Parallelizing the stabilizer formalism for quantum machine learning applications

TL;DR

This paper introduces a parallelized stabilizer formalism simulator for quantum machine learning, significantly improving performance and scalability on multi-core devices compared to existing state-vector simulators.

Contribution

The work presents a novel parallelization approach for stabilizer formalism simulators, enabling efficient deployment on multi-core systems for quantum machine learning applications.

Findings

Python implementation is 4.23 times faster than Qiskit for 4-qubit, 60,200-gate simulations.

Parallelization significantly enhances the scalability of stabilizer formalism simulators.

The approach reduces computational resources needed for deep quantum machine learning models.

Abstract

The quantum machine learning model is emerging as a new model that merges quantum computing and machine learning. Simulating very deep quantum machine learning models requires a lot of resources, increasing exponentially based on the number of qubits and polynomially based on the depth value. Almost all related works use state-vector-based simulators due to their parallelization and scalability. Extended stabilizer formalism simulators solve the same problem with fewer computations because they act on stabilizers rather than long vectors. However, the gate application sequential property leads to less popularity and poor performance. In this work, we parallelize the process, making it feasible to deploy on multi-core devices. The results show that the proposal implementation on Python is faster than Qiskit, the current fastest simulator, 4.23 times in the case of 4-qubits, 60,2K gates.