quant-ph · 2026-08-20 · No. 90

Quantum Physics, 2026-08-20.

4 new papers in quant-ph. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

4 entries
  1. 01

    Bernstein-Vazirani Networks: Quantum Machine Learning by Interference

    Natacha Kuete Meli, Tolga Birdal, Prayag Tiwari, Vladislav Golyanik, Michael Moeller

    quant-ph · cs.AI · cs.CV · cs.LG

    We introduce Bernstein-Vazirani Networks (BVNs), a non-variational quantum machine learning framework that leverages quantum interference for supervised learning, demonstrated on vision and representation learning tasks. In their standard form, BVNs follow the principle of quantum Fourier sampling: labelled data are placed in superposition and interfered in the Fourier basis to extract globally informative features. We then define generalised...

    arxiv.org/abs/2608.19043 · PDF

  2. 02

    AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL

    Daniele Lizzio Bosco, Jacopo Cossio, Carla Piazza, Giuseppe Serra

    quant-ph · cs.AI

    Clifford circuits play a foundational role in quantum computing, particularly due to their importance in quantum error correction and fault-tolerant logical synthesis. While these circuits can be efficiently simulated and represented as symplectic matrices, standard synthesis methods-such as the Aaronson-Gottesman algorithm-often yield sub-optimal circuits with excessively high gate counts. In this work, we introduce AlphaClifford, a...

    arxiv.org/abs/2608.18946 · PDF

  3. 03

    Quantum Tensor Network Learning with DMRG

    Gustav J L Jäger, Martin B Plenio, Hans-Martin Rieser

    quant-ph · cs.LG

    Tensor Networks are a relatively new machine learning approach. The architectures proposed initially are inspired by approaches from quantum many-body physics simulations. One common layout is the matrix product state (MPS) also known as a tensor train optimized with gradient descent techniques. We introduce a global normalization condition, so that the MPS represents a quantum state. We investigate two optimization methods that find the...

    arxiv.org/abs/2608.18901 · PDF

  4. 04

    Quantum-Logic Tsetlin Machines: Interpretable Quantum Machine Learning with Commuting Projector Clauses

    Krishna Bhatia

    quant-ph · cs.LG · cs.LO

    Tsetlin Machines (TMs) learn interpretable Boolean clauses using finite-state automata. We introduce the Quantum-Logic Tsetlin Machine (QL-TM), which replaces Boolean literals with quantum propositions represented by projectors while retaining classical include/exclude automata. Clauses are restricted to commuting measurement contexts and activate through the Born probability of their joint projector. We prove an exact reduction to ordinary...

    arxiv.org/abs/2608.18659 · PDF

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