The paper introduces LCAP, a method for adapting photonic neural networks to real hardware by learning a shared correction from a population of chips and then personalizing each chip using only 32 fixed output probes. LCAP decomposes adaptation into a transferable population correction and a probe‑inferred latent personalization, allowing feed‑forward calibration without device‑specific optimization. Experiments on a simulated three‑layer 64‑mode MZI network show accuracy improvements from 80.4% to 93.4% and significant gains on unseen chips.
arXiv:2607. 19421v1 Announce Type: cross Abstract: Silicon-photonic (SiPh) accelerators have emerged as a promising platform for Vision Transformer (ViT) inference by performing matrix multiplications on microring-resonator (MRR) banks with high throughput and energy efficiency.
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.
arXiv:2604. 02429v2 Announce Type: replace-cross Abstract: Convolutional neural networks (CNNs) have transformed image processing, but the energy consumption and inference latency of electronic based implementations remain fundamental bottlenecks.
arXiv:2608. 02229v1 Announce Type: new Abstract: Classical neural networks frequently produce overconfident predictions on ambiguous or out-of-distribution (OOD) data, a liability that grows with each AI system deployed in safety-critical real-world scenarios.
arXiv:2606. 10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision.
PICasso is an AI‑enabled framework that converts natural‑language specifications into manufacturable silicon photonic integrated circuits (PICs) through a structured pipeline of NL → YAML → GDS, PDK‑aware knowledge injection, automated placement and routing, DRC/LVS validation, and SAX‑based photonic simulation. The authors introduce PIC‑Set, a benchmark of 36 parameterized PIC design tasks, and evaluate several large language models (LLMs) using new metrics such as structural and functional Spec@k, optimization efficiency, and robustness. Across the benchmark, PICasso markedly improves specification satisfaction, achieving up to 92.7% structural Spec@3 and 52% functional Spec@3, while reducing mean insertion loss from 4.98 dB to 3.25 dB through simulation‑guided optimization.
The paper introduces a hardware‑aware framework that uses genetic programming to evolve layer‑specific scalar functions for Vision Transformers, replacing traditional LayerNorm with efficient, heterogeneous approximations. By applying a post‑training re‑alignment strategy, the method eliminates the need for full model retraining while achieving 90‑93% variance capture and recovering over 84% of ImageNet‑1K Top‑1 accuracy for ViT‑B and ViT‑L. The resulting architecture removes the global reduction bottleneck, reducing arithmetic complexity and off‑chip memory traffic, thereby enabling efficient deployment of ViTs on edge accelerators.
arXiv:2607. 20045v1 Announce Type: cross Abstract: Physical noise in near-term quantum hardware is usually treated as a nuisance to suppress.
arXiv:2607. 05814v1 Announce Type: cross Abstract: Real-time decoding is a major bottleneck in scaling quantum error correction (QEC) from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing.
The paper investigates how postprocessing routines in quantum neural network software can cause significant data loss when run on large quantum hardware. In a case study of Qiskit’s “SamplerQNN”, a filter that assumes measurement bit‑strings are in virtual qubit space removed 85–99.6% of valid shots on IBM backends, leading to unnormalised probability vectors and distorted predictions. This loss caused inference accuracy to drop from 0.94 to 0.39 and compressed training loss signals by 22–27×, severely reducing optimizer sensitivity. The authors implemented a layout‑based marginalisation fix that was merged into the library to make “SamplerQNN” forward‑compatible with current and future hardware.
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:2606. 03038v1 Announce Type: new Abstract: Neural field surrogates can accelerate photonic design loops, but a surrogate that looks accurate in global field error can still mis-rank candidate devices when the final decision depends on localized output-port readouts.