Quantum computing has the potential to revolutionize various fields, but practical deployments capable of solving real-world problems face significant headwinds due to the fragile nature of quantum systems. Qubits, the fundamental units of quantum information, are inherently unstable and susceptible to decoherence—a process by which interactions with the environment cause them to lose their quantum properties. External noise from thermal fluctuations, vibrations, or electromagnetic fields exacerbates this instability, necessitating extreme isolation and control, often achieved by maintaining qubits at ultra-low temperatures. Preserving quantum coherence long enough to perform meaningful computations remains one of the most formidable obstacles, particularly as systems scale.

Another major challenge is ensuring the accuracy and reliability of quantum operations, or “gates.” Quantum gates must manipulate qubits with extraordinary precision, yet hardware imperfections introduce errors that accumulate over time, jeopardizing the integrity of computations. While quantum error correction techniques offer potential solutions, they demand enormous computational resources, dramatically increasing hardware requirements. These physical and technical limitations present fundamental hurdles to building scalable, practical quantum computers.

The Intersection With Neural Networks

One promising approach to mitigating these issues lies in the unexpected ability of classical neural networks to approximate quantum states. As discussed in When Can Classical Neural Networks Represent Quantum States? (Yang et al., 2024), certain neural network architectures—such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs)—can be trained to exhibit quantum properties. This insight suggests that instead of relying entirely on fragile physical qubits, classical neural networks could serve as an intermediary computational layer, learning and simulating quantum behaviors in ways that reduce the burden on quantum processors. Yang further proposes that classical deep learning models may be able to efficiently learn and encode quantum correlations, allowing them to predict and correct errors dynamically, thereby improving fault tolerance without the need for excessive physical qubits.

Neural networks capable of representing quantum states could also enable new forms of hybrid computing. Instead of viewing artificial intelligence (AI) and quantum computing as separate domains, recent research suggests a future where they complement one another. Classical AI models could handle optimization, control, and data preprocessing, while quantum systems tackle computationally intractable problems.

Ultimately, the interplay between quantum mechanics and AI will most likely reshape our approach to computation. While quantum computers remain in their infancy, AI could provide a bridge to unlock their potential. By harnessing classical neural networks to mimic quantum properties, the scientific community may overcome the current limitations of quantum hardware and accelerate the development of practical, scalable quantum systems. The boundary between classical and quantum computation may not be as rigid as once thought.