Search Results for author:
Found 14 papers, 5 papers with code
Guided Quantum Compression for Higgs Identification
To ameliorate this issue, we design an architecture that unifies the preprocessing and quantum classification algorithms into a single trainable model: the guided quantum compression model.
Symmetry breaking in geometric quantum machine learning in the presence of noise
This work studies the behavior of EQNN models in the presence of noise.
Approximately Equivariant Quantum Neural Network for $p4m$ Group Symmetries in Images
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which can be efficiently simulated with a low depth on near-term quantum hardware in the presence of noises.
Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems.
Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors
The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms.
Trainability barriers and opportunities in quantum generative modeling
In this work, we investigate the barriers to the trainability of quantum generative models posed by barren plateaus and exponential loss concentration.
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.
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequential unitary transformations, is intrinsically linear.
Automatic and effective discovery of quantum kernels
Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data.
Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection
Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art Quantum Machine Learning algorithms with respect to the Classical Approach.
Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning
Our framework can also be used as a library and integrated into pre-existing software, maximizing code reuse.
Running the Dual-PQC GAN on noisy simulators and real quantum hardware
In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN.
Conditional Born machine for Monte Carlo event generation
So called Born machines are purely quantum models and promise to generate probability distributions in a quantum way, inaccessible to classical computers.
