Quantum generative AI foundation models: integrating VQAs with fault-tolerant error correction
TL;DR
This paper proposes a hybrid quantum-classical framework utilizing isometric Tree Tensor Networks (TTNs) and a novel Quantum Self-Attention (QSA) subroutine, capable of compressing the latent space of a classical 10-parameter Large Language Model into a 10-parameter quantum neural network via amplitude encoding.
Abstract
The exponential parameter scaling of classical transformer models confronts severe physical and economic barriers. To sustain generative AI capabilities, alternative computational paradigms must be explored. This paper projects the architecture and scaling laws of Quantum Generative AI Foundation Models by integrating Variational Quantum Algorithms (VQAs) with Fault-Tolerant Quantum Error Correction (QEC). We propose a hybrid quantum-classical framework utilizing isometric Tree Tensor Networks (TTNs) and a novel Quantum Self-Attention (QSA) subroutine, capable of compressing the latent space of a classical 10 11 -parameter Large Language Model (LLM) into a 10 6 parameter quantum neural network via amplitude encoding. To circumvent Noise-Induced Barren Plateaus (NIBP), we map the training requirements onto a fault-tolerant regime. Assuming a surface code QEC overhead with a physical-to-logical qubit ratio of approximately 2,000:1, we establish the resource requirements for a VQA operating below the 10 −4 physical gate error threshold. Our numerical projections indicate an approximate 45% reduction in total energy expenditure for frontier model training and a per-query attention processing complexity of O ( L log d ) . The quantum framework fundamentally subverts the classical compute wall by substituting linear parameter scaling with logarithmic latent space compression, acknowledging that full attention matrix computation retains a dependency on measurement precision overheads.