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Nature Computational Science

Boosting simulation of quantum computers

Jordi Tura

Published Oct 15, 2021• 0 citations
Publisher
DOI
10.1038/s43588-021-00145-5

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Gaussian Boson Sampling (GBS) provides a route toward demonstrating quantum computational advantage. However, optical loss, which reduces the entanglement in the system, can render GBS results classically simulable. We propose a nonlinear photonic architecture based on optical parametric amplifiers (OPAs) arranged in an interferometer network. This active configuration amplifies quantum correlations within the circuit while preserving the #P-hard Hafnian structure of the output probabilities. Using logarithmic negativity, we numerically show that entanglement scales linearly with both the OPA gain and network depth in the lossless limit, and maintains linear scaling with the number of modes under realistic loss rate. These scaling behaviors suggest that classical simulation in lossy scenarios remains computationally intractable. Our results demonstrate that OPA-boosted GBS preserves computational hardness in noisy environments, offering a more effective implementations of near-term photonic quantum computers.
In the context of quantum computing, error correction remains a pivotal challenge, primarily due to imperfect gate operations and environmental interactions. This study introduces a machine learning-based method to simulate and analyze these errors. Utilizing a minimal scalable 2-Majorana-zero-mode (2-MZM) island model within a one-dimensional p-wave topological superconductor, this research employs a detailed Hamiltonian approach combined with a bosonic thermal bath interaction. The analysis is conducted using the Pauli master equation and Monte Carlo simulations. This work’s novel contribution lies in applying Boosted Decision Tree with Gradient boosting (BDTG) and Multi-Layer Perceptron (MLP) machine learning techniques. These methods, trained on Monte Carlo simulation data, showed proficiency in predicting the evolution of error probabilities in the quantum system. The results indicate a significant potential for machine learning to offer a more efficient alternative for simulating quantum computing errors, thereby contributing to developing more robust quantum computing systems.