Blog
Traffic-Oblivious Multi-Commodity Flow Network Design
We consider the Minimum Multi-Commodity Flow Subgraph (MMCFS) problem: given a directed graph $G$ with edge capacities $\mathit{cap}$ and a retention ratio $\alpha\in(0,1)$, find an edge-wise minimum subgraph $G’ \subseteq G$ such that for all traffic matrices $T$ routable in $G$ using...
Beating the break-even point with autonomous quantum error correction
Quantum error correction (QEC) is essential for practical quantum computing, as it protects fragile quantum information from errors by encoding it in high-dimensional Hilbert spaces. Conventional QEC protocols typically require repeated syndrome measurements, real-time feedback, and the use of multiple physical qubits...
Matter-antimatter asymmetry in generalized coupling theories
We explore the gravitational baryogenesis paradigm in the homogeneous and isotropic cosmology of generalized coupling gravity and, in particolare, of the so-called Minimal Exponential Measure Model (MEMe). We show that, also in this theory, the time derivative of the Ricci scalar couples...
QAOA-PCA: Enhancing Efficiency in the Quantum Approximate Optimization Algorithm via Principal Component Analysis
The Quantum Approximate Optimization Algorithm (QAOA) is a promising variational algorithm for solving combinatorial optimization problems on near-term devices. Tuttavia, as the number of layers in a QAOA circuit increases, which is correlated with the quality of the solution, the number of...
Noise-Tolerant Coreset-Based Class Incremental Continual Learning
Many applications of computer vision require the ability to adapt to novel data distributions after deployment. Adaptation requires algorithms capable of continual learning (CL). Continual learners must be plastic to adapt to novel tasks while minimizing forgetting of previous tasks.However, CL opens...
A Note on the Stability of the Dark Energy Model from Time Crystals
In this note, we investigate the stability of the dark energy model from time crystals proposed in [1]. We emphasize two ingredients, the coupling of the scalar field to gravity, and the fact that these time crystals are on an expanding FRW...
Reduction of $ε$-expanded Feynman integrals
Since Feynman integrals (FIs) at higher spacetime dimensions are free of infrared and collinear divergence–and their ultraviolet divergences can be systematically subtracted–this allows us to construct a wide range of locally finite Feynman integrals. Especially, we propose a method named $\bar{R}$-operation to...
Online model learning with data-assimilated reservoir computers
We propose an online learning framework for forecasting nonlinear spatio-temporal signals (fields). The method integrates (io) dimensionality reduction, here, a simple proper orthogonal decomposition (POD) projection; (ii) a generalized autoregressive model to forecast reduced dynamics, here, a reservoir computer; (iii) online adaptation...
Deep photonic reservoir computer for nonlinear equalization of 16-level quadrature amplitude modulation signals
Photonic reservoir computer (PRC) is a kind of real-time and adaptive recurrent neural network, where only weights in the readout layer require training. PRC is a promising tool to deal with the crucial issue of nonlinear equalization in optical fiber communications. Here...
Projective Variety Recovery from Unknown Linear Projections
We study how a smooth irreducible algebraic variety $X$ of dimension $n$ embedded in $\mathbb{C} \mathbb{P}^{M}$ (with $m \geq n+2$), which degree is $d$, can be recovered using two projections from unknown points onto unknown hyperplanes. The centers and the hyperplanes of...