$i$-QER: An Intelligent Approach towards Quantum Error Reduction

Abstract

Quantum computing has become a promising computing approach because of its capability to solve certain problems, exponentially faster than classical computers. A $n$-qubit quantum system is capable of providing $2^n$ computational space to a quantum algorithm. However, quantum computers are prone to errors. Quantum circuits that can reliably run on today's Noisy Intermediate-Scale Quantum (NISQ) devices are not only limited by their qubit counts but also by their noisy gate operations. In this paper, we have introduced $i$-QER, a scalable machine learning-based approach to evaluate errors in a quantum circuit and helps to reduce these without using any additional quantum resources. The $i$-QER predicts possible errors in a given quantum circuit using supervised learning models. If the predicted error is above a pre-specified threshold, it cuts the large quantum circuit into two smaller sub-circuits using an error-influenced fragmentation strategy for the first time to the best of our knowledge. The proposed fragmentation process is iterated until the predicted error reaches below the threshold for each sub-circuit. The sub-circuits are then executed on a quantum device. Classical reconstruction of the outputs obtained from the sub-circuits can generate the output of the complete circuit. Thus, $i$-QER also provides classical control over a scalable hybrid computing approach, which is a combination of quantum and classical computers. The $i$-QER tool is available at https://github.com/SaikatBasu90/i-QER.

19 pages, 9 figures

References in corpus (5)

• Charge insensitive qubit design derived from the Cooper pair box
• CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations
• Determining the parity of a permutation using an experimental NMR qutrit
• Quantum Divide and Compute: Exploring The Effect of Different Noise Sources
• Quantum Divide and Compute: Hardware Demonstrations and Noisy Simulations

Cited by in corpus (5)

• High Dimensional Quantum Machine Learning With Small Quantum Computers
• Classical Splitting of Parametrized Quantum Circuits
• Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey
• SCIM MILQ: An HPC Quantum Scheduler
• FragQC: An Efficient Quantum Error Reduction Technique using Quantum Circuit Fragmentation