211 citations · 1.4k across the 42 of their papers we have counts for
14 papers · 1 filter
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
Massimiliano Incudini, Michele Grossi, Antonio Mandarino +3
Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of cla…
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…
Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines
Michael Schenk, Elías F. Combarro, Michele Grossi +4
Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann machines (QBM) was shown to significantly improve the learning efficiency compared to classical Q-lea…
Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection
Michele Grossi, Noelle Ibrahim, Voica Radescu +4
This paper presents a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM…
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…
Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning
Francesco Di Marcantonio, Massimiliano Incudini, Davide Tezza +1
Exploiting the properties of quantum information to the benefit of machine learning models is perhaps the most active field of research in quantum computation. This interest has su…
Running the Dual-PQC GAN on noisy simulators and real quantum hardware
Su Yeon Chang, Edwin Agnew, Elías F. Combarro +3
In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN. We applied the model on a…
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…
IBM quantum platforms: a quantum battery perspective
Giulia Gemme, Michele Grossi, Dario Ferraro +2
We characterize for the first time the performances of IBM quantum chips as quantum batteries, specifically addressing the single-qubit Armonk processor. By exploiting the Pulse ac…
Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research
Travis S. Humble, Andrea Delgado, Raphael Pooser +23
Quantum computing offers a new paradigm for advancing high-energy physics research by enabling novel methods for representing and reasoning about fundamental quantum mechanical phe…
Impact of quantum noise on the training of quantum Generative Adversarial Networks
Kerstin Borras, Su Yeon Chang, Lena Funcke +8
Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different e…
Quantum integration of elementary particle processes
Gabriele Agliardi, Michele Grossi, Mathieu Pellen +1
We apply quantum integration to elementary particle-physics processes. In particular, we look at scattering processes such as and ${\rm e}^+{\rm e…