A5100346766- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Advanced Bandit Algorithms Research
- Cloud Computing and Resource Management
- Stochastic Gradient Optimization Techniques
- Metaheuristic Optimization Algorithms Research
- Advanced Multi-Objective Optimization Algorithms
- Electric Power System Optimization
- Distributed and Parallel Computing Systems
- Parallel Computing and Optimization Techniques
- Statistical Methods and Inference
- Computability, Logic, AI Algorithms
Northeastern University
2021
Google (United States)
2020
IBM Research - Thomas J. Watson Research Center
2019
We introduce Markov chain Monte Carlo quantum (MCMCQ), a novel compiler-level optimization of programs that accounts for the emerging programming mode. MCMCQ is first systematic approach stochastic quantum-program optimization, targeting program performance, correctness, and noise tolerance. An evaluation over 500 confirms its effectiveness.
In recent years, industry and academia have made tremendous research attempts to implement quantum computing technologies. But is still grounded by numerous critical barriers, leading its low accessibility practicality. To overcome this problem, we propose an end-to-end framework for mapping computationally hard problems on a computer via reduction.
Numerical optimization has been extensively used in many real-world applications related to Scientific Computing, Artificial Intelligence and, more recently, Quantum Computing. However, existing optimizers conduct their internal computations sequentially, which affects performance. We observed a general pattern that enabled us parallelize such and achieve significant speedup. designed novel parallelization algorithm for optimizers, consists of detection, prediction, precomputation, caching....
In recent years, tremendous efforts from both the industrial and academic research communities have been put into bringing forth quantum computing technologies. With potential proliferation of universal computers on horizon, computing, however, is still severely grounded by numerous grave barriers, which lead to its low accessibility practicality. For example, vastly different underlying models, combined with steep background knowledge requirements, makes it extremely difficult, if possible...