quant-ph · 2026-07-07 · No. 46

Quantum Physics, 2026-07-07.

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

01 — The papers

5 entries
  1. 01

    Quantum Spectral Anomaly Detection

    Yewei Yuan, Michele Minervini, Mark M. Wilde, Nana Liu

    quant-ph · cs.LG

    A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantum dataset assumed to be normal. Classically, principal component analysis (PCA) for centered data computes the anomaly score by evaluating the test sample relative to the subspace spanned by the selected leading eigenvectors. However, for quantum data that lack a standard centering, explicitly...

    arxiv.org/abs/2607.05307 · PDF

  2. 02

    Routing Anonymity and Identifiability of Noisy Quantum Hardware

    Ben Priestley, Mina Doosti

    quant-ph · cs.LG

    Present-day quantum computing is cloud-based, where a user submits a circuit to a service provider's proprietary backend hardware. While providers may wish to hide implementation details, scheduling choices, or even which physical device was used, noisy finite-shot outputs can carry backend-specific fingerprints: information imprinted in the classical output distribution that can reveal the backend identity. So far, such fingerprints have...

    arxiv.org/abs/2607.05281 · PDF

  3. 03

    Canonical quantization of neurons

    Alexander He, Nana Liu, Mark M. Wilde

    quant-ph · cond-mat.stat-mech · cs.LG

    Canonical quantization provides a systematic procedure for constructing quantum models from classical Hamiltonians. Here, we apply this principle to a fundamental computational primitive of machine learning: the neuron. Specifically, by viewing a neuron as a composition of an energy function and an activation function, we quantize this model by replacing the energy function with a quantum Hamiltonian and applying the activation function to it...

    arxiv.org/abs/2607.05000 · PDF

  4. 04

    HamQASBench: A Hamiltonian-Informed Diagnostic Benchmark for Evaluating Quantum Architecture Search

    Jiayang Niu, Akib Karim, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Usman, Yongli Ren

    quant-ph · cs.AI

    Quantum Architecture Search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms, yet existing benchmarks organize instances by molecular identity or qubit count -- criteria agnostic to Hamiltonian structure -- and rely solely on energy accuracy, which cannot detect structural failures such as over-parameterization on near-product ground states. We introduce HamQASBench, a Hamiltonian-informed...

    arxiv.org/abs/2607.04845 · PDF

  5. 05

    Breaking the One-Dimensional Expressibility-Trainability Tradeoff

    Kyoungho Cho, Yu-Seong Jeon, Jinhyoung Lee, Jeongho Bang

    quant-ph · cs.LG

    Expressive parameterized quantum circuits (PQCs) are often designed under a dilemma: the growth of expressibility and entangling power (EP) that improves Hilbert-space coverage is also expected to randomize an ansatz and activate barren-plateau (BP) conditions. We show that this dilemma is not a one-dimensional tradeoff. The usual picture collapses three inequivalent objects -- parameter-ensemble coverage, fixed-circuit entangling response,...

    arxiv.org/abs/2607.04598 · PDF

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