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Quixer: A Quantum Transformer Model
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Progress in the realisation of reliable large-scale quantum computers has motivated research into the design of quantum machine learning models. We present Quixer: a novel quantum transformer model which utilises the Linear Combination of Unitaries and Quantum Singular Value Transform primitives as building blocks. Quixer operates by preparing a superposition of tokens and applying a trainable non-linear transformation to this mix. We present the first results for a quantum transformer model applied to a practical language modelling task, obtaining results competitive with an equivalent classical baseline. In addition, we include resource estimates for evaluating the model on quantum hardware, and provide an open-source implementation for classical simulation. We conclude by highlighting the generality of Quixer, showing that its parameterised components can be substituted with fixed structures to yield new classes of quantum transformers.
Forward citations
Cited by 5 Pith papers
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Trainability of Parametrised Linear Combinations of Unitaries
Sums of trainable parametrised circuits remain trainable, with explicit variance formulas for LCU states under Haar-random assumptions.
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Stacking the Deck: Tunable Trainability in Stacked LCUs
Stacked LCUs of fermionic Gaussian unitaries give variance Ω(1/(n k^{3l})) against classical simulation O(k^{2l} n^3) and quantum gate count O(l k n^2), with layers l as the single dial.
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Universal Quantum Transformer
UQT on 5 qubits achieves exact deterministic learning of Z_11 modular arithmetic and S_4 non-Abelian algebra via quantum-native mechanisms, claiming to bypass classical attention limits and run on NISQ hardware.
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Quantum Phase Recognition via Quantum Attention Mechanism
Swap-test measurements of qubit-pair correlations plus a classical network classify cluster-Ising ground states into AFM, SPT, and paramagnetic phases with high accuracy from small training sets.
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Quantum Agents
A definition and maturity model for quantum agents are proposed, with three small quantum-circuit prototypes illustrating Grover search, variational bandits, and adaptive image encryption.
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