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Found 11 papers, 2 papers with code
Quantum Theory and Application of Contextual Optimal Transport
In cases where paired data measurements ($\mu$, $\nu$) are coupled to a context variable $p_i$ , one may aspire to learn a global transportation map that can be parameterized through a potentially unseen con-text.
Symmetry-invariant quantum machine learning force fields
Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning, and that chemical systems represent, in fact, an interesting and rich playground for the development and application of advanced quantum machine learning tools.
Unravelling physics beyond the standard model with classical and quantum anomaly detection
Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC).
Quantum anomaly detection in the latent space of proton collision events at the LHC
The designed quantum anomaly detection models, namely an unsupervised kernel machine and two clustering algorithms, are trained to find new-physics events in the latent representation of LHC data produced by the autoencoder.
Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19.
Quantum neural networks force fields generation
Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales.
Variational learning for quantum artificial neural networks
In the last few years, quantum computing and machine learning fostered rapid developments in their respective areas of application, introducing new perspectives on how information processing systems can be realized and programmed.
Quantum Physics
Deterministic Algorithms for Compiling Quantum Circuits with Recurrent Patterns
In particular, such patterns appear in quantum circuits that are used to compute the ground state properties of molecular systems using the variational quantum eigensolver (VQE) method together with the RyRz heuristic wavefunction Ansatz.
Quantum Physics Computational Complexity Data Structures and Algorithms
Non-unitary operations for ground-state calculations in near term quantum computers
We introduce a quantum Monte Carlo inspired reweighting scheme to accurately compute energies from optimally short quantum circuits.
Quantum Physics Statistical Mechanics Computational Physics
Variational Quantum Simulation of Ultrastrong Light-Matter Coupling
We propose the simulation of quantum-optical systems in the ultrastrong-coupling regime using a variational quantum algorithm.
Quantum Physics
Improving Variational Quantum Optimization using CVaR
The expectation is estimated as the sample mean of a set of measurement outcomes, while the parameters of the trial state are optimized classically.
Quantum Physics
