How do we represent, process, and infer information in a noisy world, and what changes when quantum physics does the representing?
We study information at the meeting point of quantum physics, signal processing, and computation. Signals from a molecule's energy spectrum, a wireless channel, or a heartbeat all carry hidden mathematical structure. Our work asks what happens when that structure is expressed in quantum form: what becomes easier to compute, what becomes harder to measure, and what new kinds of inference become possible.
At the core of our approach is structure-aware quantum algorithm design. By exploiting patterns such as sparsity, banded and Toeplitz operators, and polynomial representations, we build quantum algorithms for simulation, spectral estimation, and filtering whose cost and error we can state precisely. The goal is not just a working circuit, but a clear account of how accuracy, depth, and resources scale together.
We also study how open quantum systems learn and remember. Through quantum reservoir computing, weak measurement, and dissipative dynamics, we explore how physical quantum systems process information over time, with direct relevance to biosignals and sensor streams.
Throughout, we keep our ideas grounded in real hardware and real problems. Working with IBM Quantum and RIKEN, we test our methods on superconducting processors and apply them to molecular simulation, biomedical signal processing, communication, and sensing, asking what today's noisy devices can actually deliver.
What does it mean to represent a signal quantum mechanically? We encode signals, distributions, and operators as quantum states and ask how filtering, spectral analysis, and estimation change as a result: what can be inferred, at what cost, and under what uncertainty limits. Applications include biomedical signal denoising and source detection in large sensor arrays.
Many operators in science and engineering have structure: Toeplitz, Hankel, banded, or sparse. We design Hamiltonian simulation and eigenspectrum estimation algorithms that exploit this structure to reduce gate count and circuit depth, with rigorous error analysis and resource estimates for running them on today's superconducting hardware.
Before a continuous function or physical operator can be processed on a quantum computer, it must be approximated and encoded. We develop polynomial and basis expansions that make this step explicit and controllable, showing exactly how accuracy trades against circuit resources, as demonstrated in simulating molecular potential energy operators.
How do noisy, open quantum systems remember and process information over time? We study quantum reservoir computing, weak measurement, and Lindblad dynamics to understand memory and learning capacity in physical quantum systems, and apply shallow entangled circuits to time-series prediction on real quantum devices.
Aug. 2026 – Present
Quantum information processing, learning theory, and quantum algorithms at the School of Electrical Engineering and Computer Science.
Sept. 2025 – Present
Quantum information physics, quantum reservoir computing, weak measurement, and open quantum dynamics.
Jan. 2024 – Jul. 2026 · currently on sabbatical
Hardware-aware quantum algorithms, functional approximation, circuit compilation, quantum-centric computation, and experiments on superconducting quantum processors.
Sept. 2020 – Feb. 2025
Synopsis submitted in Oct. 2023; defended in Feb. 2025. Quantum algorithms and architectures for structured Hamiltonian operators with efficient gate complexity.
Jan. 2018 – Sept. 2020
Synopsis submitted in Feb. 2020; defended in Sept. 2020. Research in statistical signal processing, estimation, and learning.
Scientific Reports
Scientific Reports
Scientific Reports
IEEE Transactions on Quantum Engineering
IEEE Transactions on Communications