arXiv:2606. 12816v2 Announce Type: replace-cross Abstract: Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors.
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Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors. Routes that appear efficient by standard overhead metrics can still lose fidelity when they pass through poorly calibrated couplers.
arXiv:2604.21863v2 Announce Type: replace-cross Abstract: Deep reinforcement learning for quantum circuit optimization faces three bottlenecks: replay buffers that overlook temporal difference (TD) t...
The paper introduces a low‑overhead, fidelity‑aware scheduling framework for multi‑QPU quantum computing systems. It employs a Graph Neural Network to predict the expected execution fidelity of a given quantum circuit on each available QPU before compilation. Using these predictions, a tunable scheduler balances execution fidelity against parallelism, achieving near‑optimal fidelity assignments while reducing the computational cost compared to brute‑force compilation on every device.
The paper introduces a reinforcement‑learning framework that automatically discovers compact parametrized quantum circuits for modeling power GaN HEMTs and logic nanowire FETs. Using a graph neural network policy trained with proximal policy optimization, the method optimizes circuit architectures based on leave‑one‑group‑out cross‑validation error, achieving the lowest mean absolute error across 11 targets compared to six classical baselines. The results show significant reductions in error and variability for key device metrics (Ioff, VTH, SS) on both HEMT and NWFET datasets, demonstrating the viability of RL‑selected quantum circuits as compact, physically consistent surrogates without explicit physical constraints.
The paper implements two quantum graph neural network architectures—Simplified Graph Convolution (SGC) and Linear Graph Convolution (LGC)—and evaluates them on benchmark graph datasets for semi‑supervised learning using quantum simulation. It compares their predictive performance and optimization behavior to classical baselines, finding that the quantum models achieve competitive results with fewer parameters. Additionally, the study provides a cost‑gradient analysis to identify trainable tasks and a classical simulability investigation to determine regimes where the circuits remain robust during training.
The paper introduces APGEM, an adaptive controller that dynamically selects among four error‑mitigation techniques—Zero‑Noise Extrapolation, Probabilistic Error Cancellation, Clifford Data Regression, and Readout Error Mitigation—based on a utility function and Q‑learning scores. Applied to a realistic Delhi‑based Capacitated Vehicle Routing Problem, the adaptive approach improves the quantum reinforcement learning agent’s approximation ratios from 0.84‑0.87 to 0.92‑0.94 under high noise, outperforming constructive heuristics and approaching metaheuristics. The controller’s strategy shifts from a Clifford‑data‑regression‑heavy regime early in training to a balanced use of all techniques as training progresses, demonstrating regime‑dependent selection.