Quantum Machine Learning in Multi-Qubit Phase-Space Part I: Foundations

• URL: http://arxiv.org/abs/2507.12117v1
• Date: Wed, 16 Jul 2025 10:37:16 GMT
• Title: Quantum Machine Learning in Multi-Qubit Phase-Space Part I: Foundations
• Authors: Timothy Heightman, Edward Jiang, Ruth Mora-Soto, Maciej Lewenstein, Marcin Płodzień,
• Abstract summary: We construct a closed, composable dynamical formalism for one- and many-qubit systems in phase-space.<n>It recasts the curse of dimensionality in terms of harmonic support on a domain that scales linearly with the number of qubits.
• Score: 0.6116681488656472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum machine learning (QML) seeks to exploit the intrinsic properties of quantum mechanical systems, including superposition, coherence, and quantum entanglement for classical data processing. However, due to the exponential growth of the Hilbert space, QML faces practical limits in classical simulations with the state-vector representation of quantum system. On the other hand, phase-space methods offer an alternative by encoding quantum states as quasi-probability functions. Building on prior work in qubit phase-space and the Stratonovich-Weyl (SW) correspondence, we construct a closed, composable dynamical formalism for one- and many-qubit systems in phase-space. This formalism replaces the operator algebra of the Pauli group with function dynamics on symplectic manifolds, and recasts the curse of dimensionality in terms of harmonic support on a domain that scales linearly with the number of qubits. It opens a new route for QML based on variational modelling over phase-space.

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