Replay-buffer engineering for noise-aware quantum circuit optimization
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The paper introduces GenQAS, a tensor network‑guided reinforcement learning framework that uses a learned local transition model to generate synthetic circuit transitions for prioritized generative replay. By mixing these synthetic transitions with real experience during Double Deep Q‑Network updates, GenQAS addresses sample starvation in quantum architecture search. Across benchmarks ranging from 6 to 15 qubits, the method improves success probabilities, identifies compact circuits, and reduces steps to chemical accuracy by up to 92.7%.
The paper introduces GenQAS, a tensor‑network‑guided reinforcement learning framework that uses a learned local transition model to generate synthetic circuit transitions for prioritized generative replay. By mixing these synthetic transitions with real experience during Double Deep Q‑Network updates, GenQAS addresses sample starvation in quantum architecture search. Experiments on chemical Hamiltonian benchmarks up to 12 qubits and a 15‑qubit Ising model show significant improvements in success probability and circuit compactness, while a noisy 6‑qubit BeH₂ transfer experiment demonstrates a 92.7% reduction in steps to chemical accuracy.
arXiv:2606. 12816v2 Announce Type: replace-cross Abstract: Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors.
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:2607. 28422v1 Announce Type: new Abstract: Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale.
arXiv:2607. 10707v1 Announce Type: cross Abstract: We propose a unified meta-decoding framework for quantum error correction that learns syndrome-to-recovery mappings across multiple stabilizer codes and noise settings, without requiring separate decoders for each configuration.