A5050886306- Quantum Computing Algorithms and Architecture
- Cold Atom Physics and Bose-Einstein Condensates
- Quantum Information and Cryptography
- Machine Learning in Materials Science
- Quantum many-body systems
- Quantum Mechanics and Applications
- Quantum and electron transport phenomena
- CRISPR and Genetic Engineering
- Theoretical and Computational Physics
- Quantum-Dot Cellular Automata
- Gyrotron and Vacuum Electronics Research
- Molecular Communication and Nanonetworks
- Parallel Computing and Optimization Techniques
- Opinion Dynamics and Social Influence
- Computational Drug Discovery Methods
- PARP inhibition in cancer therapy
- Molecular Junctions and Nanostructures
- DNA Repair Mechanisms
- Advanced Data Storage Technologies
- Diet, Metabolism, and Disease
- Mass Spectrometry Techniques and Applications
Pennsylvania Academy of Science
2024-2025
Université Paris-Saclay
2023-2024
Institut d’Optique Graduate School
2023
Laboratoire Charles Fabry
2023
Centre National de la Recherche Scientifique
2023
Vinci (France)
2023
Laboratoire de Physique des Plasmas
2022
Dana-Farber Cancer Institute
2016
Harvard University
2016
High-grade serous ovarian carcinomas (HGSOCs) with BRCA1/2 mutations exhibit improved outcome and sensitivity to double-strand DNA break (DSB)-inducing agents (i.e., platinum poly(ADP-ribose) polymerase inhibitors [PARPis]) due an underlying defect in homologous recombination (HR). However, resistance PARPis represents a significant barrier the long-term survival of these patients. Although BRCA1/2-reversion are clinically validated mechanism, they account for less than half...
Programmable arrays of hundreds Rydberg atoms have recently enabled the exploration remarkable phenomena in many-body quantum physics. In addition, development high-fidelity gates are making them promising architectures for implementation circuits.We present here<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>P</mml:mi><mml:mi>u</mml:mi><mml:mi>l</mml:mi><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi></mml:math>, an open-source Python library programming neutral-atom...
We implement and characterize a protocol that enables arbitrary local controls in dipolar atom array, where the degree of freedom is encoded pair Rydberg states. Our approach relies on combination addressing beams global microwave fields. Using this method, we directly prepare two different types three-atom entangled states, including $W$ state exhibiting finite chirality. verify nature underlying entanglement by performing quantum tomography. Finally, leveraging our ability to measure...
Using a Rydberg quantum processor with up to 32 qubits, the authors implement machine learning tasks on data structured into graphs and show that their platform can distinguish two different graph connectivities. To illustrate potential of such method, they it classify toxicity given molecule based real-world biochemistry dataset.
This study explores the implementation of Quantum Approximate Optimisation Algorithm (QAOA) in its analog form using a neutral atom quantum processing unit to solve Maximum Independent Set problem. The QAOA leverages natural encoding problem Hamiltonians by Rydberg interactions, while employing Bayesian navigate quantum-classical parameter space effectively under constraints hardware noise and resource limitations. We evaluate approach through combination simulations experimental runs on...
The Maximum Independent Set (MIS) problem is a fundamental combinatorial optimization task that can be naturally mapped onto the Ising Hamiltonian of neutral atom quantum processors. Given its connection to NP-hard problems and real-world applications, there has been significant experimental interest in exploring advantage for MIS. Pioneering experiments on King's Lattice graphs suggested quadratic speed-up over simulated annealing, but recent benchmarks using state-of-the-art methods found...
Machine learning models capable of handling the large data sets collected in financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques that, combined with classical algorithms, may deliver competitive, faster, and more interpretable models. In this paper we propose a quantum-enhanced machine solution for prediction credit rating downgrades, also known as fallen-angels forecasting risk management field. We implement on...
Machine Learning models capable of handling the large datasets collected in financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for prediction credit rating downgrades, also known as fallen-angels forecasting risk management field. We...
Spontaneous symmetry breaking underlies much of our classification phases matter and their associated transitions. The nature the underlying being broken determines many qualitative properties phase; this is illustrated by case discrete versus continuous breaking. Indeed, in contrast to case, a leads emergence gapless Goldstone modes controlling, for instance, thermodynamic stability ordered phase. Here, we realize two-dimensional dipolar XY model -- which exhibits spin-rotational utilizing...
We implement and characterize a protocol that enables arbitrary local controls in dipolar atom array, where the degree of freedom is encoded pair Rydberg states. Our approach relies on combination addressing beams global microwave fields. Using this method, we directly prepare two different types three-atom entangled states, including W-state state exhibiting finite chirality. verify nature underlying entanglement by performing quantum tomography. Finally, leveraging our ability to measure...
Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for running quantum algorithms. One unique advantage of this lies in the ability to reconfigure geometry qubit register, from shot shot. This feature makes possible native embedding graph-structured problems at hardware level, with profound consequences resolution complex optimization machine learning tasks. By driving qubits, one can generate...
In the quantum optimisation paradigm, variational algorithms face challenges with hardware-specific and instance-dependent parameter tuning, which can lead to computational inefficiencies. However, promising potential of transferability across problem instances similar local structures has been demonstrated in context Quantum Approximate Optimisation Algorithm. this paper, we build on these advancements by extending concept annealing-based protocols, employing Bayesian design robust...
Using a quantum processor to embed and process classical data enables the generation of correlations between variables that are inefficient represent through computation. A fundamental question is whether these could be harnessed enhance learning performances on real datasets. Here, we report use neutral atom comprising up $32$ qubits implement machine tasks graph-structured data. To end, introduce feature map encode information about graphs in parameters tunable Hamiltonian acting an array...
The manipulation of neutral atoms by light is at the heart countless scientific discoveries in field quantum physics last three decades. level control that has been achieved single particle within arrays optical traps, while preserving fundamental properties matter (coherence, entanglement, superposition), makes these technologies prime candidates to implement disruptive computation paradigms. current processing units (QPU) are nothing close universal computers but can already target...