Demo 2：Grover 4-qubit

第 3 章 量子算法基础 · 本章演示 · 代码演示总索引

可视化：2×2 子图 —— 迭代 0/1/2/4 测量概率演化

对应教程：第 3 章 量子算法基础 中相关小节 · 依赖安装：pip install "unified-quantum[visualization,simulation]"

实现要点

完整源码

demos/ch03-algo-basics/02_grover_4qubit.py

"""
ch03 演示 2：Grover 4-qubit 标记 |0011⟩，迭代 0/1/2/4 次测量概率演化

对应教程：chenzhaoyun.com/quantum-tutorial/ch03-algo-basics/grover.html
依赖：pip install unified-quantum[visualization]
运行：python demos/ch03-algo-basics/02_grover_4qubit.py
"""
from uniqc import (
    Circuit,
    grover_diffusion,
    grover_oracle,
    submit_task,
    wait_for_result,
)
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path

OUT = Path(__file__).parent
N_QUBITS = 4
MARKED = 3  # = |0011>，即"|11>" 在 4-bit 表示下的等价态
ITERATIONS = (0, 1, 2, 4)
SHOTS = 2000


def run_grover(iterations: int):
    """构造均匀初态 |s> = H^n|0>，迭代 Grover 算子若干次，返回测量计数。"""
    oracle = grover_oracle(marked_state=MARKED, n_qubits=N_QUBITS)
    diff = grover_diffusion(n_qubits=N_QUBITS)
    c = Circuit()
    # 1) 均匀叠加初态
    for q in range(N_QUBITS):
        c.h(q)
    # 2) 迭代 Grover = Oracle + Diffusion
    for _ in range(iterations):
        c.add_circuit(oracle)
        c.add_circuit(diff)
    # 3) 测量每个输入比特
    for q in range(N_QUBITS):
        c.measure(q)
    result = wait_for_result(
        submit_task(circuit=c, backend="dummy:local:simulator", shots=SHOTS)
    )
    # uniqc 的 grover_oracle(marked_state, n_qubits) 会为内部 TOFFOLI
    # 多塞 3 个 ancilla（qubits 4,5,6）；counts key 的高位就是它们。只保留
    # 输入寄存器的低 N_QUBITS 位即可。
    input_counts = {}
    for bs, cnt in result.counts.items():
        input_counts[bs[-N_QUBITS:]] = input_counts.get(bs[-N_QUBITS:], 0) + cnt
    return input_counts


# === 1. 跑 0/1/2/4 次迭代 ===
counts_by_iter = {k: run_grover(k) for k in ITERATIONS}

labels = [f"{i:0{N_QUBITS}b}" for i in range(2 ** N_QUBITS)]
probs_by_iter = {}
for it, counts in counts_by_iter.items():
    total = sum(counts.values())
    probs_by_iter[it] = np.array([counts.get(lbl, 0) / total for lbl in labels])

# === 2. matplotlib 可视化（2x2 子图，对应 4 个迭代深度） ===
fig, axes = plt.subplots(2, 2, figsize=(13, 8.5), sharey=True)
axes = axes.flatten()

for ax, it in zip(axes, ITERATIONS):
    probs = probs_by_iter[it]
    color = np.where(np.arange(len(probs)) == MARKED, "#d62728", "#1f77b4")
    ax.bar(np.arange(len(probs)), probs * 100, color=color, edgecolor="black")
    ax.set_xticks(np.arange(len(probs)))
    ax.set_xticklabels(labels, rotation=90, fontsize=8)
    ax.set_xlabel("Basis state")
    ax.set_ylabel("Probability (%)")
    p_marked = probs[MARKED]
    ax.set_title(
        f"Iteration {it}  —  P(|{MARKED:0{N_QUBITS}b}>) = {p_marked:.3f}",
        fontsize=11,
    )
    ax.set_ylim(0, 70)
    ax.grid(axis="y", alpha=0.3)

fig.suptitle(
    f"Grover on {N_QUBITS} qubits marking |{MARKED:0{N_QUBITS}b}> "
    f"({SHOTS} shots per iteration, dummy:local:simulator)",
    fontsize=12,
    y=0.995,
)
plt.tight_layout()
plt.savefig(OUT / "02_grover_4qubit.png", dpi=120, bbox_inches="tight")
plt.close()

# === 3. stdout 关键统计 ===
print("ch03-algo-basics demo 2: Grover 4-qubit amplification")
print(f"  marked state: |{MARKED:0{N_QUBITS}b}>  (= int {MARKED})")
print(f"  N = 2^{N_QUBITS} = {2**N_QUBITS}; optimal iterations ≈ "
      f"{np.pi/4 * np.sqrt(2**N_QUBITS):.2f}")
print(f"  P(marked) per iteration:")
for it in ITERATIONS:
    p = probs_by_iter[it][MARKED]
    print(f"    iter {it}: {p:.3f}")
print(f"  saved: {OUT / '02_grover_4qubit.png'}")

运行结果

运行方式

# 仓库根目录执行；输出 PNG 到 demos/ch03-algo-basics/02_grover_4qubit.png，关键统计到 stdout
.venv/bin/python3 demos/ch03-algo-basics/02_grover_4qubit.py

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