47 citations · 96 across the 11 of their papers we have counts for
11 papers
Operational criteria for quantum advantage in latency-constrained nonlocal games
Changhao Li, Seigo Kikura, Akihisa Goban +2
Remote entanglement enables coordinated decision making without communication and produces correlations beyond those achievable by any classical strategy, representing a practical…
Metriq: A Collaborative Platform for Benchmarking Quantum Computers
Alessandro Cosentino, Changhao Li, Vincent Russo +6
The fragmented landscape of quantum computer benchmarks, characterized by system-specific tools and inconsistent evaluation methodologies, hinders reliable cross-platform performan…
Parameter Setting Heuristics Make the Quantum Approximate Optimization Algorithm Suitable for the Early Fault-Tolerant Era
Zichang He, Ruslan Shaydulin, Dylan Herman +4
Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising quantum heuristics for combinatorial optimization. While QAOA has been shown to perform well on small…
QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest
Romina Yalovetzky, Niraj Kumar, Changhao Li +1
Random Forest (RF) is a popular tree-ensemble method for supervised learning, prized for its ease of use and flexibility. Online RF models require to account for new training data…
Prospects of Privacy Advantage in Quantum Machine Learning
Jamie Heredge, Niraj Kumar, Dylan Herman +5
Ensuring data privacy in machine learning models is critical, particularly in distributed settings where model gradients are typically shared among multiple parties to allow collab…
Quantum counterdiabatic driving enhanced by two-stage local control
Changhao Li, Jiayu Shen, Ruslan Shaydulin +1
Counterdiabatic driving can suppress diabatic losses during adiabatic ground state preparation. As its implementation necessitates the generation of an adiabatic gauge potential, w…
Q-CHOP: Quantum constrained Hamiltonian optimization
Michael A. Perlin, Ruslan Shaydulin, Benjamin P. Hall +7
Combinatorial optimization problems that arise in science and industry typically have constraints. Yet the presence of constraints makes them challenging to tackle using both class…
Privacy-preserving quantum federated learning via gradient hiding
Changhao Li, Niraj Kumar, Zhixin Song +2
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed…
Training Transformers with 4-bit Integers
Haocheng Xi, Changhao Li, Jianfei Chen +1
Quantizing the activation, weight, and gradient to 4-bit is promising to accelerate neural network training. However, existing 4-bit training methods require custom numerical forma…
Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Optimization
Zichang He, Ruslan Shaydulin, Shouvanik Chakrabarti +4
Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent…
Can ChatGPT be used to generate scientific hypotheses?
Yang Jeong Park, Daniel Kaplan, Zhichu Ren +5
We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do. While the error rate is high, generative AI seems t…