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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…
Conditional Progressive Generative Adversarial Network for satellite image generation
Renato Cardoso, Sofia Vallecorsa, Edoardo Nemni
Image generation and image completion are rapidly evolving fields, thanks to machine learning algorithms that are able to realistically replace missing pixels. However, generating…
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…
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…
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…
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…
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 computing for data analysis in high energy physics
Andrea Delgado, Kathleen E. Hamilton, Prasanna Date +22
Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and o…
Dual-Tasks Siamese Transformer Framework for Building Damage Assessment
Hongruixuan Chen, Edoardo Nemni, Sofia Vallecorsa +3
Accurate and fine-grained information about the extent of damage to buildings is essential for humanitarian relief and disaster response. However, as the most commonly used archite…