Baratz’s contribution is organizational and product-focused. D-Wave identifies him as the executive who previously led research and development and product delivery, and as its chief executive since 2020. The company’s computing offering combines annealing hardware, cloud access, development tools and hybrid solvers. This makes his work relevant to the practical question of how researchers and organizations gain access to quantum resources. The profile credits leadership of that development effort without assigning him sole authorship of the hardware or treating company performance claims as independently proven computational advantage.
Cecil and Ida Green Professor of Physics, Emeritus; researcher at Google
Massachusetts Institute of Technology; Google · United States
A theorist who helped develop adiabatic quantum computation and the quantum approximate optimization algorithm, exploring ways quantum dynamics can be used to solve computational problems.
Farhi’s research expands the set of ways to organize quantum computation. Adiabatic computation encodes a problem in the gradual evolution of a physical system, while the quantum approximate optimization algorithm uses alternating operations and adjustable parameters. Both approaches connect mathematical problems with quantum dynamics. Their inclusion here reflects the influence of these frameworks on research; it does not assume that either delivers a general practical advantage over the best classical methods.
Professor; Head, Specialized Academy for Quantum Computing
Institute of Science Tokyo · Japan
Helped establish quantum annealing as an approach to optimization, using statistical physics to investigate how quantum fluctuations guide systems through difficult energy landscapes.
Nishimori connects quantum computation with the statistical mechanics of complex systems. His work with Tadashi Kadowaki introduced quantum annealing in a transverse-field Ising model, making quantum fluctuations a controllable ingredient in optimization. Later research with Yuya Seki investigated how changing those fluctuations can alter the phase transitions that obstruct an annealing process. These results provide concepts and testable models for an important computing approach. They do not establish a universal speedup over classical optimization methods.
