Shallow-circuit Supervised Learning on a Quantum Processor

• URL: http://arxiv.org/abs/2601.03235v1
• Date: Tue, 06 Jan 2026 18:26:53 GMT
• Title: Shallow-circuit Supervised Learning on a Quantum Processor
• Authors: Luca Candelori, Swarnadeep Majumder, Antonio Mezzacapo, Javier Robledo Moreno, Kharen Musaelian, Santhanam Nagarajan, Sunil Pinnamaneni, Kunal Sharma, Dario Villani,
• Abstract summary: We show that one can overcome fundamental obstacles by using a linear Hamiltonian-based machine learning method.<n>We demonstrate the efficacy and scalability of the methods by performing experiments on benchmark datasets using up to 50 qubits of an IBM Heron quantum processor.
• Score: 0.3276917305778521
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Quantum computing has long promised transformative advances in data analysis, yet practical quantum machine learning has remained elusive due to fundamental obstacles such as a steep quantum cost for the loading of classical data and poor trainability of many quantum machine learning algorithms designed for near-term quantum hardware. In this work, we show that one can overcome these obstacles by using a linear Hamiltonian-based machine learning method which provides a compact quantum representation of classical data via ground state problems for k-local Hamiltonians. We use the recent sample-based Krylov quantum diagonalization method to compute low-energy states of the data Hamiltonians, whose parameters are trained to express classical datasets through local gradients. We demonstrate the efficacy and scalability of the methods by performing experiments on benchmark datasets using up to 50 qubits of an IBM Heron quantum processor.

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