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Hamiltonian engineering of superconducting circuits, fluxonium design, and device characterization
Quantum Error CorrectionHardware-aware QEC schemes, measurement-free protocols, and erasure-based error correction
Tensor NetworksTensor network methods for quantum simulation and optimization
Neural NetworksMachine learning applications for quantum computing, from decoder optimization to circuit design
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Research
Quantum hardware theory — superconducting device physics, error correction, tensor networks, and machine learning.
Fluxonium Erasure
Erasure error detection and conversion in fluxonium qubits for improved quantum error correction.
View project →Neural Network for detecting leakage in readout
Depending on experimentalist's need, we have a potential small project on implementing a neural network for detecting leakage in readout.
Methodological paper on optimizing pulse sequences
We are writing a methodological paper on optimizing pulse sequences for superconducting qubits.
GPU Pauli frame QEC simulation
GPU-accelerated simulation tools for superconducting quantum circuits and coupled systems.
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Measurement-Free Quantum Error Correction
Novel QEC protocols that eliminate measurement overhead for improved qubit coherence.
View project →Many-Body Quantum System Simulation
Using tensor networks to simulate strongly correlated quantum systems.
ML pulse optimization
Using machine learning to optimize pulse sequences for superconducting qubits.
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