Quantum Neural Networks and Application to
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Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes. In this work, we introduce a training framework that reduces this cost to logarithmic in the number of qubits, making...
Exterior complex scaling enables physics-informed neural networks for quantum scattering
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Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
Announce Type: new Abstract: Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informed neural networks (PINNs) and their domain-decomposed variants (FBPINNs) offer a mesh-free alternative but face convergence challenges when representing complex velocity fields. We present a hybrid quantum-classical FBPINN for acoustic FWI, bringing together quantum computing and classical machine learning, in which...
Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
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Cryptographic Backdoor for Neural Networks: Boon and Bane
arXiv:2509.20714v2 Announce Type: replace Abstract: In this paper we show that cryptographic backdoors in a neural network (NN) can be highly effective in two directions, namely mounting the attacks as well as in presenting the defenses as well. On the attack side, a carefully planted cryptographic backdoor enables powerful and invisible attack on the NN. Considering the defense, we present applications:
Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
arXiv:2605.28690v2 Announce Type: replace-cross Abstract: Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states characterizing the heterogeneity of a target system. Preparing such ensembles state-by-state is prohibitive in both variational and fault-tolerant settings, motivating a generative-modeling approach. We introduce latent-conditioned parameterized quantum circuits (LPQCs), a hybrid...
Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks This post is a high-level explainer for my Master’s thesis, which involves designing hardware architectures for ultrafast inference and online learning using the Kolmogorov-Arnold Network (KAN) architecture. I’ll assume familiarity with standard machine learning concepts, as well as some understanding of hardware and digital circuits; read my previous post here for the latter. Please read the two papers below for more...
Towards interpretable AI with quantum annealing feature selection
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Measurement of reactor neutrino oscillation with the first JUNO data
Abstract Neutrino oscillations (see refs. 1,2 and references therein), a quantum effect manifesting at macroscopic scales, are governed by lepton flavour mixing angles and neutrino mass-squared differences3 that are fundamental parameters of particle physics, representing phenomena beyond the Standard Model. Precision measurements of these parameters are essential for testing the completeness of the three-flavour framework, determining the mass ordering of neutrinos and probing possible new...
Crystal Nights by Greg Egan
Publication history - Interzone #215, April 2008. - Free podcast at Transmissions From Beyond. [Site no longer active] - Oceanic (collection, Orion) -