278 citations · 2k across the 57 of their papers we have counts for
59 papers · 1 filter
Benchmarking Quantum Convolutional Neural Networks and their classical surrogates on long-range strongly correlated models
Tatiana A. Bespalova, Réka Schwengelbeck, Laura Mismetti +3
We benchmark quantum convolutional neural networks (QCNNs) and their classical surrogates for phase differentiation in one-dimensional strongly correlated systems with beyond-neare…
Shaping double descent in quantum extreme learning through model design
Sabri Meyer, Ege Yilmaz, Aurelien Lucchi +2
Increasing capacity can induce a machine learning model to generalize worse until it perfectly interpolates the training data, and better once it moves beyond interpolation. This i…
Computing noise-canceling observables via Pauli propagation
Andrew Eddins, Caleb Johnson, Alberto Baiardi +8
The pursuit of quantum advantage is driving the co-evolution of quantum processors and classical simulation methods. Despite advances in scale and quality, the accuracy of quantum…
String dynamics of a (2+1)D U(1) quantum link model on a digital quantum computer
Anthony Gandon, Alessandro Mariani, Debasish Banerjee +5
The (2+1)D U(1) pure gauge theory always exists in the confining phase, with strings of non-zero string tension giving a characteristic linear potential between static charges. Thi…
Combining non-parametric quantum states and MERA tensor networks for ground-state optimization
Julian Schuhmacher, Alberto Baiardi, Francesco Tacchino +1
Hybrid tensor networks offer a promising route to enhance the expressivity of classical tensor network methods by incorporating quantum states prepared on a quantum computer. Exist…
Bowtie VarQTE: A Resource-Efficient Quantum State Preparation Primitive
Marc Drudis, Alberto Baiardi, Mattia Chiurco +3
The preparation of quantum states is a fundamental requirement for many quantum algorithms. A native route to preparing physically structured states is based on short-time simulati…
Breaking concentration barriers for quantum extreme learning on digital quantum processors
Timothée Dao, Ege Yilmaz, Ibrahim Shehzad +8
Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their pra…
Exploring pathways towards quantum advantage in quantum chemistry: the case of a molecule with half-Möbius topology
Samuele Piccinelli, Stefano Barison, Alberto Baiardi +8
We report quantum chemistry calculations performed on superconducting quantum processors for a molecule exhibiting the half-Möbius electronic topology originally introduced by Ronč…
Stabilizer-based quantum simulation of fermion dynamics with local qubit encodings
Anthony Gandon, Samuele Piccinelli, Max Rossmannek +4
Simulating the dynamical properties of large-scale many-fermion systems is a longstanding goal of quantum chemistry, material science and condensed matter. Local fermion-to-qubit e…
Optimizing two-dimensional isometric tensor networks with quantum computers
Sebastian Leontica, Alberto Baiardi, Julian Schuhmacher +2
We propose a hybrid quantum-classical algorithm for approximating the ground state of two-dimensional quantum systems using an isometric tensor network ansatz, which maps naturally…
Large-scale implementation of quantum subspace expansion with classical shadows
Laurin E. Fischer, Daniel Bultrini, Ivano Tavernelli +1
Quantum subspace expansion (QSE) offers promising avenues to perform spectral calculations on quantum processors but comes with a large measurement overhead. Informationally comple…
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…
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-…
Resource-Efficient Simulations of Particle Scattering on a Digital Quantum Computer
Yahui Chai, Joe Gibbs, Vincent R. Pascuzzi +4
We develop and demonstrate methods for simulating the scattering of particle wave packets in the interacting Thirring model on digital quantum computers, with hardware implementati…
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…
Gradient Scalability and Taylor Surrogation of Quantum Cost Landscapes
Sabri Meyer, Francesco Scala, Francesco Tacchino +1
Variational Quantum Algorithms are promising candidates for near-term quantum computing, yet they face scalability challenges due to barren plateaus, where gradients vanish exponen…
A circuit-differentiation framework for Green's functions on quantum computers
Samuele Piccinelli, Francesco Tacchino, Ivano Tavernelli +1
We propose a general framework for computing Retarded Green's Functions (RGFs) on quantum computers by recasting their evaluation as a problem of circuit differentiation. Our propo…
Towards Practical Quantum Neural Network Diagnostics with Neural Tangent Kernels
Francesco Scala, Christa Zoufal, Dario Gerace +1
Knowing whether a Quantum Machine Learning model would perform well on a given dataset before training it can help to save critical resources. However, gathering a priori informati…
Dynamical simulations of many-body quantum chaos on a quantum computer
Laurin E. Fischer, Matea Leahy, Andrew Eddins +16
Quantum circuits with local unitaries have emerged as a rich playground for the exploration of many-body quantum dynamics of discrete-time systems. While the intrinsic locality mak…
Simulating the Fermi-Hubbard model with long-range hopping on a quantum computer
Tatiana A. Bespalova, Karlo Delić, Guido Pupillo +2
We investigate the performance and accuracy of digital quantum algorithms for the study of static and dynamic properties of the fermionic Hubbard model at half-filling with next-ne…
Hybrid Tree Tensor Networks for quantum simulation
Julian Schuhmacher, Marco Ballarin, Alberto Baiardi +4
Hybrid Tensor Networks (hTN) offer a promising solution for encoding variational quantum states beyond the capabilities of efficient classical methods or noisy quantum computers al…
Dual frame optimization for informationally complete quantum measurements
Laurin E. Fischer, Timothée Dao, Ivano Tavernelli +1
Randomized measurement protocols such as classical shadows represent powerful resources for quantum technologies, with applications ranging from quantum state characterization and…
Fermionic wave packet scattering: a quantum computing approach
Yahui Chai, Arianna Crippa, Karl Jansen +4
Quantum computing provides a novel avenue towards simulating dynamical phenomena, and, in particular, scattering processes relevant for exploring the structure of matter. However,…
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…