yongshan DOT ding AT yale DOT edu
I am an Assistant Professor of Computer Science at Yale University. I am a member of the Yale Quantum Institute (YQI) and affiliated with the Computer Systems Lab (CSL). Our Quantum Computer Systems (QCS) group produces impactful research that improves the capability and performance of next-generation quantum computing systems. We work on a broad set of problems related to algorithms and computer architecture in quantum computing.
Quantum computers hold enormous potential in solving some classically intractable problems. To unlock this potential, researchers around the globe are racing to develop practical quantum algorithms, software, and hardware. In our group, we are especially interested in projects that bridge theory and application. For example, our current research efforts consist of several themes:
Enabling Quantum Algorithms in the Presence of Noise — Emerging quantum computing applications need theoretical efficiency guarantees and practical noise-resilient implementations in order to show their advantage over classical algorithms. We are exploring quantum circuit designs that achieve high accuracy for problems such as Hamiltonian simulations, optimizations, and machine learning tasks.
Designing Efficient Compiling Software — Traditional quantum compilation typically seeks universality, in that it decomposes any target unitary into a sequence of instructions from a set of basis gates. We are developing systems software that takes advantage of device connectivity and ancillary qubits to better understand the conditions for efficient universality.
Architectural Support for Quantum Noise Mitigation and Error Correction — We are recently developing application- and device-adapted error correction methods and exploring the role of architecture in enabling efficient detecting and decoding of errors.
CIQC Colloquium at UC Berkeley, November 2021
Searching and Training Parametrized Quantum Circuits in the Presence of Noise
Parametrized quantum circuits are a promising candidate for hybrid classical-quantum algorithms. However, the training performance and output fidelity of these circuits are significantly degraded under the influence of noise. To truly unlock the potential of noisy intermediate-scale quantum devices, we need to adapt the design and implementation of quantum circuits to ... Learn more ❯❯