Demo 1：Deutsch-Jozsa

第 3 章 量子算法基础 · 本章演示 · 代码演示总索引

可视化：双子图柱状 —— 4-qubit 常数 vs 平衡 Oracle 一次查询

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/01_dj_constant_balanced.py

"""
ch03 演示 1：Deutsch-Jozsa 算法 4-qubit 常数 vs 平衡 Oracle

对应教程：chenzhaoyun.com/quantum-tutorial/ch03-algo-basics/deutsch-jozsa.html
依赖：pip install unified-quantum[visualization]
运行：python demos/ch03-algo-basics/01_dj_constant_balanced.py
"""
from uniqc import (
    Circuit,
    deutsch_jozsa_circuit,
    deutsch_jozsa_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_INPUT = 4
SHOTS = 2000


def run_dj(balanced: bool):
    """构建并运行一个 4-qubit DJ 电路，返回 4-bit 输入寄存器上的计数。"""
    oracle = deutsch_jozsa_oracle(qubits=list(range(N_INPUT)), balanced=balanced)
    c = deutsch_jozsa_circuit(oracle=oracle, qubits=list(range(N_INPUT)))
    # DJ 助手已经隐式测量输入寄存器，不要重复 measure()
    result = wait_for_result(
        submit_task(circuit=c, backend="dummy:local:simulator", shots=SHOTS)
    )
    # 丢弃 ancilla（最高位 qubit 4），只保留输入寄存器的 4 个比特
    input_counts = {}
    for bitstring, cnt in result.counts.items():
        # bitstring 长度=5：key[0] = ancilla(q4), key[1:] = input(q3,q2,q1,q0)
        input_bits = bitstring[1:]
        input_counts[input_bits] = input_counts.get(input_bits, 0) + cnt
    return input_counts


# === 1. 跑常数 Oracle 和平衡 Oracle ===
const_counts = run_dj(balanced=False)
balan_counts = run_dj(balanced=True)

# 构造完整的 4-bit 标签
all_labels = [f"{i:0{N_INPUT}b}" for i in range(2 ** N_INPUT)]
const_vals = np.array([const_counts.get(lbl, 0) for lbl in all_labels])
balan_vals = np.array([balan_counts.get(lbl, 0) for lbl in all_labels])

const_total = const_vals.sum()
balan_total = balan_vals.sum()
const_frac_zero = const_vals[0] / const_total
non_zero_frac = balan_vals[1:].sum() / balan_total

# === 2. matplotlib 可视化（2 子图：常数 / 平衡） ===
fig, axes = plt.subplots(1, 2, figsize=(13, 4.8), sharey=True)

for ax, vals, title, expected in (
    (axes[0], const_vals, "Constant oracle  (f(x)=0)", "expect 100% on 0000"),
    (axes[1], balan_vals, "Balanced oracle  (f(x)=x_0)", "expect 0% on 0000"),
):
    color = np.where(vals > 0, "#1f77b4", "#d3d3d3")
    # 突出 0000
    color = np.where(np.arange(len(vals)) == 0, "#d62728", color)
    ax.bar(np.arange(len(vals)), vals / vals.sum() * 100, color=color, edgecolor="black")
    ax.set_xticks(np.arange(len(vals)))
    ax.set_xticklabels(all_labels, rotation=90, fontsize=7)
    ax.set_xlabel("Input register (qubits 3..0)")
    ax.set_ylabel("Probability (%)")
    ax.set_title(f"{title}\n{expected}", fontsize=11)
    ax.set_ylim(0, 105)
    ax.grid(axis="y", alpha=0.3)

fig.suptitle(
    f"Deutsch-Jozsa on {N_INPUT} input qubits "
    f"({SHOTS} shots per circuit, dummy:local:simulator)",
    fontsize=12,
    y=1.02,
)
plt.tight_layout()
plt.savefig(OUT / "01_dj_constant_balanced.png", dpi=120, bbox_inches="tight")
plt.close()

# === 3. stdout 关键统计 ===
print("ch03-algo-basics demo 1: Deutsch-Jozsa 4-qubit constant vs balanced")
print(f"  total shots per circuit: {SHOTS}")
print(
    f"  constant: P(0000) = {const_frac_zero:.3f}  "
    f"(unique outcomes: {int((const_vals > 0).sum())})"
)
print(
    f"  balanced: P(0000) = {balan_vals[0] / balan_total:.3f}  "
    f"P(non-zero) = {non_zero_frac:.3f}  "
    f"(unique outcomes: {int((balan_vals > 0).sum())})"
)
print(f"  saved: {OUT / '01_dj_constant_balanced.png'}")

运行结果

运行方式

# 仓库根目录执行；输出 PNG 到 demos/ch03-algo-basics/01_dj_constant_balanced.png，关键统计到 stdout
.venv/bin/python3 demos/ch03-algo-basics/01_dj_constant_balanced.py

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