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Advancing quantum and AI algorithms for practical applications

Romina Yalovetzky is an applied researcher with extensive experience developing hybrid quantum-classical and AI algorithms, focused on translating advances in quantum computing and AI into practical, real-world solutions for industry.

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Years of experience in technology and applied research.
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U.S. patents in quantum & AI technologies

Speaking & Impact
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Romina Yalovetzky

Quantum computing researcher

AI & optimization

Industry applications

Published in leading journals including

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About me

I work at the intersection of quantum computing and AI, with a focus on building algorithms that move beyond theory into real-world impact. My work centers on hybrid quantum-classical approaches and practical optimization problems, aiming to make near-term quantum technologies useful in industry. I am currently working as a Vice President, Applied Research Lead at the Global Technology Applied Research team at JPMorgan Chase, based in New York City.
I focus on translating advances in quantum computing and AI into practical, real-world applications at the intersection of research and industry. Originally trained as a physicist at the University of Buenos Aires, I am currently pursuing graduate studies in computer science at NYU Courant, combining a strong theoretical foundation with modern approaches in AI and machine learning.
Google Scholar
Speaking & Impact

Focus areas

My work sits at the intersection of quantum computing, optimization, and machine learning, with an emphasis on developing practical algorithms for real-world applications

Quantum Algorithms for Finance

Applying quantum methods to financial modeling, risk analysis, and optimization. One example of this is our work “Solving linear systems on quantum hardware with hybrid HHL++”, in which we solve a portfolio optimization problem by solving a linear system of equations on quantum hardware.

Read paper

Quantum Optimization with Applications in AI/ML

Developing quantum approaches for optimization in machine learning and decision-making. One example of this is our work “Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer”, in which we solve the problem of extractive summarization by framing it as an optimization problem that is solved with quantum optimization algorithms that preserve the desired number of sentences in summary.

Read paper

Hybrid Quantum-Classical Algorithms

Designing algorithms for near-term quantum systems by combining classical and quantum techniques. An example of this is our recently developed qReduMIS algorithm that leverages the power of quantum optimization to inform classical reduction and solve the Maximum Independent Set (MIS), which is a problem with applications spanning finance, routing, and communication networks.

Read paper

Quantum Algorithms for Finance

Applying quantum methods to financial modeling, risk analysis, and optimization. One example of this is our work «Solving linear systems on quantum hardware with hybrid HHL++», in which we solve a portfolio optimization problem by solving a linear system of equations on quantum hardware.

Read paper

Quantum Optimization with Applications in AI/ML

Developing quantum approaches for optimization in machine learning and decision-making. One example of this is our work «Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer», in which we solve the problem of extractive summarization by framing it as an optimization problem that is solved with quantum optimization algorithms that preserve the desired number of sentences in summary.

Read paper

Hybrid Quantum-Classical Algorithms

Designing algorithms for near-term quantum systems by combining classical and quantum techniques. An example of this is our recently developed qReduMIS algorithm that leverages the power of quantum optimization to inform classical reduction and solve the Maximum Independent Set (MIS), which is a problem with applications spanning finance, routing, and communication networks.

Read paper

Conferences

Talks & Events

A selection of talks, panels, and invited events where I share work on quantum computing, machine learning, and the transition from research to real-world impact.
Speaking & Impact
AWS re:Invent 2025 -JPMorganChase & Amazon Advanced Solutions Lab.

Scholarly papers

Research work

A selection of research papers reflecting my work on quantum computing, optimization, and machine learning, with a focus on developing practical and scalable methods.
  • 2025

A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values

Tyler Chen*, Akshay Seshadri*, Mattia J. Villani*, Pradeep Niroula, Shouvanik Chakrabarti, Archan Ray, Pranav Deshpande, Romina Yalovetzky, Marco Pistoia, and Niraj Kumar.

  • * Contributed equally
arXiv
NeurIPS
  • 2025

qReduMIS: A Quantum-Informed Reduction Algorithm for the Maximum Independent Set Problem

Martin J.A. Schuetz*, Romina Yalovetzky*, Ruben S. Andrist, Grant Salton, Yue Sun, Rudy Raymond, Shouvanik Chakrabarti, Atithi Acharya, Ruslan Shaydulin, Marco Pistoia, Helmut G. Katzgraber.

  • * Contributed equally
arXiv
  • 2024

Quantum Compilation Toolkit for Rydberg Atom Arrays with Implications for Problem Hardness and Quantum Speedups

Martin J.A. Schuetz, Ruben S. Andrist, Grant Salton, Romina Yalovetzky, Rudy Raymond, Yue Sun, Atithi Acharya, Shouvanik Chakrabarti, Marco Pistoia, and Helmut G. Katzgraber.

  • * Contributed equally
arXiv
APS
  • 2024

Characterizing Barren Plateaus in Quantum Ansätze with the Adjoint Representation

Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti, Niraj Kumar, Romina Yalovetzky, Jamie Heredge, Shree Hari Sureshbabu, Marco Pistoia.

  • Talk at 2024 QIP
Nature
  • 2024

Invited: Challenges and Opportunities of Quantum Optimization in Finance

Zichang He, Shouvanik Chakrabarti, Dylan Herman, Niraj Kumar, Changhao Li, Pierre Minssen, Pradeep Niroula, Ruslan Shaydulin, Yue Sun, Shree Hari Sureshbabu, Romina Yalovetzky, and Marco Pistoia.

  • * Contributed equally
ACM
  • 2024

Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing

Atithi Acharya, Romina Yalovetzky, Pierre Minssen, Shouvanik Chakrabarti, Ruslan Shaydulin, Rudy Raymond, Yue Sun, Dylan Herman, Ruben S. Andrist, Grant Salton, Martin J.A. Schuetz, Helmut G. Katzgraber, and Marco Pistoia.

  • * Contributed equally
arXiv
APS
  • 2024

QC-Forest: A Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest

Romina Yalovetzky, Niraj Kumar, Changhao Li, Marco Pistoia.

  • * Contributed equally
arXiv
  • 2024

Prospects of Privacy Advantage in Quantum Machine Learning

Jamie Heredge, Niraj Kumar, Dylan Herman, Shouvanik Chakrabarti, Romina Yalovetzky, Shree Hari Sureshbabu, Marco Pistoia.

  • * Contributed equally
arXiv
Nature
  • 2023

Des-q: A Quantum Algorithm to Construct and Efficiently Retrain Decision Trees for Regression and Binary Classification

Niraj Kumar*, Romina Yalovetzky*, Changhao Li, Pierre Minssen, Marco Pistoia.

  • Talk at the 2024 APS March Meeting, Minneapolis, MN.
  • Poster at 2023 QTML, Geneva, Switzerland.
  • * Contributed equally
arXiv
Quantum
  • 2023

Characterizing Barren Plateaus in Quantum Ansätze with the Adjoint Representation

Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti, Niraj Kumar, Romina Yalovetzky, Jamie Heredge, Shree Hari Sureshbabu, Marco Pistoia.

  • Talk at 2024 QIP
Nature
  • 2023

Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem

Ruslan Shaydulin, Changhao Li, Shouvanik Chakrabarti, Matthew DeCross, Dylan Herman, Niraj Kumar, Jeffrey Larson, Danylo Lykov, Pierre Minssen, Yue Sun, Yuri Alexeev, Joan M. Dreiling, John P. Gaebler, Thomas M. Gatterman, Justin A. Gerber, Kevin Gilmore, Dan Gresh, Nathan Hewitt, Chandler V. Horst, Shaohan Hu, Jacob Johansen, Mitchell Matheny, Tanner Mengle, Michael Mills, Steven A. Moses, Brian Neyenhuis, Peter Siegfried, Romina Yalovetzky, Marco Pistoia.

  • Oral presentation at 2023 IEEE Quantum Week, Bellevue, USA.
Science.org
  • 2023

Hardness of the Maximum Independent Set Problem on Unit-Disk Graphs and Prospects for Quantum Speedups

Ruben S. Andrist*, Martin J.A. Schuetz*, Pierre Minssen*, Romina Yalovetzky*, Shouvanik Chakrabarti, Dylan Herman, Niraj Kumar, Grant Salton, Ruslan Shaydulin, Yue Sun, Marco Pistoia, Helmut G. Katzgraber.

  • Oral presentation at 2023 AWS & QuEra Re:Invent, Las Vegas
  • * Contributed equally
APS
  • 2023

Quantum Deep Hedging

El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, Marco Pistoia.

  • * Contributed equally
Quantum
Quantum views
  • 2022

Constrained Quantum Optimization for Extractive Summarization on a Trapped-Ion Quantum Computer

Pradeep Niroula*, Ruslan Shaydulin*, Romina Yalovetzky*, Pierre Minssen, Dylan Herman, Shaohan Hu, Marco Pistoia.

  • Talks at 2023 QIP (Ghent), APS (Las Vegas), IEEE Quantum Week (Phoenix), Quantum Latino.
  • * Contributed equally
Nature
Top 100
  • 2022

Universal Quantum Speedup for Branch-and-Bound, Branch-and-Cut, and Tree-Search Algorithms

Shouvanik Chakrabarti, Pierre Minssen, Romina Yalovetzky, Marco Pistoia.

  • Talks at 2023 APS March Meeting and INFORMS, Phoenix.
  • * Contributed equally
arXiv
  • 2021

Solving Linear Systems on Quantum Hardware with Hybrid HHL++

Romina Yalovetzky, Pierre Minssen, Dylan Herman, Marco Pistoia.

  • Talks at 2022 APS, 2022 QIP, Kenan Institute Seminar, and 2023 INFORMS.
  • * Contributed equally
Nature
  • 2021

Universal Quantum Speedup for Branch-and-Bound, Branch-and-Cut, and Tree-Search Algorithms

Shouvanik Chakrabarti, Pierre Minssen, Romina Yalovetzky, Marco Pistoia.

  • Talks at 2023 APS March Meeting and INFORMS, Phoenix.
  • * Contributed equally
arXiv
  • 2022

Quantum Machine Learning for Finance – ICCAD Special Session Paper

Marco Pistoia, Syed Farhan Ahmad, Akshay Ajagekar, Alexander Buts, Shouvanik Chakrabarti, Dylan Herman, Shaohan Hu, Andrew Jena, Pierre Minssen, Pradeep Niroula, Arthur Rattew, Yue Sun, Romina Yalovetzky.

  • * Contributed equally
arXiv
ACM

Patents filed

Inventions

Patents and technical contributions reflecting my work on quantum computing and related technologies, with a focus on translating research into practical innovation.
  • 2022
  • Granted

Systems and Methods for Enhanced Eigenvalue Inversion Using Quantum Conditional Logic

R. Yalovetzky, D. Herman, P. Minssen, M. Pistoia, B. Alexander, S. Hu.

  • US Patent No. 12,259,942 B2
  • 2022
  • Granted

Systems and Methods for Optimization of Time Evolution for Quantum Computer-Based Eigenvalue Estimation

P. Minssen, R. Yalovetzky, D. Herman, M. Pistoia, B. Alexander, S. Hu.

  • US Patent No. 12,242,562 B2
  • 2025
  • Filed

Systems and Methods for Decision Tree Construction and Update Using Quantum Algorithms

Romina Yalovetzky, N. Kumar, C. Li, M. Pistoia.

  • US Patent App. 18/448,662
  • 2025
  • Filed

Systems and Methods for Hybrid Classical-Quantum Optimization Using Random Matrix Theory-Based Subproblem Identification on Correlation Matrices

A. Acharya, R. Shaydulin, S. Chakrabarti, D. Herman, P. Minssen, Y. Sun, R. Yalovetzky, M. Pistoia.

  • US Patent App. 18/462,187
  • 2023
  • Filed

Systems and Methods for Quantum Computing-Assisted Portfolio Selection

A. Ajagekar, P. Minssen, R. Yalovetzky, M. Pistoia.

  • US Patent App. 17/653,258
  • 2023
  • Filed

Systems and Methods for Implementing Quantum Branch-and-Bound Algorithms with Heuristics

S. Chakrabarti, P. Minssen, R. Yalovetzky, M. Pistoia.

  • US Patent App. 17/648,054
  • 2023
  • Filed

Systems and Methods for Quantum Computing-Based Extractive Summarization

P. Niroula, R. Yalovetzky, P. Minssen, D. Herman, M. Pistoia, S. Hu.

  • US Patent App. 17/647,311

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Career Path

Work experience

Experience across research and industry, working on quantum computing and machine learning with a focus on practical applications and impact
  • Feb 2024 - current

Vice President Lead Applied Researcher on Quatum Computing

JPMorgan Chase 

  • • Global Technology Applied Research
  • New York, USA
  • Feb 2022 - Feb 2024

Senior Associate Applied Researcher on Quantum Computing

JPMorgan Chase

  • • Global Technology Applied Research
  • New York, USA
  • Apr 2021 - Feb 2022

Associate Applied Researcher on Quantum Computing

JPMorgan Chase 

  • • Global Technology Applied Research
  • New York, USA
  • Feb 2021 - Apr 2021

Associate Software Developer

JPMorgan Chase 

  • Buenos Aires, Argentina
  • Sep 2018 - Feb 2021

Analyst Software Developer

Global Technology Applied Research
  • Buenos Aires, Argentina
  • Mar 2018 - Mar 2019

Undergraduate Teaching Assistant

University of Buenos Aires (UBA)
  • • School of Science
  • Course: Physics Lab
  • Sep 2017 - Aug 2018

Resercher intern

FATE SAICI
  • • R&D Department
Research project studying the mechanical response of the tyre to an impact made by an instrumented hammer at the lab. Studied the tyre in isolation and inside the vehicle. Work presented in a poster at the 2018 annual meeting of the Argentinian Physical Society (Asociación Argentina de Física).
Report
  • Buenos Aires, Argentina
  • Jun 2017 - Aug 2017

Summer Student

CERN

  • • CMS Experiment
Research project under the supervision of Felice Pantaleo. The work is “Tuning of the Cellular Automaton Parameters for the Phase 1 Track Seeding at the CMS Experiment”. Oral presentation at the 2017 annual meeting of the Argentinian Physical Society.
Report
  • Geneve, Switzerland
  • Mar 2017 - Mar 2018

Scientific Promoter

University of Buenos Aires (UBA)

  • • School of Science
  • Feb 2024 - current

Vice President Lead Applied Researcher on Quatum Computing

JPMorgan Chase 

  • • Global Technology Applied Research
  • New York, USA
  • Feb 2022 - Feb 2024

Senior Associate Applied Researcher on Quantum Computing

JPMorgan Chase

  • • Global Technology Applied Research
  • New York, USA
  • Apr 2021 - Feb 2022

Associate Applied Researcher on Quantum Computing

JPMorgan Chase 

  • • Global Technology Applied Research
  • New York, USA
  • Feb 2021 - Apr 2021

Associate Software Developer

JPMorgan Chase 

  • Buenos Aires, Argentina
  • Sep 2018 - Feb 2021

Analyst Software Developer

Global Technology Applied Research
  • Buenos Aires, Argentina
  • Mar 2018 - Mar 2019

Undergraduate Teaching Assistant

University of Buenos Aires (UBA)
  • • School of Science
  • Course: Physics Lab
  • Sep 2017 - Aug 2018

Resercher intern

FATE SAICI
  • • R&D Department

Research project studying the mechanical response of the tyre to an impact made by an instrumented hammer at the lab. Studied the tyre in isolation and inside the vehicle. Work presented in a poster at the 2018 annual meeting of the Argentinian Physical Society (Asociación Argentina de Física).

Report
  • Buenos Aires, Argentina
  • Jun 2017 - Aug 2017

Summer Student

CERN

  • • CMS Experiment
Research project under the supervision of Felice Pantaleo. The work is “Tuning of the Cellular Automaton Parameters for the Phase 1 Track Seeding at the CMS Experiment”. Oral presentation at the 2017 annual meeting of the Argentinian Physical Society.
Report
  • Geneve, Switzerland
  • Mar 2017 - Mar 2018

Scientific Promoter

University of Buenos Aires (UBA)

  • • School of Science

Academic Background

Education

A history of academic excellence and foundational training in Physics and Engineering.
  • Sep 2024 - Dec 2026

Master in Computer Science

  • New York University
  • New York, USA.
Courant Computer Science.
  • Mar 2014 - Jun 2019

Bachelor and Master in Physics

  • University of Buenos Aires
  • Buenos Aires, Argentina

Graduated with honors, I obtained the honors diploma by the University of Buenos Aires for outstanding academic performance.
Grade: 8.9/10

  • Mar 2008 - Dec 2013

High School Diploma in Science and Engineering

  • Colegio Nacional de Buenos Aires
  • Buenos Aires, Argentina
Graduated with honors, I obtained the gold medal by the University of Buenos Aires for outstanding academic performance in Math.

Academic Background

Education

A history of academic excellence and foundational training in Physics and Engineering.
  • Sep 2024 - Dec 2026

Master in Computer Science

  • University of Buenos Aires, Argentina
  • Buenos Aires, Argentina
Graduated with honors. Complete ‘Licenciatura’ degree, which is equivalent to a combined Bachelor’s and Master’s program.

Honors Diploma

Awarded by the University of Buenos Aires for outstanding academic performance.

Final Grade

8.9

Bachelor and Master in Physics

  • University of Buenos Aires, Argentina
  • Buenos Aires, Argentina
Graduated with honors. Complete ‘Licenciatura’ degree, which is equivalent to a combined Bachelor’s and Master’s program.

Honors Diploma

Awarded by the University of Buenos Aires for outstanding academic performance.

Final Grade

8.9

High School Diploma in Science and Engineering

  • Colegio Nacional de Buenos Aires
  • Buenos Aires, Argentina

Pre-university high school dependent on the University of Buenos Aires.

Gold medal

Awarded by the University of Buenos Aires for outstanding academic performance.

Code and data released

Contributions to the open source community

Contributions to open source projects in quantum computing and machine learning, supporting reproducibility, accessibility, and collaboration in research and development.
  • CODE

qReduMIS: A Quantum-Informed Reduction Algorithm for the Maximum Independent Set Problem

Quantum-informed reduction algorithm for Maximum Independent Set optimization.

GitHub Repository
  • CODE

Quantum Compilation Toolkit for Rydberg Atom Arrays with Implications for Problem Hardness and Quantum Speedups

Quantum compilation tools for Rydberg atom array architectures.
GitHub Repository
  • CODE

Decomposition pipeline for large-scale portfolio optimization with applications to near-term quantum computing

Quantum compilation tools for Rydberg atom array architectures.
Access Repository
  • CODE
  • DATA

QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest

Hybrid classical-quantum approach to accelerate Random Forest retraining.
Access Repository
  • CODE
  • DATA

Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification

Efficient quantum construction and retraining of decision trees.
Access Repository
  • CODE

Hardness of the Maximum Independent Set Problem on Unit-Disk Graphs and Prospects for Quantum Speedups

Analyzing MIS problem hardness for potential quantum speedups.
GitHub Repository
  • DATA

Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer

Quantum optimization data for extractive text summarization tasks.
Access Repository

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