Beginner score
78/100
Competition workspace
适合已经了解量子比特、实数酉矩阵、$R_y$ 与 CNOT 门，并能用 PaddlePaddle 2.0 及以上版本完成端到端模型的个人参赛者。前几题有固定电路结构可练习，但想取得较高总分的人需要能处理无结构的三、四和八量子比特电路分解。
Suggested next step
Decide whether this contest fits your current stage before you sink time into the leaderboard.
Beginner score
78/100
Learning value
72/100
Estimated effort
4-12 hours
Metric
Official scoring is published on the Ba…
适合已经了解量子比特、实数酉矩阵、$R_y$ 与 CNOT 门，并能用 PaddlePaddle 2.0 及以上版本完成端到端模型的个人参赛者。前几题有固定电路结构可练习，但想取得较高总分的人需要能处理无结构的三、四和八量子比特电路分解。
Taken from the competition's official page.
Question 2 input: a 4×4 real unitary matrix
Question 3 input: an 8×8 real unitary matrix
Question 4 input: an 8×8 real unitary matrix
Question 5 input: a 16×16 real unitary matrix
Quantum computing quick reference manual
If the items below still feel unfamiliar, you usually get a better result by preparing first instead of rushing in.
PaddlePaddle 2.0
量子电路基础
实数酉矩阵
线性代数与张量积
量子门保真度计算
文本格式化输出
The real friction is usually not library usage. It is validation, time allocation, and task framing.
主要难点是为无预设结构的目标酉矩阵设计电路：既要提高与目标矩阵的保真度，又要压低 $R_y$ 和 CNOT 门数量，因为门数会直接计入代价并影响得分。八量子比特题的目标矩阵为 $256\times256$，且占总题目分值的主要部分，电路搜索与验证工作会最重。
This guide is the best pre-read if you want a cleaner start instead of trial-and-error.
How to learn feature engineering, validation, and competition workflow without heavy hardware.
No GPU? Pick Competitions That Still Teach You Good HabitsUse these fields to make a quick decision before you dive deeper.
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