The preservation of privacy is a critical concern in the implementation of
artificial intelligence on sensitive training data. There are several
techniques to preserve data privacy but quantum computations are inherently
more secure due to the no-cloning theorem, resulting in a most desirable
computational platform on top of the potential quantum advantages. There have
been prior works in protecting data privacy by Quantum Federated Learning (QFL)
and Quantum Differential Privacy (QDP) studied independently. However, to the
best of our knowledge, no prior work has addressed both QFL and QDP together
yet. Here, we propose to combine these privacy-preserving methods and implement
them on the quantum platform, so that we can achieve comprehensive protection
against data leakage (QFL) and model inversion attacks (QDP). This
implementation promises more efficient and secure artificial intelligence. In
this paper, we present a successful implementation of these
privacy-preservation methods by performing the binary classification of the
Cats vs Dogs dataset. Using our quantum-classical machine learning model, we
obtained a test accuracy of over 0.98, while maintaining epsilon values less
than 1.3. We show that federated differentially private training is a viable
privacy preservation method for quantum machine learning on Noisy
Intermediate-Scale Quantum (NISQ) devices.

我们提出了结合量子计算的隐私保护方法，在基于量子的人工智能系统中实现数据泄露和模型反演攻击的全面保护，取得了成功。

差分隐私的联合量子机器学习

Federated Quantum Machine Learning with Differential Privacy

We develop and implement automated methods for optimizing quantum circuits of
the size and type expected in quantum computations that outperform classical
computers. We show how to handle continuous gate parameters and report a
collection of fast algorithms capable of optimizing large-scale quantum
circuits. For the suite of benchmarks considered, we obtain substantial
reductions in gate counts. In particular, we provide better optimization in
significantly less time than previous approaches, while making minimal
structural changes so as to preserve the basic layout of the underlying quantum
algorithms. Our results help bridge the gap between the computations that can
be run on existing hardware and those that are expected to outperform classical
computers.

研究开发了自动优化量子电路的方法，能够处理连续的门参数，并报告了一组快速算法，能够优化大规模量子电路，从而实现超越经典计算机的量子计算。

大型量子电路连续参数的自动优化

Automated optimization of large quantum circuits with continuous  parameters

We consider quantum computations comprising only commuting gates, known as
IQP computations, and provide compelling evidence that the task of sampling
their output probability distributions is unlikely to be achievable by any
efficient classical means. More specifically we introduce the class post-IQP of
languages decided with bounded error by uniform families of IQP circuits with
post-selection, and prove first that post-IQP equals the classical class PP.
Using this result we show that if the output distributions of uniform IQP
circuit families could be classically efficiently sampled, even up to 41%
multiplicative error in the probabilities, then the infinite tower of classical
complexity classes known as the polynomial hierarchy, would collapse to its
third level. We mention some further results on the classical simulation
properties of IQP circuit families, in particular showing that if the output
distribution results from measurements on only O(log n) lines then it may in
fact be classically efficiently sampled.

研究了只包含交错门的 IQP 计算，提供了强有力的证据表明用任何高效的经典方式都无法实现采样其输出的概率分布的任务。同时证明 post-IQP 等同于经典类 PP，且 如果经典地有效地采样均匀 IQP 电路族的输出分布，即使概率的乘法误差达到 41％，无线级的多项式层次结构将坍塌到其第三层，并提到了一些关于 IQP 电路族的经典模拟性质的进一步结果。