Tuesday, February 17, 2026
Alioscia Hamma (Universita di Napoli Federico II)

Title: To learn a mocking-black hole 

The problem of decoding information scrambled by a black hole lies at the heart of the black hole information paradox: semiclassical Hawking radiation appears thermal and featureless, suggesting information loss, yet quantum theory mandates unitarity and information preservation. In this talk, I will explore a quantum information-theoretic approach to this challenge inspired by the Hayden-Preskill-Yoshida-Kitaev protocol. We show that even for “quasi-chaotic” scramblers — unitaries that mimic the internal dynamics of black holes — it is possible to construct efficient decoders without prior detailed knowledge of the scrambler. This result challenges the conventional intuition that chaotic scrambling irrevocably hides information behind exponential complexity barriers, as conjectured in holographic models of black holes. Moreover, we prove something impossible: unscrambling of complex behavior can be achieved by a simple process. 

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Tuesday, March 3, 2026
Akira Tanji (Keio University, Japan)

Title: Learning from Quantum Data with Limited Copies and Imperfections

Learning from quantum data is constrained by limited copies of quantum states, trainability issues, and imperfections in realistic quantum systems. In this talk, I present learning algorithms designed to address these practical limitations. First, I present a framework for quantum phase classification via partial tomography-based quantum hypothesis testing. Using classical shadows, a standard partial-tomography technique, we estimate reduced density matrices and construct localized Helstrom measurements, enabling accurate classification while reducing the required number of state copies. Second, I describe learning from imperfect quantum data via unsupervised domain adaptation on classical shadows. By combining shadow-based features with domain-adversarial learning, the method transfers knowledge from ideal states to noisy or algorithmically biased ones, enabling reliable predictions under hardware noise and algorithmic errors. These results advance quantum data analysis toward practical applications in realistic quantum settings. 

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Thursday, March 12, 2026
Alioscia Hamma (Universita di Napoli Federico II)

Title: QTris, a game for Quantum Mechanics


One of the major obstacles in the teaching and understanding of quantum mechanics is the interpretation of its rules and its main constructs, such as the wave-function. One can take seriously the idea that understanding means to understand how to use the rules of the game. QTris is a boardgame for the gentle introduction and teaching of quantum mechanics from elementary to graduate school. I even use it to remind people in my group what quantum mecanichs is about. 

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Tuesday, March 31, 2026
Ryotaro Okabe (MIT)

Title: Accelerating Quantum Material Discovery with Machine Learning: 

From Property Prediction to Materials Design

 Materials are the foundation for solving nearly every major technological challenge of our time — including quantum computing, secure communication, clean energy, medical imaging, and next-generation electronics. Quantum materials, including topological insulators, frustrated magnets, and unconventional superconductors, sit at the frontier of these applications, but discovering them is extraordinarily hard. The space of possible crystal structures is astronomically large, and each candidate requires costly first-principles calculations or slow experimental synthesis. In this talk, I will describe a research program built on a central idea: machine learning guided by the physical symmetry of crystal structures is more accurate, data-efficient, and physically interpretable than black-box alternatives. I will present work on predicting phonon dispersions and optical spectra directly from crystal structure, and on a generative AI framework that produces millions of candidate quantum materials filtered for stability, novelty, and experimental viability. I will also discuss how AI models are beginning to assist with synthesis planning, and what an AI-driven discovery loop for quantum materials could look like in the near term. 

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Tuesday, April 7, 2026
No colloquium: Departmental Meeting 

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Tuesday, April 14, 2026
Chuteng Zhou (Arms)

Model Hardware Co-design in the Age of AI

Artificial Intelligence (AI) is transforming society, but its rapid growth comes with increasing demands on energy and computational resources. At the same time, it presents a unique opportunity to rethink the computing stack. A key emerging trend is the tighter coupling between machine learning models and hardware: models are increasingly designed to match the characteristics of target hardware, while new hardware architectures are developed with direct feedback from model behavior. This co-design paradigm is becoming increasingly important for sustaining AI scaling. In this talk, I will present recent industry research on model-hardware co-design, including approaches for analog Compute-in-Memory (CiM), model optimization for edge Neural Processing Units (NPUs) and algorithm-hardware co-design for low-precision federated learning. I will argue that closer integration between models and hardware is critical to unlocking the next generation of efficient, scalable and decentralized AI systems.

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Tuesday, April 21, 2026
Berthy Feng (MIT)

TBA

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Tuesday, April 28, 2026
Matthew Weiss (UMass Boston)

TBA

TBA

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Tuesday, May 5, 2026
No colloquium: Departmental Meeting 

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Tuesday, May 12, 2026
Liang Fu (MIT)

TBA

TBA 

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