raijeku / qmeans

Q-Means algorithm implementation using Qiskit compatible with Scikit-Learn.

Hi there, I'm David 👋

• 🔭 I’m currently working on optimizing pulses on IBM quantum devices, analyzing my quantum k-means implementation, quantum walks and constellation modulation. I'm also working on my personal website!
• 🌱 I’m currently learning about the Quantum Approximate Optimization Algorithm (QAOA), quantum walks and constellation diagrams in optics.
• 👯 I’m looking to collaborate on quantum machine learning and quantum benchmarking projects. Classical machine learning application projects too.
• 🤔 I’m looking for help with improving my open source projects.
• 💬 Ask me about anything math or quantum related.
• 📫 How to reach me: Email me at [email protected]
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qmeans's Issues

TypeError: '<=' between instances of 'str' and 'int'

Hello.
I'm trying out your module, for which I thank you, and got a curious error that can't figure out on my own:

I'm using IBM Quantum lab online, and this is what I am doing:

X = pd.read_csv('../Datos/bankmarketing-101.csv', sep=';', usecols=['age', 'job', 'marital'])
# Change categorical data to number
X["marital"] = pd.Categorical(X["marital"])
X["marital"] = X["marital"].cat.codes
# Change categorical data to number 
X["job"] = pd.Categorical(X["job"])
X["job"] = X["job"].cat.codes

# Procesamiento
qk_means = QuantumKMeans(dev, n_clusters=3, verbose=True)
qk_means.fit(X)

An then I get the error:

[100 rows x 3 columns]
Iteration 0
Traceback (most recent call last):
  Input In [11] in <cell line: 30>
    qk_means.fit(X)
  File /opt/conda/lib/python3.8/site-packages/qmeans/qkmeans.py:756 in fit
    else: distances = np.asarray([[distance(point,centroid,self.backend,self.map_type,self.shots,np.array([norms[i],cluster_norms[j]])) for i, point in X.iterrows()] for j, centroid in normalized_clusters.iterrows()])
  File /opt/conda/lib/python3.8/site-packages/qmeans/qkmeans.py:756 in <listcomp>
    else: distances = np.asarray([[distance(point,centroid,self.backend,self.map_type,self.shots,np.array([norms[i],cluster_norms[j]])) for i, point in X.iterrows()] for j, centroid in normalized_clusters.iterrows()])
  File /opt/conda/lib/python3.8/site-packages/qmeans/qkmeans.py:756 in <listcomp>
    else: distances = np.asarray([[distance(point,centroid,self.backend,self.map_type,self.shots,np.array([norms[i],cluster_norms[j]])) for i, point in X.iterrows()] for j, centroid in normalized_clusters.iterrows()])
  File /opt/conda/lib/python3.8/site-packages/qmeans/qkmeans.py:194 in distance
    job = execute(qc,backend=backend, shots=shots)
  File /opt/conda/lib/python3.8/site-packages/qiskit/execute_function.py:299 in execute
    experiments = transpile(
  File /opt/conda/lib/python3.8/site-packages/qiskit/compiler/transpiler.py:327 in transpile
    unique_transpile_args, shared_args = _parse_transpile_args(
  File /opt/conda/lib/python3.8/site-packages/qiskit/compiler/transpiler.py:648 in _parse_transpile_args
    basis_gates = _parse_basis_gates(basis_gates, backend)
  File /opt/conda/lib/python3.8/site-packages/qiskit/compiler/transpiler.py:762 in _parse_basis_gates
    if backend_version <= 1:
TypeError: '<=' not supported between instances of 'str' and 'int'```

I don't see any string in the data, also searched the internet for similar problems similar, to no avail. So I ask you: can you see what is that I'm doing wrong?

Thanks in advance.
--
Nicolás.

QiskitError: "Cannot unroll the circuit to the given basis

I'm attempting to run the usage example but I want to use the ibmq_qasm_simulator instead of the aer_simulator_statevector

The code:

backend = provider.get_backend("ibmq_qasm_simulator")
X = pd.DataFrame(np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]))
qk_means = QuantumKMeans(backend, n_clusters=2, verbose=True)
qk_means.fit(X)
print(qk_means.labels_)

The error message:

Data is:     0  1
0   1  2
1   1  4
2   1  0
3  10  2
4  10  4
5  10  0
Iteration 0
Output exceeds the [size limit](command:workbench.action.openSettings?%5B%22notebook.output.textLineLimit%22%5D). Open the full output data [in a text editor](command:workbench.action.openLargeOutput?5a07abf9-11ba-4018-b8a5-3504edf05547)---------------------------------------------------------------------------
QiskitError                               Traceback (most recent call last)
Cell In[10], line 4
      2 X = pd.DataFrame(np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]))
      3 qk_means = QuantumKMeans(backend, n_clusters=2, verbose=True)
----> 4 qk_means.fit(X)
      5 print(qk_means.labels_)

File [~/sw/k-means/.env/lib/python3.10/site-packages/qmeans/qkmeans.py:794](https://file+.vscode-resource.vscode-cdn.net/Users/james/sw/k-means/~/sw/k-means/.env/lib/python3.10/site-packages/qmeans/qkmeans.py:794), in QuantumKMeans.fit(self, X, y, batch)
    792 else: 
    793     if self.map_type == 'probability':
--> 794         distances = np.asarray([[distance(point,centroid,self.backend,self.map_type,self.shots,np.array([norms[i],cluster_norms[j]]),noise_model=self.noise_model) for i, point in X.iterrows()] for j, centroid in normalized_clusters.iterrows()])
    795     elif self.map_type == 'angle':
    796         distances = np.asarray([[distance(point,centroid,self.backend,self.map_type,self.shots,np.array([1,1]),self.norm_relevance,noise_model=self.noise_model) for i, point in X.iterrows()] for j, centroid in normalized_clusters.iterrows()])

File [~/sw/k-means/.env/lib/python3.10/site-packages/qmeans/qkmeans.py:794](https://file+.vscode-resource.vscode-cdn.net/Users/james/sw/k-means/~/sw/k-means/.env/lib/python3.10/site-packages/qmeans/qkmeans.py:794), in (.0)
    792 else: 
    793     if self.map_type == 'probability':
--> 794         distances = np.asarray([[distance(point,centroid,self.backend,self.map_type,self.shots,np.array([norms[i],cluster_norms[j]]),noise_model=self.noise_model) for i, point in X.iterrows()] for j, centroid in normalized_clusters.iterrows()])
    795     elif self.map_type == 'angle':
    796         distances = np.asarray([[distance(point,centroid,self.backend,self.map_type,self.shots,np.array([1,1]),self.norm_relevance,noise_model=self.noise_model) for i, point in X.iterrows()] for j, centroid in normalized_clusters.iterrows()])

File [~/sw/k-means/.env/lib/python3.10/site-packages/qmeans/qkmeans.py:794](https://file+.vscode-resource.vscode-cdn.net/Users/james/sw/k-means/~/sw/k-means/.env/lib/python3.10/site-packages/qmeans/qkmeans.py:794), in (.0)
    792 else: 
    793     if self.map_type == 'probability':
...
    108 unrolled_dag = UnrollCustomDefinitions(
    109     self._equiv_lib, self._basis_gates, target=self._target
    110 ).run(decomposition)

QiskitError: "Cannot unroll the circuit to the given basis, ['u1', 'u2', 'u3', 'u', 'p', 'r', 'rx', 'ry', 'rz', 'cx', 'cy', 'cz', 'csx', 'cp', 'id', 'x', 'y', 'z', 'h', 's', 'sdg', 'sx', 't', 'tdg', 'swap', 'ccx', 'cswap', 'unitary', 'diagonal', 'initialize', 'cu1', 'cu2', 'cu3', 'rxx', 'ryy', 'rzz', 'rzx', 'mcx', 'mcy', 'mcz', 'mcsx', 'mcp', 'mcu1', 'mcu2', 'mcu3', 'mcrx', 'mcry', 'mcrz', 'mcr', 'mcswap', 'multiplexer', 'kraus', 'delay', 'roerror', 'measure']. Instruction reset not found in equivalence library and no rule found to expand."