Tootfinder

Color-Rule-Function Encoding for Combinatorial Memory
Alexander Khitun
https://arxiv.org/abs/2606.11365 https://arxiv.org/pdf/2606.11365 https://arxiv.org/html/2606.11365
arXiv:2606.11365v1 Announce Type: new
Abstract: Combinatorial memory is a class of memory in which information is encoded in the set of paths through a structured mesh. In this work, we introduce a systematic encoding framework, referred to as the Color-Rule-Function (CRF) approach, for representing information in combinatorial memory. The method consists of four key steps: selecting a sequence of paths in the mesh, assigning values (e.g., colors) to each cell, defining a set of rules based on the values encountered along each path, and constructing a Boolean function that determines the state of each path. . The coding procedure is illustrated by several examples. The design space scales of the CRF scale fundamentally faster compared to conventional memory. This apparent advantage arises from the use of rule-based and functional representations but is accompanied by increased hardware complexity. A possible hardware realization of the CRF framework is discussed. Importantly, the hardware overhead can be substantially reduced through the use of customized modules. The examples of the customized design are described in the text. The combination of CRF coding with customized module design may lead to a practical advantage in data storage density. According to the estimates, the data storage density may exceed Exabit per centimeter squared. A key problem that requires further investigation is related to the minimum Hamming distance between an arbitrary target bit sequence and the closest sequence realizable within the CRF framework under fixed hardware constraints.
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Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[1/5]:
- Tight Bounds for Quantum Phase Estimation and Related Problems
Nikhil S. Mande, Ronald de Wolf
https://arxiv.org/abs/2305.04908 https://mastoxiv.page/@arXiv_quantph_bot/110337460439591382
- Quantum thermodynamics of the Caldeira-Leggett model with non-equilibrium Gaussian reservoirs
Vasco Cavina, Massimiliano Esposito
https://arxiv.org/abs/2405.00215 https://mastoxiv.page/@arXiv_quantph_bot/112370293649586776
- A quantum implementation of high-order power method for estimating geometric entanglement of pure...
Andrii Semenov, Niall Murphy, Simone Patscheider, Alessandra Bernardi, Elena Blokhina
https://arxiv.org/abs/2405.19134 https://mastoxiv.page/@arXiv_quantph_bot/112528797708787049
- Unifying framework for quantum simulation algorithms for time-dependent Hamiltonian dynamics
Yu Cao, Shi Jin, Nana Liu
https://arxiv.org/abs/2411.03180 https://mastoxiv.page/@arXiv_quantph_bot/113435338816776896
- Mixed-State Topological Order under Coherent Noise
Seunghun Lee, Eun-Gook Moon
https://arxiv.org/abs/2411.03441 https://mastoxiv.page/@arXiv_quantph_bot/113440883864687961
- Quest for quantum advantage: Monte Carlo wave-function simulations of the Coherent Ising Machine
Manushan Thenabadu, Run Yan Teh, Jia Wang, Simon Kiesewetter, Margaret D Reid, Peter D Drummond
https://arxiv.org/abs/2501.02681 https://mastoxiv.page/@arXiv_quantph_bot/113786139350946567
- Honest-binding quantum bit commitment from separable operations
Ziad Chaoui, Anna Pappa, Matteo Rosati
https://arxiv.org/abs/2501.07351 https://mastoxiv.page/@arXiv_quantph_bot/113825499573295523
- Expressivity of Quantum Reservoir Computers
Sch\"utte, G\"otting, M\"untinga, List, Brunner, Gies
https://arxiv.org/abs/2501.15528 https://mastoxiv.page/@arXiv_quantph_bot/113904787967762788
- Additivity and chain rules for quantum entropies via multi-index Schatten norms
Omar Fawzi, Jan Kochanowski, Cambyse Rouz\'e, Thomas Van Himbeeck
https://arxiv.org/abs/2502.01611 https://mastoxiv.page/@arXiv_quantph_bot/113944408733032864
- On the Addressability Problem on CSS Codes
J\'er\^ome Guyot, Samuel Jaques
https://arxiv.org/abs/2502.13889 https://mastoxiv.page/@arXiv_quantph_bot/114035128180919513
- Robust Mixed-State Cluster States and Spurious Topological Entanglement Negativity
Seunghun Lee, Eun-Gook Moon
https://arxiv.org/abs/2504.16165 https://mastoxiv.page/@arXiv_quantph_bot/114391788348136547
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Copyright Attorney identifies legal mistakes made by Bricks & Minifigs in their public statement, while trying to deflect blame from their $90M company.
▶️ Fired, closed, blamed: What the Press Release Gave Away - Lawful Masses with Leonard French
https://youtube.com/watch?v=55FMmihiDw

Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data
Isao Kurosawa
https://arxiv.org/abs/2606.29339 https://arxiv.org/pdf/2606.29339 https://arxiv.org/html/2606.29339
arXiv:2606.29339v1 Announce Type: new
Abstract: Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring. In each setting a detector must stay reliable both when sensors fail and when the signal is buried in noise. These two failure modes are routinely conflated, and architectural complexity is often credited with robustness it may not deserve. We assemble a unified binary event-detection benchmark from three physically distinct real sources -- Hi-net seismic waveforms, Utah FORGE 2024 borehole DAS, and MAFAULDA industrial vibration -- each mapped to a common 8-channel, 256-sample representation, and evaluate a fault-tolerant detector (CEPHALON) trained with per-sample sensor-dropout against standard detectors (a 1D convolutional network, a temporal convolutional network, and a compact Transformer) trained with an identical recipe. On clean data every model is near-perfect (AUC ~ 0.99). Under progressive sensor loss, simple models with sensor-dropout are already robust and CEPHALON holds no advantage. Under additive noise, however, CEPHALON degrades far more gracefully: at -2.5 dB its overall AUC is 0.939 versus 0.532-0.572 for the convolutional baselines. Same-architecture ablations isolate the cause: disabling internal redundancy at inference reduces the low-SNR advantage only modestly, whereas removing sensor-dropout training collapses it (0.899 to 0.603 at -5 dB). The training recipe is therefore the dominant cause and parallel redundancy only secondary. We release a complete, numbered, reproducible pipeline so that every figure can be regenerated.
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A New Invariant for Prime Alternating Knots From Error-Correcting Codes
Altan B. Kilic, Ruud Pellikaan, Alberto Ravagnan
https://arxiv.org/abs/2606.10871 https://arxiv.org/pdf/2606.10871 https://arxiv.org/html/2606.10871
arXiv:2606.10871v1 Announce Type: new
Abstract: This paper shows that the Alexander-Briggs code of a knot gives rise to a new invariant that distinguishes prime alternating knots. The restriction to prime alternating knots precisely follows from the fact that our approach relies on Tait s flyping theorem. We also provide examples where the new invariant succeeds in separating knots that the well known invariants, such as some knot polynomials, fail.
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Joint elastic full waveform inversion of multi-component geophone and distributed acoustic sensing data
Hoang Anh Nguyen, Ali Tura
https://arxiv.org/abs/2607.01649 https://arxiv.org/pdf/2607.01649 https://arxiv.org/html/2607.01649
arXiv:2607.01649v1 Announce Type: new
Abstract: Joint full waveform inversion (FWI) of distributed acoustic sensing (DAS) and ocean-bottom node (OBN) data typically requires converting measured strain to particle velocity, introducing numerical noise and spectral distortion. To eliminate this, we present an elastic multi-parameter FWI framework using a velocity-stress-strain (VSS) formulation that directly models pressure, particle velocity, and gauge-length-averaged DAS strain from a single forward simulation. Data residuals are injected additively into a single backward simulation, making computational cost independent of the active sensor subsets. We benchmark individual and combined datasets on cross-talk and elastic Marmousi models. Our results show that joint inversion recovers elastic parameters more accurately than single deployments when the sensors offer complementary information. Specifically, pairing two-component geophones with a deviated borehole DAS cable yields the most accurate parameter recovery and mitigates inter-parameter cross-talk by providing a distinct physical observable and complementary depth aperture. We release our implementation as xFWI, an open-source, Devito-based Python package for scalable, multi-deployment inversions.
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