A Modified Depolarization Approach for Efficient Quantum Machine
  Learning
Bikram Khanal Pablo Rivas
Quantum Computing in the Noisy Intermediate-Scale Quantum (NISQ) era has
shown promising applications in machine learning, optimization, and
cryptography. Despite the progress, challenges persist due to system noise,
errors, and decoherence that complicate the simulation of quantum systems. The
depolarization channel is a standard tool for simulating a quantum system's
noise. However, modeling such noise for practical applications is
computationally expensive when we have limited hardware resources, as is the
case in the NISQ era. We propose a modified representation for a single-qubit
depolarization channel with two Kraus operators based only on X and Z Pauli
matrices. Our approach reduces the computational complexity from six to four
matrix multiplications per execution of a channel. Experiments on a Quantum
Machine Learning (QML) model on the Iris dataset across various circuit depths
and depolarization rates validate that our approach maintains the model's
accuracy while improving efficiency. This simplified noise model enables more
scalable simulations of quantum circuits under depolarization, advancing
capabilities in the NISQ era.