TY - GEN
T1 - Predicting the Optimizability for Workflow Decisions
AU - Mete, Burak
AU - Schulz, Martin
AU - Ruefenacht, Martin
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With the rapid advancement of quantum technologies, the integration between classical and quantum computing systems is an active area of research critical to future development. The coupling between these systems requires both to be as efficient as possible. One of the key elements to increase efficiency on the quantum side is circuit optimization. The goal is to execute the circuit on the desired hardware in less time and with less complexity, thereby reducing the impact of noise on the quantum system. However, the optimization process does not guarantee to generate improved results, yet it is always a computationally highly complex task that can create significant load for the classical computing side. To mitigate this problem, we propose a novel approach to predict the optimizability of any circuit using a Machine Learning-based algorithm within the decision workflow. This optimizes the most suitable circuits thereby increasing efficiency of the optimization process itself.
AB - With the rapid advancement of quantum technologies, the integration between classical and quantum computing systems is an active area of research critical to future development. The coupling between these systems requires both to be as efficient as possible. One of the key elements to increase efficiency on the quantum side is circuit optimization. The goal is to execute the circuit on the desired hardware in less time and with less complexity, thereby reducing the impact of noise on the quantum system. However, the optimization process does not guarantee to generate improved results, yet it is always a computationally highly complex task that can create significant load for the classical computing side. To mitigate this problem, we propose a novel approach to predict the optimizability of any circuit using a Machine Learning-based algorithm within the decision workflow. This optimizes the most suitable circuits thereby increasing efficiency of the optimization process itself.
KW - Long-Short Term Memory
KW - Quantum Circuit Optimization
KW - Quantum Compilation
KW - Quantum Computing
KW - Quantum Systems
KW - Sequential Learning
UR - http://www.scopus.com/inward/record.url?scp=85148603619&partnerID=8YFLogxK
U2 - 10.1109/QCS56647.2022.00013
DO - 10.1109/QCS56647.2022.00013
M3 - Conference contribution
AN - SCOPUS:85148603619
T3 - Proceedings of QCS 2022: 3rd International Workshop on Quantum Computing Software, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis
SP - 68
EP - 74
BT - Proceedings of QCS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE/ACM International Workshop on Quantum Computing Software, QCS 2022
Y2 - 13 November 2022 through 13 November 2022
ER -