprobability: The input quasiprobabilities
-
-    Returns:
-        The converted probability distribution
-    """
-    sorted_probs, states = zip(
-        *sorted(zip(quasiprobability, range(len(quasiprobability))))
-    )
-    num_elems = len(sorted_probs)
-    new_probs = np.zeros(num_elems)
-    beta = 0
-    diff = 0
-    for state, prob in zip(states, sorted_probs):
-        temp = prob + beta / num_elems
-        if temp < 0:
-            beta += prob
-            num_elems -= 1
-            diff += prob * prob
-        else:
-            diff += (beta / num_elems) * (beta / num_elems)
-            new_probs[state] = prob + beta / num_elems
-    return new_probs
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def naive_probability_distribution(quasiprobability):
-    """
-    Convert quasiprobability dist to probability dist by zero-ing out negative values.
-
-    Takes a quasiprobability distribution and does the following two steps:
-    1. Update all negative probabilities to 0
-    2. Normalize
-
-    Args:
-        quasiprobability: The input quasiprobabilities
-
-    Returns:
-        The converted probability distribution
-    """
-    new_probs = np.where(quasiprobability < 0, 0, quasiprobability)
-    new_probs /= np.sum(new_probs)
-    return new_probs
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def dict_to_array(distribution_dict, force_prob):
-    """
-    Convert dictionary of shot results to array of distribution.
-
-    Args:
-        distribution_dict: The dictionary containing the shot information
-            from circuit execution
-        force_prob: Whether to force the distribution to be normalized
-
-    Returns:
-        The resulting probability information
-    """
-    state = list(distribution_dict.keys())[0]
-    num_qubits = len(state)
-    num_shots = sum(distribution_dict.values())
-    cnts = np.zeros(2**num_qubits, dtype=float)
-    for state in distribution_dict:
-        cnts[int(state, 2)] = distribution_dict[state]
-    if abs(sum(cnts) - num_shots) > 1:
-        print(
-            "dict_to_array may be wrong, converted counts = {}, input counts = {}".format(
-                sum(cnts), num_shots
-            )
-        )
-    if not force_prob:
-        return cnts
-    else:
-        prob = cnts / num_shots
-        assert abs(sum(prob) - 1) < 1e-10
-        return prob
diff --git a/circuit_knitting/utils/metrics.py b/circuit_knitting/utils/metrics.py
deleted file mode 100644
index 354f56ca5..000000000
--- a/circuit_knitting/utils/metrics.py
+++ /dev/null
@@ -1,291 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""
-Functions for comparing array distances.
-
-.. currentmodule:: circuit_knitting.utils.metrics
-
-.. autosummary::
-   :toctree: ../stubs
-
-   chi2_distance
-   MSE
-   MAPE
-   cross_entropy
-   HOP
-"""
-
-import copy
-
-import numpy as np
-
-from qiskit.utils.deprecation import deprecate_func
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def chi2_distance(target, obs):  # noqa: D301
-    r"""
-    Measure the Chi-square distance.
-
-    The Chi-Square distance is a measure of statistically correlation between
-    two feature vectors and is defined as $ \sum_i \frac{(x_i - y_i)^2}{x_i + y_i}$.
-
-    Examples:
-    >>> chi2_distance(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
-    0.21645021645021645
-
-    >>> chi2_distance(np.array([0.25, 0.25, 0.25, 0.25]), np.array([0.25, 0.25, 0.25, 0.25]))
-    0
-
-    Args:
-        target: The target feature vector
-        obs: The actually observed feature vector
-
-    Returns:
-        The computed distance
-
-    Raises:
-        Exception: The target is not a numpy array or dictionary
-    """
-    target = copy.deepcopy(target)
-    obs = copy.deepcopy(obs)
-    obs = np.absolute(obs)
-    if isinstance(target, np.ndarray):
-        assert len(target) == len(obs)
-        distance = 0
-        for t, o in zip(target, obs):
-            if abs(t - o) > 1e-10:
-                distance += np.power(t - o, 2) / (t + o)
-    elif isinstance(target, dict):
-        distance = 0
-        for o_idx, o in enumerate(obs):
-            if o_idx in target:
-                t = target[o_idx]
-                if abs(t - o) > 1e-10:
-                    distance += np.power(t - o, 2) / (t + o)
-            else:
-                distance += o
-    else:
-        raise Exception("Illegal target type:", type(target))
-    return distance
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def MSE(target, obs):  # noqa: D301
-    r"""
-    Compute the Mean Squared Error (MSE).
-
-    The MSE is a common metric in fields such as deep learning and is used to
-    measure the squared distance between two vectors via:
-    $\sum_i (x_i - y_i)^2$.
-
-    Example:
-    >>> MSE(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
-    0.0275
-
-    Args:
-        target: The target feature vector
-        obs: The actually observed feature vector
-
-    Returns:
-        The computed MSE
-
-    Raises:
-        Exception: The target is not a dict
-        Exception: The target and obs are not numpy arrays
-        Exception: The target is not a numpy array and the obs are not a dict
-    """
-    target = copy.deepcopy(target)
-    obs = copy.deepcopy(obs)
-    if isinstance(target, dict):
-        se = 0
-        for t_idx in target:
-            t = target[t_idx]
-            o = obs[t_idx]
-            se += (t - o) ** 2
-        mse = se / len(obs)
-    elif isinstance(target, np.ndarray) and isinstance(obs, np.ndarray):
-        target = target.reshape(-1, 1)
-        obs = obs.reshape(-1, 1)
-        squared_diff = (target - obs) ** 2
-        se = np.sum(squared_diff)
-        mse = np.mean(squared_diff)
-    elif isinstance(target, np.ndarray) and isinstance(obs, dict):
-        se = 0
-        for o_idx in obs:
-            o = obs[o_idx]
-            t = target[o_idx]
-            se += (t - o) ** 2
-        mse = se / len(obs)
-    else:
-        raise Exception("target type : %s" % type(target))
-    return mse
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def MAPE(target, obs):  # noqa: D301
-    r"""
-    Compute the Mean Absolute Percentage Error (MAPE).
-
-    The MAPE is a scaled metric in the range [0, 100] defining the percentage
-    difference between two vectors via:
-    $ \sum_i \frac{x_i - y_i}{x_i} $.
-
-    Example:
-    >>> MAPE(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
-    91.66666666666659
-
-    Args:
-        target: The target feature vector
-        obs: The actually observed feature vector
-
-    Returns:
-        The computed MAPE
-
-    Raises:
-        Exception: The target is not a dict
-        Exception: The target and obs are not numpy arrays
-        Exception: The target is not a numpy array and the obs are not a dict
-    """
-    target = copy.deepcopy(target)
-    obs = copy.deepcopy(obs)
-    epsilon = 1e-16
-    if isinstance(target, dict):
-        curr_sum = np.sum(list(target.values()))
-        new_sum = curr_sum + epsilon * len(target)
-        mape = 0
-        for t_idx in target:
-            t = (target[t_idx] + epsilon) / new_sum
-            o = obs[t_idx]
-            mape += abs((t - o) / t)
-        mape /= len(obs)
-    elif isinstance(target, np.ndarray) and isinstance(obs, np.ndarray):
-        target = target.flatten()
-        target += epsilon
-        target /= np.sum(target)
-        obs = obs.flatten()
-        obs += epsilon
-        obs /= np.sum(obs)
-        mape = np.abs((target - obs) / target)
-        mape = np.mean(mape)
-    elif isinstance(target, np.ndarray) and isinstance(obs, dict):
-        curr_sum = np.sum(list(target.values()))
-        new_sum = curr_sum + epsilon * len(target)
-        mape = 0
-        for o_idx in obs:
-            o = obs[o_idx]
-            t = (target[o_idx] + epsilon) / new_sum
-            mape += abs((t - o) / t)
-        mape /= len(obs)
-    else:
-        raise Exception("target type : %s" % type(target))
-    return mape * 100
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def cross_entropy(target, obs):  # noqa: D301
-    r"""
-    Compute the cross entropy between two distributions.
-
-    The cross entropy is a measure of the difference between two probability
-    distributions, defined via:
-    $ -\sum_i x_i \log y_i $.
-
-    Example:
-    >>> cross_entropy(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
-    1.3862943611198906
-
-    Args:
-        target: The target feature vector
-        obs: The actually observed feature vector
-
-    Returns:
-        The computed cross entropy
-
-    Raises:
-        Exception: The target is not a dict
-        Exception: The target and obs are not numpy arrays
-        Exception: The target is not a numpy array and the obs are not a dict
-    """
-    target = copy.deepcopy(target)
-    obs = copy.deepcopy(obs)
-    if isinstance(target, dict):
-        CE = 0
-        for t_idx in target:
-            t = target[t_idx]
-            o = obs[t_idx]
-            o = o if o > 1e-16 else 1e-16
-            CE += -t * np.log(o)
-        return CE
-    elif isinstance(target, np.ndarray) and isinstance(obs, np.ndarray):
-        obs = np.clip(obs, a_min=1e-16, a_max=None)
-        CE = np.sum(-target * np.log(obs))
-        return CE
-    elif isinstance(target, np.ndarray) and isinstance(obs, dict):
-        CE = 0
-        for o_idx in obs:
-            o = obs[o_idx]
-            t = target[o_idx]
-            o = o if o > 1e-16 else 1e-16
-            CE += -t * np.log(o)
-        return CE
-    else:
-        raise Exception("target type : %s, obs type : %s" % (type(target), type(obs)))
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def HOP(target, obs):
-    """
-    Compute the Heavy Output Probability (HOP).
-
-    The HOP is an important metric for quantum volume experiments and is defined at the
-    probability that one measures a bitstring above the median target probability.
-
-    Example:
-    >>> HOP(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
-    0.5
-
-    Args:
-        target: The target feature vector
-        obs: The actually observed feature vector
-
-    Returns:
-        The computed HOP
-    """
-    target = copy.deepcopy(target)
-    obs = copy.deepcopy(obs)
-    target_median = np.median(target)
-    hop = 0
-    for t, o in zip(target, obs):
-        if t > target_median:
-            hop += o
-    return hop
diff --git a/compose.yaml b/compose.yaml
index d0df582b3..ada62cb74 100644
--- a/compose.yaml
+++ b/compose.yaml
@@ -7,7 +7,6 @@
 services:
   notebook:
     build: .
-    platform: linux/amd64
     restart: unless-stopped
     # The following line allows Ray to use /dev/shm rather than warn
     # about using /tmp
diff --git a/pyproject.toml b/pyproject.toml
index b7aa86a86..e383d4984 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -127,4 +127,4 @@ enable = [
 
 [tool.pytest.ini_options]
 testpaths = ["./circuit_knitting/", "./test/"]
-addopts = "--doctest-modules -rs --durations=10 --ignore=circuit_knitting/utils/metrics.py --ignore=circuit_knitting/cutting/cutqc"
+addopts = "--doctest-modules -rs --durations=10
diff --git a/tox.ini b/tox.ini
index cbca18fe4..d83e4eca8 100644
--- a/tox.ini
+++ b/tox.ini
@@ -52,7 +52,7 @@ commands =
   coverage3 run --source circuit_knitting --parallel-mode -m pytest --run-slow test/ --coverage {posargs}
   coverage3 combine
   coverage3 html
-  coverage3 report --fail-under=100 --show-missing --omit="circuit_knitting/cutting/cutqc/**/*,circuit_knitting/utils/conversion.py,circuit_knitting/utils/metrics.py"
+  coverage3 report --fail-under=100 --show-missing
 
 [testenv:docs]
 extras =