Near-term quantum systems tend to be noisy. Crosstalk noise has been
recognized as one of several major types of noises in superconducting Noisy
Intermediate-Scale Quantum (NISQ) devices. Crosstalk arises from the concurrent
execution of two-qubit gates on nearby qubits, such as \texttt{CX}. It might
significantly raise the error rate of gates in comparison to running them
individually. Crosstalk can be mitigated through scheduling or hardware machine
tuning. Prior scientific studies, however, manage crosstalk at a really late
phase in the compilation process, usually after hardware mapping is done. It
may miss great opportunities of optimizing algorithm logic, routing, and
crosstalk at the same time. In this paper, we push the envelope by considering
all these factors simultaneously at the very early compilation stage. We
propose a crosstalk-aware quantum program compilation framework called CQC that
can enhance crosstalk mitigation while achieving satisfactory circuit depth.
Moreover, we identify opportunities for translation from intermediate
representation to the circuit for application-specific crosstalk mitigation,
for instance, the \texttt{CX} ladder construction in variational quantum
eigensolvers (VQE). Evaluations through simulation and on real IBM-Q devices
show that our framework can significantly reduce the error rate by up to
6$\times$, with only $\sim$60\% circuit depth compared to state-of-the-art gate
scheduling approaches. In particular, for VQE, we demonstrate 49\% circuit
depth reduction with 9.6\% fidelity improvement over prior art on the H4
molecule using IBMQ Guadalupe. Our CQC framework will be released on GitHub.

本文提出了一种名为 CQC 的考虑到诸多因素的抗串扰量子程序编译框架，它能够在保持电路深度的同时提高抗串扰能力，特别是 VQA 算法中，我们演示了使用 IBM Q Guadalupe 的 H4 分子，相对于现有技术，我们的框架降低了 49％的电路深度，并提高了 9.6％的保真度。

量子机器中解决串扰问题的协同编译工作流

A Synergistic Compilation Workflow for Tackling Crosstalk in Quantum Machines

Applications such as simulating complicated quantum systems or solving
large-scale linear algebra problems are very challenging for classical
computers due to the extremely high computational cost. Quantum computers
promise a solution, although fault-tolerant quantum computers will likely not
be available in the near future. Current quantum devices have serious
constraints, including limited numbers of qubits and noise processes that limit
circuit depth. Variational Quantum Algorithms (VQAs), which use a classical
optimizer to train a parametrized quantum circuit, have emerged as a leading
strategy to address these constraints. VQAs have now been proposed for
essentially all applications that researchers have envisioned for quantum
computers, and they appear to the best hope for obtaining quantum advantage.
Nevertheless, challenges remain including the trainability, accuracy, and
efficiency of VQAs. Here we overview the field of VQAs, discuss strategies to
overcome their challenges, and highlight the exciting prospects for using them
to obtain quantum advantage.

该文综述了 VQAs 领域，讨论了克服困难的策略，并突出了使用 VQAs 获得量子优势的激动人心前景。

变分量子算法

Variational Quantum Algorithms

Near-term quantum computers are noisy, and therefore must run algorithms with
a low circuit depth and qubit count. Here we investigate how noise affects a
quantum neural network (QNN) for state discrimination, applicable on near-term
quantum devices as it fulfils the above criteria. We find that when simulating
gradient calculation on a noisy device, a large number of parameters is
disadvantageous. By introducing a new smaller circuit ansatz we overcome this
limitation, and find that the QNN performs well at noise levels of current
quantum hardware. We also show that networks trained at higher noise levels can
still converge to useful parameters. Our findings show that noisy quantum
computers can be used in applications for state discrimination and for
classifiers of the output of quantum generative adversarial networks.

本篇研究探究了 QNN 在噪声量较高的量子计算机上进行状态辨别的效果，通过引入新的电路技术，并在当前的量子硬件噪声水平下对其进行验证，发现其在较高噪声水平下仍能收敛于有用的参数并用于量子生成对抗网络的分类器。