arXiv:2511. 12482v2 Announce Type: replace-cross Abstract: Quantum error correction is essential for fault-tolerant quantum computing.
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:2608. 15760v1 Announce Type: cross Abstract: Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve logical quantum information.
The paper proposes a hybrid, learning‑based strategy for selecting quantum error‑correcting codes at each concatenation level. By estimating the effective noise channel after each level, the method chooses small, non‑additive encoders when the noise has structure and switches to standard codes as the noise becomes uniform. Simulations show that this adaptive approach can achieve a target logical error rate with up to two orders of magnitude fewer qubits than using stabilizer codes alone for strongly structured noise.
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.
arXiv:2607. 21121v1 Announce Type: cross Abstract: In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed.
arXiv:2606. 18503v1 Announce Type: new Abstract: Remaining useful life (RUL) estimation is central to predictive maintenance, where an unplanned failure can cost far more than the asset itself.
arXiv:2507. 18606v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks.
The paper introduces a hybrid quantum–classical regression framework that uses a lightweight classical embedding as a learnable geometric preconditioner to improve the conditioning of a downstream variational quantum circuit. It further incorporates a curriculum optimization protocol that gradually increases circuit depth and switches from SPSA-based exploration to Adam-based fine‑tuning. Experiments on PDE‑informed and standard regression datasets show that this approach consistently outperforms pure QNN baselines, yielding more stable convergence and reduced structured errors, especially in data‑limited regimes.
arXiv:2607. 19563v1 Announce Type: cross Abstract: Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for that require costly decoder-level information.
The paper introduces Adaptive Policy-Guided Error Mitigation (APGEM), a context-aware layer that dynamically selects error mitigation strategies—such as ZNE, PEC, CDR, and REM—during quantum reinforcement learning (QRL) training on NISQ devices. APGEM uses policy-level indicators (quantum-state fidelity, policy entropy, cumulative reward, and approximation ratio) to choose the most suitable mitigation method and integrates it directly into the reinforcement learning loop. Evaluated on the Capacitated Vehicle Routing Problem under various NISQ noise models, APGEM outperforms static mitigation techniques, achieving about 94% of an oracle strategy’s utility, maintaining higher fidelity as noise increases, and producing more stable learning behavior.
Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement. However, its role in scientific machine learning remains largely unexplored.
arXiv:2605. 22097v2 Announce Type: replace-cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints.