Current quantum portfolio optimization pipelines rely on Random Matrix Theory (RMT) forcorrelation matrix cleaning, a static linear method that cannot adapt when market regimesshift. Meanwhile, D-Wave's own operational audit shows the quantum processor runs for just0.68% of total computation time, with input quality driving all output quality essentially. Thispaper proposes using Quantum Neural Networks (QNNs) — or, as a near-term fallback,classical neural networks — as an adaptive preprocessing layer before quantum portfoliooptimization. QNNs offer provable universal approximation guarantees, non-linear correlationmodeling, and regime-aware adaptation. Combined with quantum annealing or gate-modelVariational Quantum Eigensolvers (VQE/QAOA) for the actual allocation step, this creates atwo-stage pipeline where better inputs lead to meaningfully better portfolios. We present themathematical foundations, compare the approach against RMT, discuss hardware-awaredesign trade-offs, and outline what can be built today on standard hardware.
Abstract: 5G is rolled out and next generation 6G networks are also being developed, ultra-low latency (URLL) communication as a standard is critical in supporting the plethora of applications, spanning autonomous vehicles, immersive extended reality experience, etc. However, tra…
This repository contains the Juypter Notebooks and python files to reproduce the main results of the paper: Quantum neural networks for cloud cover parameterizations in climate models, Lorenzo et al. 2026
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