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15 Top ML Specialists in The Quantum Computing Industry

Quantum Machine Learning

As in other articles in this series, Quantum Machine Learning specialists are usually individuals who possess — at the very least — an MSc in a STEM discipline, though a candidate with a Ph.D. is much more sought after. The perfect candidates should be familiar with ML, in particular with probabilistic graphical models and artificial neural networks. Additionally, knowledge of quantum computation is another skill that employers are looking for in a future employee, as well as a solid comprehension of today’s scientific programming language.

The Quantum Insider will now highlight fifteen of these talented individuals. People who, in their own unique way, are creating our industry’s future.

*All data correct as of mid-January 2022

1. Bruce Krayenhoff: Machine Learning Scientist, 1QBit


First on our list is Bruce Krayenhoff, an ML Scientist at 1QBit, a Canadian quantum startup applying breakthroughs in computation to ML and optimization science through a widely accessible, hardware-agnostic software platform.

An exceptional mathematician working on reinforcement learning and other ML solutions, Krayenhoff’s passions are understanding ML deeply and developing clear, well-designed, tested and documented code. Growing up on Vancouver Island solving math problems in the car, his deep interest in math led him to excel in his pursuit of a Master’s degree in Computational Biophysics from the University of British Columbia. During this time, he started tutoring other students, a vocation he enjoys immensely and continues in his spare time.

In 2012, Bruce discovered ML and enrolled in a Master’s degree at Simon Fraser University researching a novel semi-supervised learning approach. He continued to develop his skills in deep learning through meetups, self-study and Kaggle competitions and looks forward to bringing his passion for understanding how things work to creating innovative solutions in reinforcement learning at 1QBit.

2. Daniel Varoli: Machine Learning Engineer, Zapata Computing


A Machine Learning Engineer at Boston-based Zapata Computing, a spinoff from Harvard that develops quantum software and algorithms for business, Daniel Varoli’s journey began at the age of 16 when he realized physics was much more than memorizing long equations. He went on to study Physics and Mathematics for his undergraduate degree. During that time he also worked as a research assistant in a Biophysics lab.

Varoli then pursued an M.Sc. in Biomechanical Engineering, and as part of his thesis built a program to assist in the detection and classification of immune cells. He was partway into his Ph.D. in Biomedical Engineering, when he realized this was not his passion and made a transition into AI. He has since worked at a distributor-focused startup as a data scientist, before joining Zapata. He is a huge fan of generative models, especially anything that involves surreal artworks.

3. Jack Maguire: Senior Machine Learning Scientist, Menten AI


Jack Maguire is a Senior Machine Learning Scientist at Menten AI, a Palo Alto-based drug discovery startup focused on designing peptides for conditions with high unmet medical needs by developing computational methods for protein design that leverage ML and quantum computing.

Maguire has a Ph.D. in Bioinformatics and Computational Biology from the University of North Carolina at Chapel Hill.

4. Tianyu Lu: Junior Machine Learning Scientist, ProteinQure


Tianyu Lu is a Junior Machine Learning Scientist at ProteinQure, a quantum startup based in Toronto whose proprietary computational platform is for protein drug discovery.

Having studied Bioinformatics, Computational Biology and Computer Science at the University of Toronto, Lu worked with Alan Moses on models of gene regulatory networks using machine learning models for differential equations. The project later pivoted to using random embeddings as baselines for protein sequence embeddings. Previously, he worked with Philip Kim on ML-guided protein design for cancer therapeutics, while also leading the University of Toronto’s iGEM team as Co-President, where he worked on optimizing PETase, a plastic-degrading enzyme.

Broadly interested in analogies between biology and computation, with a focus on algorithms for protein design such as the intersection of directed evolution and model-based optimization, Lu’s research goal is to model and sample from the p(sequence, structure, function) distribution.

5. Freek de Haas: Machine Learning Researcher, 1QBit


Freek de Haas is a Machine Learning Researcher at 1QBit. An experienced Operations Researcher with a demonstrated history of working in the information technology and services industry, he is skilled in computer science, optimization, ML, Python, and data analysis.

De Haas obtained a Ph.D. in Mathematical Biology from the University of British Columbia, Vancouver. His research focused on the genetic manipulation of populations using CRISPR-based gene drives.

6. Enrique Cervero Martín: Quantum Machine Learning Scientist, Cambridge Quantum Computing (now Quantinuum)


A mathematics graduate with extensive knowledge in analysis, mathematical physics and quantum information theory in particular, and interest in algorithms, complexity theory, cryptography and ML, Enrique Cervero Martín is a Quantum Machine Learning Scientist at Cambridge Quantum Computing, now Quantinuum after a merger with Honeywell Quantum Solutions.

Cervero Martín received an MSc in Mathematics with Quantum Information from the University of Copenhagen.

7. Usman Ayub Sheikh, Machine Learning Engineer, Multiverse Computing


Usman Ayub Sheikh is a Machine Learning Engineer at Multiverse Computing, a Bilboa, Spain-based quantum startup. An electrical engineer with a specialization in Robotics and AI, for his Ph.D. from the University of Basque Country (UPV/EHU) he researched Cognitive Neuroscience at the Basque Center on cognition, Brain and Language (BCBL).

8. Jaspreet Oberoi: Director of Machine Learning and Quantum Inspired Solutions, 1QBit


Jaspreet Oberoi is Director of Machine Learning and Quantum Inspired Solutions at 1QBit, where he directs the ML and the quantum-inspired optimization division at the company. He is usually busy connecting business with technology, client problems with 1QBit brains and the company strategy with on-the-ground execution.

He has more than six years of experience in identifying and formulating data-driven quantum and classical solutions for businesses in finance, retail, healthcare and more.

Oberoi obtained a Ph.D. in Engineering Science from Simon Fraser University

9. Alexander Kitaygorodsky: Machine Learning Scientist, ProteinQure


A Machine Learning Scientist at Canadian startup ProteinQure, Alexander Kitaygorodsky completed his Ph.D. in Biomedical Informatics at Columbia University, developing novel neural network architectures for genetic variant analysis.

He has worked on problems ranging from drug synergy prediction to modelling of noncoding selection impacts. What most excites Kitaygorodsky is building deep learning applications to help forward medicine.

10. Vsevolod Radchenko: Machine Learning Researcher, Image Processing, 1QBit


Vsevolod Radchenko is another Machine Learning Researcher of Image Processing at 1QBit. After receiving a BSc from South Ural State University, he studied at the Lappeenranta University of Technology in Finland and received an MSc in Computational Engineering and Physics.

At 1QBit, he is a part of the machine learning team, where he works on medical image analysis.

11. Pablo Martin: Machine Learning Engineer, Multiverse Computing


A Ph.D. candidate in Theoretical Physics at Instituto de Física Teórica UAM-CSIC, where his research is focused on developing ML techniques to find patterns in data in particle physics experiments, Pablo Martin is a Machine Learning Engineer at Multiverse Computing.

Passionate in dealing with complex data and applying data insights to solve problems, as a researcher Martin has developed great autonomy, collaboration and communication skills.

12. Ashlin Kanawaty: Junior Machine Learning Scientist, ProteinQure


Ashlin Kanawaty is a Junior Machine Learning Scientist at ProteinQure. Kanawaty received an M.Sc. in Neuropharmacology from the University of Toronto and a B. A. Sc. from McMaster University. She has a diversity of experience in wet lab research, medical robotics and ML.

13. Thomas Merkh, Senior Machine Learning Engineer, Q-CTRL


Thomas Merkh is a Senior Machine Learning Engineer at quantum startup Q-CTRL and is based in Los Angeles. With interests in operationalizing state-of-the-art ML models for real application use, building scalable cloud-based machine learning pipelines, designing data-intensive web-based applications, and high-performance computing, for his undergraduate, Merkh attended Rensselaer Polytechnic Institute in Troy, NY. During his time there, he focused his efforts on computational physics and applied mathematics. Afterward, he moved to Los Angeles to pursue graduate school in applied mathematics at UCLA.

Merkh’s research at UCLA dealt with the applied and theoretical properties of neural networks. Now in his role at Q-CTRL, his current goals are to operationalize ML models for quantum applications and automate machine learning deployment cycles.

14. Steffen Cruz: Machine Learning Lead, Solid State AI


Steffen Cruz is a Machine Learning Lead at Solid State AI, a Toronto-based startup whose hybrid quantum-classical ML algorithms are particularly effective at predicting on sparse data sets — those with limited data, or those with lots of noise and only a few relevant signals.

With a Ph.D. in Experimental Sub-atomic Physics from The University of British Columbia, Cruz’s skills lie in software and ML development, data science, and automation.

15. Brian Dellabetta: Senior ML-DevOps Engineer, Zapata Computing


Brian Dellabetta is a Senior ML-DevOps Engineer at Zapata Computing. Dellabetta started his career as an Electrical Engineer, writing low-level code for database management system software.

As a graduate student in Computational Physics, he simulated the electronic behaviour of novel solid-state devices. He has worked at an education technology start-up as a full-stack software engineer and data scientist and in information security at a large financial firm as a data engineer and data scientist.

With a Ph.D. in Electrical and Electronics Engineering from The Grainger College of Engineering, University of Illinois Urbana-Champaign, Dellabetta is also an Adjunct Professor at DePaul University in Chicago.

The Quantum Insider (TQI)


Just in case this list hasn’t satisfied your cravings for data on hardware specialists in the quantum space, you can pop on over to The Quantum Insider (TQI), TQD’s very own data platform. Here you can find deep and insightful information on all aspects of the QIS industry.

TQI is an invaluable resource for journalists, researchers, investors, companies, and government agencies looking to extend their knowledge of the growing quantum tech ecosystem!

Deep Tech Insider (DTI)


Interested in other niches of deep tech? Good, TQD team has that covered, too: Check out the Deep Tech Insider, the best place for news on all that is happening in the world of deep tech.

Metaverse Insider


Interested in the very latest in cutting-edge tech? Then maybe you should read the Metaverse Insider, making the Metaverse accessible through media, news, insights and data.

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When: July 13, 2023 at 11:30am

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James Dargan

James Dargan is a writer and researcher at The Quantum Insider. His focus is on the QC startup ecosystem and he writes articles on the space that have a tone accessible to the average reader.

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The Future of Materials Discovery: Reducing R&D Costs significantly with GenMat’s AI and Machine Learning Tools

When: July 13, 2023 at 11:30am

What: GenMat Webinar

Jake Vikoren

Jake Vikoren

Company Speaker

Deep Prasad

Deep Prasad

Company Speaker

Araceli Venegas

Araceli Venegas

Company Speaker

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