In the current NISQ era, the performance of QNN models is strictly hindered by the limited qubit number and inevitable noise. A natural idea to improve the robustness of QNN is the implementation of a distributed system. Nevertheless, due to the heterogeneity and instability of quantum chips (e.g., noise, frequent online/offline), training and inference on distributed quantum devices may even destroy the accuracy. In this paper, we propose HeteroQNN, a comprehensive QNN framework designed for efficient and high-accuracy distributed training and inference. The main innovation of HeteroQNN is it decouples the QNN circuit into two uniform representations: model vector and behavioral vector. The model vector specifies the gate parameters in the QNN model, while the behavioral vector captures the hardware features when implementing the QNN circuit. To handle the architectural heterogeneity, we introduce personalized QNN models in each QPU and share the gradient among QPUs with homogeneous behavioral vectors. We propose shot-oriented distributed inference, which is much more fine-grained scheduling that can improve accuracy and balance the workload. Finally, by leveraging the hidden homogeneity in the model vector, we present the maintenance for QPU variability. The experiments show that accelerates the training process by 4.03× with 7.87% loss reduction, compared with the previous distributed QNN framework.

In the current NISQ era, the performance of QNN models is strictly hindered by the limited qubit number and inevitable noise. A natural idea to improve the robustness of QNN is to involve multiple quantum devices. Nevertheless, due to the heterogeneity and instability of quantum devices (e.g., noise, frequent online/offline), training and inference on distributed quantum devices may even destroy the accuracy. In this paper, we propose ArbiterQ, a comprehensive QNN framework designed for efficient and high-accuracy training and inference on heterogeneous QPUs. The main innovation of ArbiterQ is it applies personalized models for each QPU via two uniform QNN representations: model vector and behavioral vector. The model vector specifies the logical-level parameters in the QNN model, while the behavioral vector captures the hardware-level features when implementing the QNN circuit. In this manner, by sharing the gradient among QPUs with similar behavioral vectors, we can effectively leverage parallelism while considering heterogeneity. We also propose shot-oriented inference scheduling, which is a much more fine-grained scheduling that can improve accuracy and balance the workload. The experiments show that ArbiterQ accelerates the training process by $4.03 \times$ with $7.87 \%$ loss reduction, compared with the previous distributed QNN framework EQC [1].

Quantum readout noise turns out to be the most significant source of error, which greatly affects the measurement fidelity. Matrix-based calibration has been demonstrated to be effective in various quantum platforms. However, existing methodologies are fundamentally limited in either scalability or accuracy. Inspired by the classical finite element method (FEM), a formal method to model the complex interaction between elements, we present our calibration framework named QuFEM. First, we apply a divide-and-conquer strategy that formulates the calibration as a series of tensor products with noise matrices. This matrices are iteratively characterized together with the calibrated probability distribution, aiming to capture the inherent locality of qubit interactions. Then, to accelerate the end-to-end calibration, we propose a sparse tensor-product engine to exploit the sparsity in the intermediate values. Our experiments show that QuFEM achieves 2.5×103× speedup in the 136-qubit calibration compared to the state-of-the-art matrix-based calibration technique [50], and provides 1.2× and 1.4× fidelity improvement on the 18-qubit and 36-qubit real-world quantum devices.

In the current Noisy Intermediate-Scale Quantum era, quantum circuit analysis is an essential technique for designing high-performance quantum programs. Current analysis methods exhibit either accuracy limitations or high computational complexity for obtaining precise results. To reduce this tradeoff, we propose QuCT, a unified framework for extracting, analyzing, and optimizing quantum circuits. The main innovation of QuCT is to vectorize each gate with each element, quantitatively describing the degree of the interaction with neighboring gates. Extending from the vectorization model, we propose two representative downstream models for fidelity prediction and unitary decomposition. The fidelity prediction model performs a linear transformation on all gate vectors and aggregates the results to estimate the overall circuit fidelity. By identifying critical weights in the transformation matrix, we propose two optimizations to improve the circuit fidelity. In the unitary decomposition model, we significantly reduce the search space by bridging the gap between unitary and circuit via gate vectors. Experiments show that QuCT improves the accuracy of fidelity prediction by 4.2 × on 5-qubit and 18-qubit quantum devices and achieves 2.5 × fidelity improvement compared to existing quantum compilers [19, 55]. In unitary decomposition, QuCT achieves 46.3 × speedup for 5-qubit unitary and more than hundreds of speedup for 8-qubit unitary, compared to the state-of-the-art method [87].