6 papers
When AI meets quantum information: A comprehensive review
Min Chen, Yu Gan, Xin Jin +15
Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum syste…
Exponentially many initializations to avoid barren plateaus
Ankit Kulshrestha, Ricard Puig, Diego GarcÃa-MartÃn +4
Barren plateaus are stated as an average-case phenomenon: pick an ansatz, initialize it naively, and concentration follows. This has led to the common view that a potential cure fo…
CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem
Ankit Kulshrestha, Xiaoyuan Liu
A quantum compiler is a critical piece in the quantum computing pipeline since it allows an abstract quantum circuit to be run on a physical quantum computer. One extremely importa…
QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning
Kien X. Nguyen, Ankit Kulshrestha, Ilya Safro +1
Qubit routing is a fundamental problem in quantum compilation, known to be NP-hard. Its dynamic nature makes local routing decisions propagate and compound over time, making global…
On the importance of hyperparameters in initializing parameterized quantum circuits
Ankit Kulshrestha, Sarvagya Upadhyay
There has been intensive research on increasing the utility and performance of Parameterized Quantum Circuits (PQCs) in the past couple of years. Owing to this research, there are…
Neural Architecture Search Algorithms for Quantum Autoencoders
Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa +1
The design of quantum circuits is currently driven by the specific objectives of the quantum algorithm in question. This approach thus relies on a significant manual effort by the…