feat: implement single shot visualizer

src/grovers_visualizer/main.py

@@ -6,11 +6,10 @@ simulation using Qiskit's Aer simulator, and visualizes the results
 using matplotlib.
 """
 
+from collections.abc import Iterator
 from itertools import product
 from math import floor, pi, sqrt
-from typing import Iterator
 
-import matplotlib.pyplot as plt
 from matplotlib.axes import Axes
 from qiskit import QuantumCircuit
 from qiskit_aer import AerSimulator
@@ -36,15 +35,16 @@ def diffusion(qc: QuantumCircuit, n: int) -> None:
     qc.h(range(n))
 
 
-def grover_search(n: int, target_state: QubitState) -> QuantumCircuit:
+def grover_search(target_state: QubitState, iterations: int | None = None) -> QuantumCircuit:
     """Construct a Grover search circuit for the given target state."""
+    n = len(target_state)
     qc = QuantumCircuit(n, n)
 
     qc.h(range(n))
 
-    num_states = 2**n
+    if iterations is None or iterations < 0:
+        iterations = floor(pi / 4 * sqrt(2**n))
 
-    iterations = floor(pi / 4 * sqrt(num_states))
     for _ in range(iterations):
         oracle(qc, target_state)
         diffusion(qc, n)
@@ -75,30 +75,49 @@ def all_states(n_qubits: int) -> Iterator[QubitState]:
 
 
 def main() -> None:
-    n_qubits = 3
-    shots = 1024
+    shots = 20
+    target = QubitState("11111111111111111")
+    n_qubits = len(target)
 
-    _, ax = plt.subplots(figsize=(8, 4))
-    plt.ion()
+    qc = grover_search(target, iterations=4)
+    print(qc)
+    simulator = AerSimulator()
+    job = simulator.run(qc, shots=shots, memory=True)
+    result = job.result()
+    memory = result.get_memory(qc)  # List of measurement results, one per shot
+    counts = result.get_counts(qc)  # Dict: state string -> count
+    print(counts)
 
-    for state in all_states(n_qubits):
-        qc = grover_search(n_qubits, state)
+    # print(qc.draw("text"))
+    print(f"Target: {target}")
 
-        print(qc.draw("text"))
+    # Ensure all possible states are present in the bar chart
+    all_states = ["".join(bits) for bits in product("01", repeat=n_qubits)]
+    counts = dict.fromkeys(all_states, 0)
+    # print(counts)
 
-        simulator = AerSimulator()
-        job = simulator.run(qc, shots=shots)
-        result = job.result()
-        counts: dict[str, int] = result.get_counts(qc)
-        sorted_counts = dict(sorted(counts.items(), key=lambda x: x[1], reverse=True))
+    # plt.ion()
+    # _, ax = plt.subplots(figsize=(6, 2))
+    # bars = ax.bar(all_states, [0] * len(all_states), color="skyblue")
+    # ax.set_xlabel("Measured State")
+    # ax.set_ylabel("Counts")
+    # ax.set_title(f"Measurement Variability for Target: {target}")
+    # ax.set_ylim(0, shots)
 
-        print(f"Target: {state}")
-        print("\n".join(f"'{k}': {v}" for k, v in sorted_counts.items()))
+    for i, measured in enumerate(memory, 1):
+        pass
+        # print(measured)
+        # measured_be = measured[::-1]
+        # if measured_be in counts:
+        #     counts[measured_be] += 1
+        # for bar, state in zip(bars, all_states, strict=False):
+        #     bar.set_height(counts[state])
+        #     bar.set_color("orange" if state == str(target) else "skyblue")
+        # ax.set_title(f"Measurement Variability for Target: {target} (Shot {i}/{shots})")
+        # plt.pause(1)
 
-        plot_counts(ax, sorted_counts, state)
-        plt.pause(1)
-
-    plt.show()
+    # plt.ioff()
+    # plt.show()
 
 
 if __name__ == "__main__":