In this paper, we propose shot optimization method for QML models at the
expense of minimal impact on model performance. We use classification task as a
test case for MNIST and FMNIST datasets using a hybrid quantum-classical QML
model. First, we sweep the number of shots for short and full versions of the
dataset. We observe that training the full version provides 5-6% higher testing
accuracy than short version of dataset with up to 10X higher number of shots
for training. Therefore, one can reduce the dataset size to accelerate the
training time. Next, we propose adaptive shot allocation on short version
dataset to optimize the number of shots over training epochs and evaluate the
impact on classification accuracy. We use a (a) linear function where the
number of shots reduce linearly with epochs, and (b) step function where the
number of shots reduce in step with epochs. We note around 0.01 increase in
loss and around 4% (1%) reduction in testing accuracy for reduction in shots by
up to 100X (10X) for linear (step) shot function compared to conventional
constant shot function for MNIST dataset, and 0.05 increase in loss and around
5-7% (5-7%) reduction in testing accuracy with similar reduction in shots using
linear (step) shot function on FMNIST dataset. For comparison, we also use the
proposed shot optimization methods to perform ground state energy estimation of
different molecules and observe that step function gives the best and most
stable ground state energy prediction at 1000X less number of shots.

本文提出了量子机器学习模型的 shot 优化方法，采用混合量子 - 经典模型进行分类任务的测试，并在 MNIST 和 FMNIST 数据集上进行测试，通过自适应 shot 分配优化模型，结果显示采用 step function 进行 shot 分配可以在降低 shots 数量的同时保证了各项指标的稳定性。

量子机器学习架构中的拍摄优化以加速训练

Shot Optimization in Quantum Machine Learning Architectures to  Accelerate Training

Natural evolutionary strategies (NES) are a family of gradient-free black-box
optimization algorithms. This study illustrates their use for the optimization
of randomly-initialized parametrized quantum circuits (PQCs) in the region of
vanishing gradients. We show that using the NES gradient estimator the
exponential decrease in variance can be alleviated. We implement two specific
approaches, the exponential and separable natural evolutionary strategies, for
parameter optimization of PQCs and compare them against standard gradient
descent. We apply them to two different problems of ground state energy
estimation using variational quantum eigensolver (VQE) and state preparation
with circuits of varying depth and length. We also introduce batch optimization
for circuits with larger depth to extend the use of evolutionary strategies to
a larger number of parameters. We achieve accuracy comparable to
state-of-the-art optimization techniques in all the above cases with a lower
number of circuit evaluations. Our empirical results indicate that one can use
NES as a hybrid tool in tandem with other gradient-based methods for
optimization of deep quantum circuits in regions with vanishing gradients.

本论文探讨了使用 Natural evolutionary strategies 方法优化处理处于梯度消失区域的随机初始化的带参数的量子电路。研究者们使用了 NES 梯度估算器来缓解方差的指数下降。他们在两种不同的问题中使用了两种特定的优化方法，并将其与标准梯度下降进行了比较。在所有这些情况下，他们的实验结果表明，使用 NES 方法可以在较少的电路评估次数下实现与现有优化技术相当的准确性，从而将 NES 方法作为与其他基于梯度的方法协同工作的混合工具来优化零梯度区域中的深度量子电路。