4 papers · 1 filter
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…
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…
QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa +2
In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuit…