20 citations · 52 across the 16 of their papers we have counts for
16 papers
Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks
Soraya V. Panambalom, Edoardo Altamura, Nick Chancellor +1
As quantum hardware scales to larger devices, the classical software layers that interface with it must evolve in step. Postprocessing routines developed and tested primarily in si…
Sensitivity of Next-Generation CMB Surveys to Neutrinos and Other Light Relics
Cynthia Trendafilova, Srinivasan Raghunathan, Benjamin Wallisch +33
Neutrinos and other light relics leave characteristic imprints in the cosmic microwave background anisotropies, making their observation a sensitive probe of the particle content a…
Quantum chemistry with provable convergence via randomized sample-based Krylov quantum diagonalization
Samuele Piccinelli, Alberto Baiardi, Stefano Barison +12
Quantum algorithms based on classical processing of individual samples have recently emerged as a promising route to perform large-scale quantum-chemical calculations on pre-fault-…
Imaginary Time Spectral Transforms for Excited State Preparation
D. A. Millar, L. W. Anderson, E. Altamura +4
Excited states of many-body quantum systems play a key role in a wide range of physical and chemical phenomena. Despite this, there is a notable lack of scalable algorithms capable…
Approximate quantum circuit compilation for proton-transfer kinetics on quantum processors
Arseny Kovyrshin, Dilhan Manawadu, Edoardo Altamura +15
Proton transfer reactions are fundamental to many chemical and biological systems, where quantum effects such as tunneling, delocalization, and zero-point motion play key kinetic c…
Encoding molecular structures in quantum machine learning
Choy Boy, Edoardo Altamura, Dilhan Manawadu +3
Quantum machine learning (QML) has great potential for the analysis of chemical datasets. However, conventional quantum data-encoding schemes, such as fingerprint encoding, are gen…
Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
M. Emre Sahin, Edoardo Altamura, Oscar Wallis +6
We present Qiskit Machine Learning (ML), a high-level Python library that combines elements of quantum computing with traditional machine learning. The API abstracts Qiskit's primi…
Entropy plateaus can emerge from gas replacement at a characteristic halo mass in simulated groups and clusters of galaxies
Edoardo Altamura, Scott T. Kay, Joop Schaye +2
The evolution of the intergalactic medium (IGM) is influenced by gravitational collapse, radiative cooling, and baryonic feedback. Using cosmological hydrodynamic zoom-in simulatio…
Evolving a multi-population evolutionary-QAOA on distributed QPUs
Francesca Schiavello, Edoardo Altamura, Ivano Tavernelli +2
Our work integrates an Evolutionary Algorithm (EA) with the Quantum Approximate Optimization Algorithm (QAOA) to optimize ansatz parameters in place of traditional gradient-based m…
Building models of the Universe with hydrodynamic simulations
Edoardo Altamura
Hydrodynamic simulations have become irreplaceable in modern cosmology for exploring complex systems and making predictions to steer future observations. In Chapter 1, we begin wit…
Inferring the dark matter splashback radius from cluster gas and observable profiles in the FLAMINGO simulations
Imogen Towler, Scott T. Kay, Joop Schaye +10
The splashback radius, coinciding with the minimum in the dark matter radial density gradient, is thought to be a universal definition of the edge of a dark matter halo. Observatio…
Galaxy cluster rotation revealed in the MACSIS simulations with the kinetic Sunyaev-Zeldovich effect
Edoardo Altamura, Scott T. Kay, Jens Chluba +1
The kinetic Sunyaev-Zeldovich (kSZ) effect has now become a clear target for ongoing and future studies of the cosmic microwave background (CMB) and cosmology. Aside from the bulk…
The impact of stochastic modeling on the predictive power of galaxy formation simulations
Josh Borrow, Matthieu Schaller, Yannick M. Bahe +5
All modern galaxy formation models employ stochastic elements in their sub-grid prescriptions to discretise continuous equations across the time domain. In this paper, we investiga…
Gas clumping and its effect on hydrostatic bias in the MACSIS simulations
Imogen Towler, Scott Kay, Edoardo Altamura
We use the MACSIS hydrodynamical simulations to estimate the extent of gas clumping in the intracluster medium of massive galaxy clusters and how it affects the hydrostatic mass bi…
EAGLE-like simulation models do not solve the entropy core problem in groups and clusters of galaxies
Edoardo Altamura, Scott T. Kay, Richard G. Bower +5
Recent high-resolution cosmological hydrodynamic simulations run with a variety of codes systematically predict large amounts of entropy in the intra-cluster medium at low redshift…
Serendipitous discovery of a physical binary quasar at z=1.76
E. Altamura, S. Brennan, A. Leśniewska +7
Binary quasars are extremely rare objects, used to investigate clustering on very small scales at different redshifts. The cases where the two quasar components are gravitationally…