🚸 Add examples

.github/workflows/ci.yml

@@ -30,7 +30,7 @@ jobs:
           python -m pip install --upgrade pip
           pip install nox
           pip install uv
-          uv sync
+          uv sync --group dev
       - name: Run pre-commit
         run: |
           uv run pre-commit install

examples/GHZ_measure.py

@@ -0,0 +1,40 @@
+# GHZ State generation and measurement
+
+import os
+
+import matplotlib.pyplot as plt
+from dotenv import load_dotenv
+from mqss.qiskit_adapter import MQSSQiskitAdapter
+from qiskit import QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+
+# Load environment variables
+load_dotenv()
+token = os.getenv("MQP_TOKEN")
+backend_name = os.getenv("MQP_BACKEND")
+
+adapter = MQSSQiskitAdapter(token=token)
+backend = adapter.get_backend(backend_name)
+
+# Parameters
+qubits = 8
+shots = 200
+
+# Build GHZ circuit
+qc = QuantumCircuit(qubits, qubits)
+qc.h(0)
+for i in range(1, qubits):
+    qc.cx(0, i)
+qc.measure_all(add_bits=False)
+
+# Transpile if needed
+trans_qc = transpile(qc, backend, optimization_level=3)
+
+# Run job
+job = backend.run(trans_qc, no_modify=True, shots=shots, queued=True)
+counts = job.result().get_counts()
+print("Result:", counts)
+
+# Plot
+plot_histogram(counts, figsize=(12, 6))
+plt.show()

examples/inverse_unitary_test.py

@@ -0,0 +1,50 @@
+# Inverse Unitary Test
+# Demonstrates how reversible a circuit is on simulator vs real hardware.
+
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+from dotenv import load_dotenv
+from mqss.qiskit_adapter import MQSSQiskitAdapter
+from qiskit import QuantumCircuit
+from qiskit.visualization import plot_histogram
+
+# Load environment variables
+load_dotenv()
+token = os.getenv("MQP_TOKEN")
+backend_name = os.getenv("MQP_BACKEND")
+
+adapter = MQSSQiskitAdapter(token=token)
+backend = adapter.get_backend(backend_name)
+
+n = 3
+x = 3  # |011⟩
+
+qc = QuantumCircuit(n)
+
+# Prepare |x⟩
+for i in range(n):
+    if (x >> i) & 1:
+        qc.x(i)
+
+# Unitaries
+for qubit in range(n):
+    qc.h(qubit)
+    for target in range(qubit + 1, n):
+        qc.crz(np.pi / 2 ** (target - qubit), qubit, target)
+
+# Inverse unitaries
+for qubit in reversed(range(n)):
+    for target in reversed(range(qubit + 1, n)):
+        qc.crz(-np.pi / 2 ** (target - qubit), qubit, target)
+    qc.h(qubit)
+
+qc.measure_all()
+
+job = backend.run(qc, shots=200, qasm3=False, queued=True)
+counts = job.result().get_counts()
+print("results:", counts)
+
+plot_histogram(counts)
+plt.show()

examples/shor_period_find.py

@@ -0,0 +1,85 @@
+# Shor period finding example
+# Find the period of f(x) = 13^x mod 15
+
+import math
+import os
+
+import matplotlib.pyplot as plt
+from dotenv import load_dotenv
+from mqss.qiskit_adapter import MQSSQiskitAdapter
+from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
+
+# Load environment variables
+load_dotenv()
+token = os.getenv("MQP_TOKEN")
+backend_name = os.getenv("MQP_BACKEND")
+
+adapter = MQSSQiskitAdapter(token=token)
+backend = adapter.get_backend(backend_name)
+
+# Parameters
+n = 4  # number of qubits in x-register
+shots = 200
+
+
+def f(x):
+    return pow(13, x, 15)
+
+
+# Build circuit
+qr_x = QuantumRegister(n, "x")
+qr_fx = QuantumRegister(4, "f(x)")
+cr_x = ClassicalRegister(n, "c_x")
+qc = QuantumCircuit(qr_x, qr_fx, cr_x)
+
+# Superposition
+for q in range(n):
+    qc.h(qr_x[q])
+qc.barrier()
+
+# f(x) implementation
+qc.x(qr_fx[0])
+qc.x(qr_fx[2])
+qc.x(qr_x[0])
+qc.ccx(qr_x[0], qr_x[1], qr_fx[0])
+qc.x(qr_x[0])
+qc.ccx(qr_x[0], qr_x[1], qr_fx[1])
+qc.x(qr_x[0])
+qc.x(qr_x[1])
+qc.ccx(qr_x[0], qr_x[1], qr_fx[2])
+qc.x(qr_x[0])
+qc.ccx(qr_x[0], qr_x[1], qr_fx[3])
+qc.x(qr_x[1])
+qc.barrier()
+
+# QFT
+for q in range(n - 1, -1, -1):
+    qc.h(q)
+    for k in range(1, q + 1):
+        qc.cp(math.pi / 2**k, q - k, q)
+    qc.barrier()
+for q in range(n // 2):
+    qc.swap(q, (n - 1) - q)
+qc.barrier()
+
+qc.measure(qr_x, cr_x)
+
+# Run job
+job = backend.run(qc, shots=shots, queued=True)
+counts = job.result().get_counts()
+print("Results:", counts)
+
+# Normalize and plot
+keys = list(counts.keys())
+x_vals = [int(k, 2) for k in keys]
+probs = [v / sum(counts.values()) for v in counts.values()]
+
+x_sorted, p_sorted = zip(*sorted(zip(x_vals, probs)))
+
+plt.figure()
+plt.grid(zorder=0, axis="y", linestyle="--")
+plt.bar(x_sorted, p_sorted, zorder=3)
+plt.xticks(rotation=65)
+plt.ylabel("Probability")
+plt.xlabel("x")
+plt.show()

pyproject.toml

@@ -68,4 +68,6 @@ dev = [
     "mkdocstrings[python]",
     "ruff>=0.11.5",
     "pytest-cov>=6.1.1",
+    "matplotlib",
+    "python-dotenv",
 ]