Publications
Papers and preprints. Also on arXiv and Google Scholar.
2027
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Near-Optimal Learning of Local Lindbladians
ACM-SIAM Symposium on Discrete Algorithms (SODA 2027), accepted
A near-optimal protocol for learning local Lindbladians from finite-time channel probes, with matching lower bounds on channel uses and total evolution time.
2026
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Perspective on AI and quantum open-source software
arXiv preprint, 2026
Verification practices and contribution standards for AI-assisted quantum open-source software, informed by public repositories and community surveys.
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Evaluating Verified Autonomy in Quantum Engineering
arXiv preprint, 2026
Quantum-Harbor provides a virtual laboratory for agent-operated quantum engineering; QIQCBench evaluates agents through recorded actions and independently verified conclusions.
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Quantum enhanced rare event discovery and sampling
arXiv preprint, 2026
A quantum algorithm that discovers and samples rare events without knowing them in advance, with optimal quantum scaling in the rarity threshold.
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Accelerating Inference for Multilayer Neural Networks with Quantum Computers
International Conference on Learning Representations (ICLR) 2026
A quantum inference method for multilayer neural networks, with an analysis of when it improves on classical evaluation.
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Provable learning of quantum states with graphical models
Physical Review Research 8, 013109, 2026
Restricted Boltzmann-machine structure yields provable learning procedures for a class of quantum states, with exponentially fewer samples than naive tomography.
2025
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Fast-forwardable Lindbladians imply quantum phase estimation
arXiv preprint (2025); contributed talk at QIP 2026
Fast-forwardable Lindbladian dynamics can implement quantum phase estimation at the Heisenberg limit, connecting open-system simulation to a standard algorithmic primitive.
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A Gentle Introduction to Quantum Machine Learning
Earlier title: Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
Springer Singapore, 2025
The main models, algorithms, and implementation assumptions of quantum machine learning, written for readers with a machine-learning background.
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Online Learning of Pure States is as Hard as Mixed States
Neural Information Processing Systems (NeurIPS) 2025
Online learning of pure quantum states can be as hard as learning mixed states: the two settings have the same regret scaling under ℓ1 loss.
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Designing a Nearly Optimal Quantum Algorithm for Linear Differential Equations via Lindbladians
Earlier title: Design nearly optimal quantum algorithm for linear differential equations via Lindbladians
Physical Review Letters 135, 120604, 2025
Linear differential equations encoded in Lindbladian dynamics, with query complexity close to the known lower bound under a plausible input model.
2024
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Quantum Transformer: Accelerating model inference via quantum linear algebra
Earlier title: Quantum linear algebra is all you need for Transformer architectures
arXiv preprint, 2024
The operations inside a Transformer expressed through quantum linear-algebra routines, with the parameter regimes in which inference is faster.
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Nonlinear transformation of complex amplitudes via quantum singular value transformation
Physical Review Research 6, 043227, 2024
Nonlinear transformations of complex amplitudes built from quantum singular value transformation and amplitude encoding.
2023
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Estimating properties of a quantum state by importance-sampled operator shadows
arXiv preprint, 2023
Importance-sampled operator shadows reduce the measurements needed to estimate selected properties of a quantum state.
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Quantum Alphatron: quantum advantage for learning with kernels and noise
Quantum 7, 1174, 2023
Quantum versions of the Alphatron algorithm, with polynomial speedups for kernel evaluation in specified learning regimes.