Works in quantum machine learning (QML) over the past few years indicate that
QML algorithms can function just as well as their classical counterparts, and
even outperform them in some cases. Among the corpus of recent work, many
current QML models take advantage of variational quantum algorithm (VQA)
circuits, given that their scale is typically small enough to be compatible
with NISQ devices and the method of automatic differentiation for optimizing
circuit parameters is familiar to machine learning (ML). While the results bear
interesting promise for an era when quantum machines are more readily
accessible, if one can achieve similar results through non-quantum methods then
there may be a more near-term advantage available to practitioners. To this
end, the nature of this work is to investigate the utilization of stochastic
methods inspired by a variational quantum version of the long short-term memory
(LSTM) model in an attempt to approach the reported successes in performance
and rapid convergence. By analyzing the performance of classical, stochastic,
and quantum methods, this work aims to elucidate if it is possible to achieve
some of QML's major reported benefits on classical machines by incorporating
aspects of its stochasticity.

分析经典、随机和量子方法的表现，研究在长短期记忆模型中利用量子变分算法启发的随机方法，以取得量子机器学习在经典机器上的一些好处。

一种受量子机器学习启发的新型随机 LSTM 模型

A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning

Quantum computing is a game-changing technology for global academia, research
centers and industries including computational science, mathematics, finance,
pharmaceutical, materials science, chemistry and cryptography. Although it has
seen a major boost in the last decade, we are still a long way from reaching
the maturity of a full-fledged quantum computer. That said, we will be in the
Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens
or even thousands of qubits quantum computing systems. An outstanding
challenge, then, is to come up with an application that can reliably carry out
a nontrivial task of interest on the near-term quantum devices with
non-negligible quantum noise. To address this challenge, several near-term
quantum computing techniques, including variational quantum algorithms, error
mitigation, quantum circuit compilation and benchmarking protocols, have been
proposed to characterize and mitigate errors, and to implement algorithms with
a certain resistance to noise, so as to enhance the capabilities of near-term
quantum devices and explore the boundaries of their ability to realize useful
applications. Besides, the development of near-term quantum devices is
inseparable from the efficient classical simulation, which plays a vital role
in quantum algorithm design and verification, error-tolerant verification and
other applications. This review will provide a thorough introduction of these
near-term quantum computing techniques, report on their progress, and finally
discuss the future prospect of these techniques, which we hope will motivate
researchers to undertake additional studies in this field.

这篇论文介绍了近期量子计算技术的各种方法，包括变分量子算法、误差缓解、量子电路编译和基准测试协议等，并指出了它们的应用前景和提高量子设备性能的潜力。

近期量子计算技术：Variational Quantum Algorithms、误差缓解、电路编译、基准测试和经典模拟

Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation

Variational Quantum Algorithms (VQAs) have received considerable attention
due to their potential for achieving near-term quantum advantage. However, more
work is needed to understand their scalability. One known scaling result for
VQAs is barren plateaus, where certain circumstances lead to exponentially
vanishing gradients. It is common folklore that problem-inspired ansatzes avoid
barren plateaus, but in fact, very little is known about their gradient
scaling. In this work we employ tools from quantum optimal control to develop a
framework that can diagnose the presence or absence of barren plateaus for
problem-inspired ansatzes. Such ansatzes include the Quantum Alternating
Operator Ansatz (QAOA), the Hamiltonian Variational Ansatz (HVA), and others.
With our framework, we prove that avoiding barren plateaus for these ansatzes
is not always guaranteed. Specifically, we show that the gradient scaling of
the VQA depends on the degree of controllability of the system, and hence can
be diagnosed through the dynamical Lie algebra $\mathfrak{g}$ obtained from the
generators of the ansatz. We analyze the existence of barren plateaus in QAOA
and HVA ansatzes, and we highlight the role of the input state, as different
initial states can lead to the presence or absence of barren plateaus. Taken
together, our results provide a framework for trainability-aware ansatz design
strategies that do not come at the cost of extra quantum resources. Moreover,
we prove no-go results for obtaining ground states with variational ansatzes
for controllable system such as spin glasses. Our work establishes a link
between the existence of barren plateaus and the scaling of the dimension of
$\mathfrak{g}$.

本文研究了通过量子优化控制诊断基于问题的变分量子算法的荒原高原现象，证明了微调度系的可控性对 VQA 的梯度缩放具有影响，并建立了荒原高原与 Lie 代数维度缩放之间的联系。

用量子优化控制工具诊断贫瘠高原

Diagnosing Barren Plateaus with Tools from Quantum Optimal Control

We present the enhanced feature quantum autoencoder, or EF-QAE, a variational
quantum algorithm capable of compressing quantum states of different models
with higher fidelity. The key idea of the algorithm is to define a
parameterized quantum circuit that depends upon adjustable parameters and a
feature vector that characterizes such a model. We assess the validity of the
method in simulations by compressing ground states of the Ising model and
classical handwritten digits. The results show that EF-QAE improves the
performance compared to the standard quantum autoencoder using the same amount
of quantum resources, but at the expense of additional classical optimization.
Therefore, EF-QAE makes the task of compressing quantum information better
suited to be implemented in near-term quantum devices.

本研究提出了增强特征量子自编码器（EF-QAE），通过定义一个参数化的量子电路和特征向量特征不同模型，EF-QAE 实现了更好的压缩量子信息性能，并在近期量子技术能力范围内实现了更好的性能。