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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.
The paper reports a large‑scale systematic comparison of 1,368 quantum neural network (QNN) configurations for forecasting the GBP/USD spot exchange rate. By varying encoding methods, ansatz designs, qubit counts, layer depths, and cost functions, the study finds that gate selection and arrangement are more critical than parameter count, and that entanglement is a system‑level property. The top QNN achieves an R² of 0.985, surpassing a classical BiLSTM, while tests on the IQM Emerald device show gate errors and decoherence as major obstacles, with gate choice and circuit depth determining noise resilience.
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:2603. 09789v3 Announce Type: replace-cross Abstract: Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data.
arXiv:2606. 28252v1 Announce Type: cross Abstract: Early detection of oral cancer markedly improves clinical outcomes, yet specialized diagnostic tools remain scarce in low-resource settings.
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.