Superconducting-processor control, coupling and calibration
Industry-commissioned contract research on control, coupling and calibration of superconducting quantum processors towards quantum error correction.
Research
Models. Methods.
Quantum possibilities.
We build theoretical and computational tools to understand quantum systems, identify their limitations and improve their control.
Direction 01
Robust quantum control
Designing gates that work with imperfect devices.
We develop composite pulses, geometric gates and optimal-control methods for spin qubits and other quantum systems, with attention to noise, leakage and hardware constraints.
Our control research spans analytically designed noise-resistant pulses and numerically optimized gates. Current questions include pulse-level compilation, robust geometric control, and how gate design responds to realistic noise and device constraints.
Related work
Composite pulses for robust universal control of singlet-triplet qubits↗Jenga-Krotov algorithm: Efficient compilation of multi-qubit gates for exchange-only qubits↗Universal robust quantum gates via doubly geometric control↗Direction 02
Physics-informed quantum learning
Connecting physical models with machine learning.
Our work includes Hamiltonian learning, noise characterization, variational algorithms and automated quantum sensing. We ask when learning-based methods improve accuracy, efficiency and generalization.
We combine known physical structure with data-driven optimization. Recent work explores inverse physics-informed neural networks for Hamiltonian learning, denoising networks for noisy variational algorithms, and reinforcement-learned circuit structures for quantum sensing.
Related work
Hamiltonian learning via inverse physics-informed neural networks↗Accelerating noisy variational quantum algorithms with physics-informed denoising networks↗Automating variational quantum sensing through reinforcement-learned circuit structures↗Direction 03
Quantum devices & many-body physics
Understanding the systems beneath the algorithms.
From multi-electron quantum dots to correlated materials and lattice models, we use microscopic theory, tensor networks and numerical methods to study interactions, phases and collective behavior.
Microscopic modeling helps explain charge stability, exchange interactions, leakage and crosstalk in quantum-dot devices. In parallel, we investigate statistical and many-body systems using transfer matrices, tensor networks and related numerical methods.
Related work
Exploring entanglement spectra and phase diagrams in multi-electron quantum dot chains↗Free-fermion approach to the partition function zeros: Special boundary conditions and product form of solution↗Tensor network investigation of the monomer-dimer model on the honeycomb lattice↗Research partnerships
Connecting methods
with physical platforms.
Our research is supported by industry-commissioned projects on superconducting-processor control and quantum and AI-based algorithms, alongside academic research grants.
Quantum and AI-based algorithms
Industry-commissioned research on quantum and AI-based algorithms for the control of near-term quantum systems.
Optimal control of silicon-based quantum devices
Theoretical methods for controlling semiconductor-based quantum systems.
Efficient and robust multi-qubit gates
RGC Germany/Hong Kong Joint Research Scheme in solid-state quantum computing.