Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms, utilizing minimal measurements. While the random measurement approach has been instrumental in this context, the quasi-exponential computational demand with increasing qubit count hurdles its feasibility in large-qubit scenarios. To bridge this knowledge gap, here we introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities: measurement outcomes and classical description of compiled circuits on explored quantum devices, both enriched with unique information. Building upon this insight, we devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation. The learned representation can effectively characterize the similarity between the explored quantum devices when executing new quantum algorithms not present in the training data. We evaluate our proposal on platforms featuring diverse noise models, encompassing system sizes up to 50 qubits. The achieved results demonstrate a three-orders-of-magnitude improvement in prediction accuracy compared to the random measurements and offer compelling evidence of the complementary roles played by each modality in cross-platform verification. These findings pave the way for harnessing the power of multimodal learning to overcome challenges in wider quantum system learning tasks.

翻译：交叉平台验证是早期量子计算领域的一项关键任务，旨在利用最少的测量次数，表征执行相同算法的两个有缺陷量子设备的相似性。尽管随机测量方法在此情境中发挥了重要作用，但随着量子比特数增加，其准指数级的计算需求阻碍了其在大量子比特场景中的可行性。为填补这一知识空白，本文引入了一种创新的多模态学习方法，认识到该任务中数据的形式化表达包含两种不同模态：测量结果与所探索量子设备上编译电路的经典描述，两者均蕴含独特信息。基于这一洞察，我们设计了一个多模态神经网络，从这些模态中独立提取知识，随后通过融合操作形成全面的数据表征。当执行训练数据中未出现的新量子算法时，学得的表征能有效刻画所探索量子设备间的相似性。我们在包含高达50个量子比特、具有不同噪声模型的平台上评估了该方案。最终结果显示，与随机测量相比，预测精度实现了三个数量级的提升，并有力证明了每种模态在交叉平台验证中的互补作用。这些发现为利用多模态学习克服更广泛量子系统学习任务中的挑战铺平了道路。