Image above: a prototype battery-powered fiber coupled 635nm LED-based light source for photodynamic therapy, curtesy of Prof. Jonathan Celli, UMB Physics
Spring 2026
Talks take place on Tuesdays, from 11am-noon, in ISC-1200
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, February 24, 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 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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Tuesday, March 24, 2026
No colloquium: Departmental Meeting
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Tuesday, March 31, 2026
Ryotaro Okabe (MIT)
Title: Advancing Quantum Material Discovery with Geometric Machine Learning
Understanding the structure-property relationship is essential in understanding and designing materials with desired functionality. While AI has made significant strides in chemistry and materials, its potential for quantum materials design remains largely untapped. This presentation introduces machine learning frameworks accelerating materials science in two key areas: efficient property prediction and guided materials generation. We introduce Graph Neural Network (GNN) approaches utilizing virtual node strategies for the rapid, high-accuracy prediction of full phonon dispersion from atomic coordinates. This method is orders of magnitude faster than existing potentials, enabling the efficient creation of large-scale phonon property databases critical for materials design. We also present SCIGEN, a generative framework that integrates geometric constraints into a diffusion model to guide the discovery of stable quantum materials candidates. SCIGEN has successfully generated millions of inorganic compounds, with high stability and experimental validation. Together, these GNN-driven approaches showcase a versatile path for both predicting complex material properties and intelligently designing new functional compounds.
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Tuesday, April 7, 2026
Rahul Kulkarni (UMass Boston)
TBA
TBA
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Tuesday, April 14, 2026
Chuteng Zhou (Arms)
TBA
TBA
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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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