76 citations · 309 across the 17 of their papers we have counts for
17 papers
When is randomization advantageous in quantum simulation?
Francesco Paganelli, Michele Grossi, Andrea Giachero +2
We study the regimes in which Hamiltonian simulation benefits from randomization. We introduce a sparse-QSVT construction based on composite stochastic decompositions, where domina…
Neutrino thermalization via randomization on a quantum processor
Oriel Kiss, Ivano Tavernelli, Francesco Tacchino +2
The dynamical evolution of neutrino flavor in supernovae can be modeled by an all-to-all spin Hamiltonian with random couplings. Simulating such two-local Hamiltonian dynamics rema…
Toward scalable quantum computations of atomic nuclei
Chenyi Gu, Matthias Heinz, Oriel Kiss +1
We solve the nuclear two-body and three-body bound states via quantum simulations of pionless effective field theory on a lattice in position space. While the employed lattice rema…
Statistics of topological defects across a phase transition in a digital superconducting quantum processor
Oriel Kiss, Daniil Teplitskiy, Michele Grossi +1
When a quantum phase transition is crossed within a finite time, critical slowing down disrupts adiabatic dynamics, resulting in the formation of topological defects. The average d…
Early Fault-Tolerant Quantum Algorithms in Practice: Application to Ground-State Energy Estimation
Oriel Kiss, Utkarsh Azad, Borja Requena +3
We investigate the feasibility of early fault-tolerant quantum algorithms focusing on ground-state energy estimation problems. In particular, we examine the computation of the cumu…
Quantum error mitigation for Fourier moment computation
Oriel Kiss, Michele Grossi, Alessandro Roggero
Hamiltonian moments in Fourier space - expectation values of the unitary evolution operator under a Hamiltonian at different times - provide a convenient framework to understand qu…
Symmetry-invariant quantum machine learning force fields
Isabel Nha Minh Le, Oriel Kiss, Julian Schuhmacher +2
Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate…
Counterdiabatic optimized driving in quantum phase sensitive models
Francesco Pio Barone, Oriel Kiss, Michele Grossi +2
State preparation plays a pivotal role in numerous quantum algorithms, including quantum phase estimation. This paper extends and benchmarks counterdiabatic driving protocols acros…
Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
Paulin de Schoulepnikoff, Oriel Kiss, Sofia Vallecorsa +2
Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies…
Trainability barriers and opportunities in quantum generative modeling
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp +5
Quantum generative models provide inherently efficient sampling strategies and thus show promise for achieving an advantage using quantum hardware. In this work, we investigate the…
Importance sampling for stochastic quantum simulations
Oriel Kiss, Michele Grossi, Alessandro Roggero
Simulating many-body quantum systems is a promising task for quantum computers. However, the depth of most algorithms, such as product formulas, scales with the number of terms in…
Quantum phase detection generalisation from marginal quantum neural network models
Saverio Monaco, Oriel Kiss, Antonio Mandarino +2
Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g. phase diagram. However, access to training labels is a major bottleneck f…
Finite-size criticality in fully connected spin models on superconducting quantum hardware
Michele Grossi, Oriel Kiss, Francesco De Luca +3
The emergence of a collective behavior in a many-body system is responsible of the quantum criticality separating different phases of matter. Interacting spin systems in a magnetic…
Conditional Born machine for Monte Carlo event generation
Oriel Kiss, Michele Grossi, Enrique Kajomovitz +1
Generative modeling is a promising task for near-term quantum devices, which can use the stochastic nature of quantum measurements as a random source. So called Born machines are p…
Quantum computing of the Li nucleus via ordered unitary coupled clusters
Oriel Kiss, Michele Grossi, Pavel Lougovski +3
The variational quantum eigensolver (VQE) is an algorithm to compute ground and excited state energy of quantum many-body systems. A key component of the algorithm and an active re…
Quantum neural networks force fields generation
Oriel Kiss, Francesco Tacchino, Sofia Vallecorsa +1
Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning…
Bayesian Optimization for machine learning algorithms in the context of Higgs searches at the CMS experiment
Oriel Kiss
Machine Learning algorithms, such as Boosted Decisions Trees and Deep Neural Network, are widely used in High-Energy-Physics. The aim of this study is to apply Bayesian Optimizatio…