  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.
