To demonstrate supremacy of quantum computing, increasingly large-scale superconducting quantum computing chips are being designed and fabricated, sparking the demand for electronic design automation in pursuit of better efficiency and effectiveness. However, the complexity of simulating quantum systems poses a significant challenge to computer-aided design of quantum chips. Harnessing the scalability of graph neural networks (GNNs), we here propose a parameter designing algorithm for large-scale superconducting quantum circuits. The algorithm depends on the so-called 'three-stair scaling' mechanism, which comprises two neural-network models: an evaluator supervisedly trained on small-scale circuits for applying to medium-scale circuits, and a designer unsupervisedly trained on medium-scale circuits for applying to large-scale ones. We demonstrate our algorithm in mitigating quantum crosstalk errors, which are commonly present and closely related to the graph structures and parameter assignments of superconducting quantum circuits. Parameters for both single- and two-qubit gates are considered simultaneously. Numerical results indicate that the well-trained designer achieves notable advantages not only in efficiency but also in effectiveness, especially for large-scale circuits. For example, in superconducting quantum circuits consisting of around 870 qubits, the trained designer requires only 27 seconds to complete the frequency designing task which necessitates 90 minutes for the traditional Snake algorithm. More importantly, the crosstalk errors using our algorithm are only 51% of those produced by the Snake algorithm. Overall, this study initially demonstrates the advantages of applying graph neural networks to design parameters in quantum processors, and provides insights for systems where large-scale numerical simulations are challenging in electronic design automation.

Despite the success of Quantum Neural Networks (QNNs) in decision-making systems, their fairness remains unexplored, as the focus primarily lies on accuracy. This work conducts a design space exploration, unveiling QNN unfairness, and highlighting the significant influence of QNN deployment and quantum noise on accuracy and fairness. To effectively navigate the vast QNN deployment design space, we propose JustQ, a framework for deploying fair and accurate QNNs on NISQ computers. It includes a complete NISQ error model, reinforcement learning-based deployment, and a flexible optimization objective incorporating both fairness and accuracy. Experimental results show JustQ outperforms previous methods, achieving superior accuracy and fairness. This work pioneers fair QNN design on NISQ computers, paving the way for future investigations.

Current advancements in technology have focused the attention of the quantum computing community toward exploring the potential of near-term devices whose computing power surpasses that of classical computers in practical applications. An unresolved central question revolves around whether the inherent noise in these devices can be overcome or whether any potential quantum advantage would be limited. There is no doubt that crosstalk is one of the main sources of noise in noisy intermediate-scale quantum (NISQ) systems, and it poses a fundamental challenge to hardware designs. Crosstalk between parallel instructions can corrupt quantum states and cause incorrect program execution. In this study, we present a comprehensive analysis of the crosstalk error effect on NISQ computers. Our approach is extremely straightforward and practical for characterizing the crosstalk error of various multi-qubit devices. In particular, we combine the randomized benchmarking (RB) and simultaneous randomized benchmarking (SRB) protocol to characterize the crosstalk error from the correlation controlled-NOT (CNOT) gate. We demonstrate this protocol experimentally on 5- \& 7-qubit devices. Our results demonstrate the crosstalk error model of two different IBM quantum devices over the experimental week and compare the error variation against the machine, number of qubits, quantum volume, processor, and topology of the IBM quantum devices. We then confirm the improvement in the circuit fidelity on different benchmarks by up to 3.06x via inserting an instruction barrier, as compared with an IBM quantum noisy device which offers near-optimal crosstalk mitigation in practice. Most importantly, we provide insight to ensure that the quantum operation can perform its quantum magic undisturbed.

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.