Aug 2026· Frontiers of Computer Science· 0 citations· 20 references
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.
As IQP circuits produce remarkably low intermediate magic relative to phase-randomised states with the same sampling distributions, this renders IQP-based quantum generative models as promising candidates for resource-efficient demonstrations of quantum advantage on early fault-tolerant architectures.
This research provides a scalable method for integrating near-term noisy intermediate-scale Quantum (NISQ) devices into state-of-the-art deep learning pipelines, fostering the further real-world adoption of hybrid quantum-classical systems for demanding artificial intelligence tasks.
Arvindhan Muthusamy, Azween Abdullah, Dr. L. Arockiam· International journal of com...· 0 citations
Quantum attention mechanisms integrate parameterised quantum circuits or quantum linear algebra primitives into transformer architectures, drawing on superposition and entanglement in the attention computation. The field has produced more than two dozen proposals between 2022 and 2026, spanning Parameterised Quantum Circuits (PQC) on near-term hardware, Quantum Linear Algebra (QLA) on fault-tolerant hardware, and quantum annealing. This survey contributes a refined taxonomy with an attention-semantics dimension distinguishing pairwise from holistic mechanisms; a resource-realism analysis that states the measurement-overhead cost of pairwise quantum attention in the Noisy Intermediate-Scale Quantum (NISQ) regime as a proposition under explicit assumptions, with the architecture classes falling outside those assumptions identified, organised within a four-axis design framework covering sequence and qubit scaling, circuit depth, encoding overhead, and hardware platform constraints, complementing the dedicated measurement-overhead analysis; and a critical synthesis of the empirical literature that finds parity or task-conditional modest gains rather than uniform quantum advantage across the five papers reporting parameter-matched classical baselines. Six open directions are identified for the next phase of research. The survey clarifies what the current literature does and does not establish about quantum attention’s practical advantage over efficient classical alternatives.
L. Sithamparanathan, Sathish A. P. Kumar· IEEE Access· 0 citations
Accurate noise models are essential for high-performance quantum error correction, yet characterizing the noise of a quantum device typically requires dedicated experiments. We present a variational framework that learns the noise model directly from quantum-error-correction syndrome and logical-observable data collected during error-corrected memory experiments. The fault-event probabilities are treated as variational parameters and optimized via gradient descent to minimize the binary cross-entropy between the decoder's predictions and experimental logical-observable outcomes. We prove that this objective is principled rather than ad hoc: a sufficiently expressive noise ansatz attains the information-theoretic minimum logical error rate. We instantiate this framework using a tensor-network decoder, which provides exact maximum-likelihood decoding and analytically differentiable gradients with respect to all noise parameters. Using circuit-level data from Google's Sycamore processor, and starting from an uninformed prior, the optimization recovers noise models whose logical error rates agree to within $2\%$ with those of Google's independently characterized detector error model. The mean squared error between the learned and reference noise parameters shows a clear overall decrease throughout training, confirming that the method recovers physically meaningful noise structure, not merely parameters that happen to decode well. We further demonstrate that the optimization can track device drifts in real time via warm-started updates, maintaining near-optimal decoding performance under synthetically evolving noise without the need for re-characterization. The approach is decoder-agnostic in its formulation and naturally extends to correlated noise models.
Nicola Pancotti, Vedika Saravanan, K. Svore· 0 citations
Quantum Phase Estimation (QPE) is a foundational algorithm for molecular ground-state energy estimation, but its deep circuit requirements make direct hardware execution impractical on Noisy Intermediate-Scale Quantum (NISQ) devices. We present an analytically grounded variational surrogate framework in which a shallow Variational Quantum Circuit (VQC) is trained to reproduce the QPE measurement distribution without any quantum circuit simulation. The training target is computed entirely classically via the Dirichlet kernel, evaluated directly from the Full Configuration Interaction (FCI) ground-state energy, the ancilla qubit count, and the time evolution parameter, eliminating the exponentially scaling simulation bottleneck of prior surrogate approaches. We apply this framework to the hydrogen molecule (H$_2$) with a symmetry-tapered Hamiltonian, conducting a four-stage experimental investigation on IBM Quantum hardware. Stage 1 compares linear and full entangler topologies for the $R_Y$-$R_Z$-$CZ$ ansatz, with and without XpXm Dynamical Decoupling (DD), across four distributional metrics (Hellinger distance, fidelity error, total variation distance, Jensen-Shannon divergence), identifying the linear entangler as optimal. Stage 2 varies VQC layers ($p=1$ to $5$) for the linear-entangler ansatz, identifying single-layer depth as optimal under hardware noise. Stage 3 applies this configuration to the reduced $R_Y$-$CZ$ ansatz, comparing ideal and noisy simulator-trained parameters. A supplementary noise analysis at $p \in \{8,64\}$ characterizes the depth-dependent interplay between circuit depth and DD effectiveness. The framework enables faithful QPE mimicry using a linearly scaling VQC, recovering the ground-state energy within the chemical accuracy threshold (1 kcal/mol), constituting a scalable, hardware-efficient paradigm for QPE-based molecular energy estimation on NISQ devices.
Mousumi Kundu, A. Patra, V. AnuragK.S. et al.· arXiv.org· 0 citations
A key challenge in practical quantum machine learning (QML), particularly for discriminative tasks such as classification, is the limited capacity of near-term quantum devices to encode high-dimensional classical data into small quantum registers. In optimized basis-encoded (bit-bit) settings, this constraint leads to cross-class collisions, where samples with different labels are mapped to the same discrete bit-string and thus become indistinguishable to any downstream model. In this work, we investigate how data representation affects QML performance under such severe information bottlenecks. We introduce discretization-aware fine-tuning (DAFT), a method that adapts a pre-trained chemical foundation model to produce representations that remain informative after quantization. DAFT reduces collision probability through a differentiable soft collision loss. We evaluate both quantum and classical models under a controlled setting in which they receive identical discretized bit-string inputs, isolating the effect of representation from model architecture. On the blood-brain barrier penetration (BBBP) molecular property prediction benchmark using ChemBERTa-77M, DAFT reduces collision counts by several orders of magnitude and improves quantum classification accuracy by more than 12 percentage points compared to a frozen backbone. Importantly, without DAFT, classical models outperform QML under the same input constraints. With DAFT, however, this comparison reverses at higher qubit counts. At 10 qubits, the quantum model surpasses a matched classical baseline trained on identical bit-strings (0.883 vs. 0.855, $p = 0.026$). These results show that, in information-constrained regimes, achieving a quantum advantage critically depends on aligning continuous representations with discrete quantum encodings.
Shunji Matsuura, S. Johri· 0 citations
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