Hardware-Tailored Diagonalization Circuits
Daniel Miller, Igor O. Sokolov, et al. (2024). "Hardware-Tailored Diagonalization Circuits." npj Quantum Inf.
CV
Education
- Ph.D in Computational Science, IBM Research Zurich & University of Zurich, Switzerland, 2019-2022
- M.S. in Physics (Grade: 5.75/6), ETH Zurich, Switzerland, 2016-2018
- B.S. in Physics, EPF Lausanne, Switzerland, 2012-2016
Work Experience
- April 2022 - Present: Senior Quantum Algorithm Developer
- Pasqal, Amsterdam, Netherlands
- Quantum graph ML & Differentiable quantum chemistry
- Leading R&D projects with Saudi Aramco and Schrodinger
- Developed scalable quantum-classical hybrid graph models
- Patented integration of quantum neural networks in density functional theory
- December 2018 - March 2022: PhD Student
- IBM Research Zurich, Switzerland
- Quantum algorithms for chemistry @ Ivano Tavernelli’s lab
- Orbital optimization, molecular dynamics, transcorrelated methods
- July 2018 - November 2018: Student Assistant
- ETH Zurich, Switzerland
- Quantum algorithms for chemistry @ Manfred Sigrist’s lab
- May 2018 - July 2018: Intern
- IBM Research Zurich, Switzerland
- Quantum algorithms for chemistry @ Ivano Tavernelli’s lab
- July 2017 - August 2017: Intern
- ETH Zurich, Switzerland
- Experimental physics @ Jerome Faist’s lab
- June 2017 - July 2017: Visiting Scientist
- EMPA, Zurich, Switzerland
- Quantum chemistry @ Daniele Passerone’s lab
Skills
- Research Areas: Quantum computing, AI, Software development, Quantum chemistry & physics, Scientific research, Client projects
- Programming: Python, C++, LaTeX, Matlab, PyTorch, JAX, Git
- Quantum Tools: Qiskit, PySCF, Gaussian, PyQuante, Psi4, Openfermion, Cirq, Qutip
- Languages: Fluent in French, English, Russian, Ukrainian and B2-level German
Publications
Ab Initio Transcorrelated Method enabling accurate Quantum Chemistry on near-term Quantum Hardware
Igor O. Sokolov (shared first authorship), et al. (2024). "Ab Initio Transcorrelated Method enabling accurate Quantum Chemistry on near-term Quantum Hardware." JCTC
Quantum-enhanced Neural Exchange-Correlation Functionals
Igor O. Sokolov, et al. (2024). "Quantum-enhanced Neural Exchange-Correlation Functionals." arXiv:2404.14258
Enhancing Graph Neural Networks with Quantum Computed Encodings
Slimane Thabet, Igor O. Sokolov, et al. (2023). "Enhancing Graph Neural Networks with Quantum Computed Encodings." ICML 2024
Orders of magnitude reduction in the computational overhead for quantum many-body problems on quantum computers via an exact transcorrelated method
Igor O. Sokolov, et al. (2022). "Orders of magnitude reduction in the computational overhead for quantum many-body problems on quantum computers via an exact transcorrelated method." Phys. Rev. Research
Microcanonical and finite-temperature ab initio molecular dynamics simulations on quantum computers
Igor O. Sokolov, et al. (2021). "Microcanonical and finite-temperature ab initio molecular dynamics simulations on quantum computers." Phys. Rev. Research
Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical equivalents?
Igor O. Sokolov, et al. (2020). "Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime." J. Chem. Phys.
Quantum algorithm for alchemical optimization in material design
Panagiotis Kl. Barkoutsos, Igor O. Sokolov, et al. (2020). "Quantum algorithm for alchemical optimization in material design." Chem. Sci.
Quantum algorithms for electronic structure calculations: particle/hole Hamiltonian and optimized wavefunction expansions
Panagiotis Kl. Barkoutsos, Igor O. Sokolov, et al. (2018). "Quantum algorithms for electronic structure calculations: particle/hole Hamiltonian and optimized wavefunction expansions." Phys. Rev. A
Talks
Quantum-enhanced Neural Exchange-Correlation Functionals
Conference Talk at MoML 2024 Conference at MIT, Cambridge, MA, USA
Enhancing Graph Neural Networks with Quantum Computed Encodings
Conference Poster at ICML 2024, Vienna, Austria
Quantum Machine Learning for Chemistry
Tutorial at Kyiv Academic University Quantum ML School, Kyiv, Ukraine
Quantum Algorithms for Chemistry
Conference Talk at QCxQC Conference (Cambridge Quantum Computing), Virtual
Awards and Recognition
- Top 3 Best Papers - MoML 2024 conference at MIT (September 2024)
- Presenter at ICML 2024 - Top AI conference (September 2024)
- Lead Developer - IBM Quantum Challenge 2021 Chemistry Exercise (September 2021)
- Best Talk Award - QCxQC Conference (September 2020)
Service and Outreach
- Supervised MSc and PhD students at IBM and Pasqal
- Contributed to hackathons and taught quantum chemistry in summer schools
- Open-source contributions to QEX and Qiskit
- Organized journal clubs at Pasqal at the interface of quantum computing, chemistry, and AI