Demonstration of a Distance-5 Surface Code on a 72-Qubit Processor
Akiko Tanaka, Kenji Yamamoto, John Preskill
Nature2024
Journal Article
203I am a Professor in the Department of Applied Physics at the University of Tokyo and Principal Investigator at RIKEN Center for Quantum Computing.
My group works on the experimental realization of fault-tolerant quantum computers using superconducting qubits. We design and fabricate quantum processors, develop error correction protocols, and explore near-term quantum algorithms for chemistry and optimization.
I am a recipient of the Nishina Memorial Prize (2022) and a Fellow of the American Physical Society.
PhD in Quantum Information Science
Caltech, Physics
2010 - 2015
Pasadena, CA
Thesis: Error Thresholds for Topological Quantum Codes
Professor in Computer Science
University of Tokyo, Applied Physics
2019 - Present
Tokyo, Japan
Research Staff Member
IBM Quantum
2015 - 2019
Yorktown Heights, NY
Akiko Tanaka, Kenji Yamamoto, John Preskill
Kenji Yamamoto, Akiko Tanaka
Akiko Tanaka, Haruki Sato, Alán Aspuru-Guzik
Akiko Tanaka, Kenji Yamamoto
Foundations of Faithful Reasoning in Language Models
Developing training methods and evaluation frameworks for improving logical consistency in large language models.
Human-Aligned NLP Systems
Multi-institution project on building NLP systems that align with human values and intentions.
MIT Technology Review Innovators Under 35
Recognized for pioneering work on faithful reasoning in AI systems.
Best Paper Award
NSF CAREER Award
Early-career faculty award for research on interpretable language models.
Qubit fabrication and characterization
Variational quantum algorithms
Quantum error correction protocols
Postdoc position on our DARPA-funded project on human-aligned NLP systems.
Requirements
PhD in NLP, ML, or related field. Publications in top venues.
Graduate seminar covering modern approaches to NLP including large language models, in-context learning, and alignment techniques.
Introduction to machine learning concepts, supervised and unsupervised methods, with a focus on deep learning fundamentals.
Excited to share that our paper 'Scaling Faithful Reasoning in Large Language Models' has been accepted as an oral presentation at NeurIPS 2024!
Read moreI am recruiting 2 PhD students to start Fall 2025. Research areas: LLM reasoning, interpretability, and alignment. Please apply through the MIT EECS admissions portal.
Read moreMIT Technology Review
The Researchers Making AI Think More Clearly
Feature article on our group's work on faithful reasoning in language models.
Lex Fridman Podcast
AI Alignment: Where Are We Now?
Conversation about the current state of AI alignment research and practical approaches.
Department of Applied Physics
University of Tokyo