Benefits of Open Quantum Systems for Quantum Machine Learning

Abstract

Quantum machine learning is a discipline that holds the promise of revolutionizing data processing and problem-solving. However, dissipation and noise arising from the coupling with the environment are commonly perceived as major obstacles to its practical exploitation, as they impact the coherence and performance of the utilized quantum devices. Significant efforts have been dedicated to mitigate and control their negative effects on these devices. This Perspective takes a different approach, aiming to harness the potential of noise and dissipation instead of combatting them. Surprisingly, it is shown that these seemingly detrimental factors can provide substantial advantages in the operation of quantum machine learning algorithms under certain circumstances. Exploring and understanding the implications of adapting quantum machine learning algorithms to open quantum systems opens up pathways for devising strategies that effectively leverage noise and dissipation. The recent works analyzed in this Perspective represent only initial steps towards uncovering other potential hidden benefits that dissipation and noise may offer. As exploration in this field continues, significant discoveries are anticipated that could reshape the future of quantum computing.

arXiv comment13 pages, 3 figures

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Cited by in corpus (7)

• Quantum reservoir computing on random regular graphs
• Impact of Amplitude and Phase Damping Noise on Quantum Reinforcement Learning: Challenges and Opportunities
• Almost fault-tolerant quantum machine learning with drastic overhead reduction
• Demonstration of system-bath physics on a gate-based quantum computer
• Exploring fixed points and eigenstates of quantum systems with reinforcement learning
• Connection between memory performance and optical absorption in quantum reservoir computing
• Quantum Simulation of Two-Level $PT$-Symmetric Systems Using Hermitian Hamiltonians