A5017741513- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Particle physics theoretical and experimental studies
- Quantum and electron transport phenomena
- Neural Networks and Reservoir Computing
- Computational Physics and Python Applications
- Quantum many-body systems
- Advancements in Semiconductor Devices and Circuit Design
- Particle Detector Development and Performance
- Parallel Computing and Optimization Techniques
- Distributed and Parallel Computing Systems
- Quantum Mechanics and Applications
- Atomic and Subatomic Physics Research
- High-Energy Particle Collisions Research
- Quantum Chromodynamics and Particle Interactions
- Physics of Superconductivity and Magnetism
- Machine Learning in Materials Science
- Low-power high-performance VLSI design
- Fractal and DNA sequence analysis
- Generative Adversarial Networks and Image Synthesis
- Neural Networks and Applications
- Electronic and Structural Properties of Oxides
- Scientific Computing and Data Management
- Advanced Data Storage Technologies
- Anomaly Detection Techniques and Applications
European Organization for Nuclear Research
2022-2025
RWTH Aachen University
2023-2024
Hamburg Institut (Germany)
2023
IBM (Germany)
2023
IBM (United States)
2023
FOM University of Applied Sciences for Economics and Management
2023
University of Pavia
2018-2021
Istituto Nazionale di Fisica Nucleare, Sezione di Pavia
2018-2021
Institute of Biomedical Technologies
2021
Institute for Physics
2018
Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with potential achieving so-called quantum advantage—namely, significant (in some cases exponential) speedup numerical simulations. The rapid development hardware devices various realizations qubits enables execution small-scale but representative applications on computers. In particular, high-energy physics community plays pivotal role accessing power computing, since field...
The ongoing quest to discover new phenomena at the LHC necessitates continuous development of algorithms and technologies. Established approaches like machine learning, along with emerging technologies such as quantum computing show promise in enhancement experimental capabilities. In this work, we propose a strategy for anomaly detection tasks based on unsupervised demonstrate its effectiveness identifying phenomena. designed models-an kernel two clustering algorithms-are trained detect...
The variational quantum eigensolver (VQE) is an algorithm to compute ground and excited state energy of many-body systems. A key component the active research area construction a parametrized trial wave function---a so-called ansatz. function parametrization should be expressive enough, i.e., represent true eigenstate system for some choice parameter values. On other hand, it trainable, number parameters not grow exponentially with size system. Here, we apply VQE problem finding energies...
We characterize for the first time performances of IBM quantum chips as batteries, specifically addressing single-qubit Armonk processor. By exploiting Pulse access enabled to some Quantum processors via Qiskit package, we investigate advantages and limitations different profiles classical drives used charge these miniaturized establishing optimal compromise between charging stored energy. Moreover, consider role played by various possible initial conditions on functioning batteries. As main...
This paper presents a first end-to-end application of Quantum Support Vector Machine (QSVM) algorithm for classification problem in the financial payment industry using IBM Safer Payments and Computers via Qiskit software stack. Based on real card data, thorough comparison is performed to assess complementary impact brought by current state-of-the-art Learning algorithms with respect Classical Approach. A new method search best features explored Machine's feature map characteristics. The...
Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g., phase diagram. However, access to training labels is major bottleneck for any supervised approach, preventing getting insights new physics. In this Letter, using convolutional neural networks, we overcome limit by determining the diagram of model where analytical solutions are lacking, only on marginal points diagram, integrable models represented. More specifically, consider axial...
Abstract Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like ones performed Large Hadron Collider (LHC). However, current experiments do not indicate clear signs of that could guide development additional Beyond Standard Model (BSM) theories. Identifying signatures out enormous amount data produced LHC falls into class anomaly detection and constitutes one greatest computational challenges. In this...
Abstract Quantum generative models provide inherently efficient sampling strategies and thus show promise for achieving an advantage using quantum hardware. In this work, we investigate the barriers to trainability of posed by barren plateaus exponential loss concentration. We explore interplay between explicit implicit losses, that with losses such as KL divergence leads a new flavor plateaus. contrast, Maximum Mean Discrepancy can be viewed expectation value observable is either low-bodied...
Motivated by recent experimental observations carried out in superconducting transmon circuits, we compare two different charging protocols for three-level quantum batteries based on time-dependent classical pulses. In the first case, complete is achieved through application of sequential pulses, while second occurs a unique step applying pulses simultaneously. The latter approach characterized shorter time, and consequently greater power. Moreover, both are analytically solvable, leading to...
Geometric quantum machine learning based on equivariant neural networks (EQNNs) recently appeared as a promising direction in learning. Despite encouraging progress, studies are still limited to theory, and the role of hardware noise EQNN training has never been explored. This work behavior models presence noise. We show that certain can preserve equivariance under Pauli channels, while this is not possible amplitude damping channel. claim symmetry breaks linearly number layers strength....
Hamiltonian moments in Fourier space—expectation values of the unitary evolution operator under a at different times—provide convenient framework to understand quantum systems. They offer insights into energy distribution, higher-order dynamics, response functions, correlation information, and physical properties. This paper focuses on computation within context nuclear effective field theory superconducting hardware. The study integrates echo verification noise renormalization Hadamard...
Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with potential achieving so-called quantum advantage, namely significant (in some cases exponential) speed-up numerical simulations. The rapid development hardware devices various realizations qubits enables execution small scale but representative applications on computers. In particular, high-energy physics community plays pivotal role accessing power computing, since field...
Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic noise. Overcoming the effects noise is a major challenge, for which different error mitigation and correction techniques have been proposed. In this paper, we conduct first study performance Generative Adversarial Networks (qGANs) in presence types noise, focusing on simplified use case high-energy physics. particular, explore readout two-qubit gate errors qGAN training process. Simulating device...
Abstract Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann machines (QBM) was shown to significantly improve the efficiency compared classical Q-learning restriction, however, discrete state-action space environments. In this paper, FERL approach is extended multi-dimensional continuous environments open doors for a broader range of real-world applications. First, free studied action spaces, but state spaces and impact experience replay on sample assessed. second...
Abstract State preparation plays a pivotal role in numerous quantum algorithms, including phase estimation. This paper extends and benchmarks counterdiabatic driving protocols across three one-dimensional spin systems characterized by transitions: the axial next-nearest neighbor Ising, XXZ, Haldane–Shastry models. We perform shallow optimal control over optimizing an energy cost function. Moreover, we provide code package for computing symbolically various adiabatic gauge potentials....
The emergence of a collective behavior in many-body system is responsible for the quantum criticality separating different phases matter. Interacting spin systems magnetic field offer tantalizing opportunity to test approaches study phase transitions. In this work, we exploit new resources offered by algorithms detect critical fully connected spin-1/2 models. We define suitable Hamiltonian depending on an internal anisotropy parameter γ that allows us examine three paradigmatic examples...
Vector-boson scattering processes are of great importance for the current run-II and future runs Large Hadron Collider. The presence triple quartic gauge couplings in process gives access to sector Standard Model (SM) possible new-physics contributions there. To test any hypothesis, sound knowledge SM is necessary, with a precision which at least matches experimental uncertainties existing forthcoming measurements. In this article we present detailed study vector-boson two positively-charged...
Measuring longitudinally polarised vector boson scattering in WW channel is a promising way to investigate unitarity restoration with the Higgs mechanism and search for possible physics beyond Standard Model. In order perform such measurement, it crucial develop an efficient reconstruction of full W kinematics leptonic decays focus on polarisation measurements. We investigated several approaches, from traditional ones up advanced deep neural network structures, we compared their ability...
We present a novel method for simulating the noisy behavior of quantum computers, which allows to efficiently incorporate environmental effects in driven evolution implementing gates acting on qubits. show how modify noiseless gate executed by computer include any Markovian noise, hence resulting what we will call gate. compare our with IBM qiskit simulator, and that it follows more closely both analytical solution Lindblad equation as well real computer, where ran algorithms involving up 18...
Simulating many-body quantum systems is a promising task for computers. However, the depth of most algorithms, such as product formulas, scales with number terms in Hamiltonian, and can therefore be challenging to implement on near-term, well early fault-tolerant devices. An efficient solution given by stochastic compilation protocol known qDrift, which builds random formulas sampling from Hamiltonian according coefficients. In this work, we unify qDrift importance sampling, allowing us...
Abstract Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1–3). Existing statistical computational methods for EI detection mostly limited pairs of SNPs due combinatorial explosion higher-order EIs. With NeEDL (network-based epistasis via local search), we leverage network medicine inform selection EIs that an order...
Abstract We present the first quantum computation of a total decay rate in high-energy physics at second order in
perturbative field theory. This work underscores confluence two recent cutting-edge advances.
On one hand, integration algorithm Quantum Fourier Iterative Amplitude Estimation (QFIAE),
which efficiently decomposes target function into its series through neural network before quantumly integrating corresponding components. On other causal unitary...