Quantum Learning

Follow this topic Ask about this topic

Momentum

4 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

Jul 6Week of Sep 21

Latest papers 43

All topics

Classical Hardness of Learning Functions of Hamiltonians
Oct 1, 2026Sota Hashimoto, Akinori KawachiQuantum Learning Quantum Machine Learning
Graduate School of Engineering, Mie University
Advantage of Sample Complexity in Quantum PAC Learning Requires Inverse Access to State-Preparation Unitaries
Sep 29, 2026Natsuto Isogai, Satoshi Yoshida, Mio MuraoQuantum Learning Quantum Machine Learning
Department of Physics, Graduate School of Science, The University of Tokyo, Tokyo, Japan
Quantum score matching with applications to learning thermal states
Sep 23, 2026Yulong Dong, Jiaqi LengQuantum Learning Hamiltonian
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, USA · Institute for Advanced Computing, Virginia Tech, Alexandria, Virginia 22305, USA · Department of Computer Science, Virginia Tech, Alexandria, Virginia 22305, USA
When are bosonic Gaussian states classical to learn?
Sep 22, 2026Senrui Chen, Antonio Anna Mele, Francesco Anna Mele +1Quantum Learning Gaussian Primitives
Institute for Quantum Information and Matter, Caltech, Pasadena, CA 91125, USA · Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany
Weighted Quantum Signal Processing: Low-Depth Polynomial Approximation with Applications to Kolmogorov-Arnold Networks
Sep 18, 2026Rohit Sarma Sarkar, Rupayan Bhattacharjee, Elias F. Combarro +5Quantum Learning Kolmogorov-Arnold Networks
Universitat Polit`ecnica de Catalunya, Barcelona, Spain · Universidad de Oviedo, Spain · Quantum Computing Infrastructures and Algorithms Competence Centre, European Organization for Nuclear Research (CERN), Geneva 1211, Switzerland +2
Fourier Analysis of Parametrized Interactive Quantum Classifiers
Sep 16, 2026Fábio Novaes, Fernando M. de Paula Neto, João V. M. CardosoQuantum Learning Hamiltonian
Academic Unit of Belo Jardim, Federal Rural University of Pernambuco, Belo Jardim, Pernambuco 55156-580, Brazil · Center of Informatics, Federal University of Pernambuco, Recife, Pernambuco 50740-560, Brazil
Exponential quantum advantage for learning signals with a single qubit
Aug 13, 2026Ishaan Kannan, Sridhar Prabhu, Saeed A. Khan +7Quantum Computational Advantage Quantum Learning
Harvard Quantum Initiative, Harvard University, Cambridge, MA 02138, USA · School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA · Department of Physics, Cornell University, Ithaca, NY 14853, USA +2
A Quantum/Classical Example Oracle Separation for Making Things Up
Aug 12, 2026Kenny ChenQuantum Learning Quantum Processor
The University of Sydney
Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs
Aug 6, 2026Ning NingQuantum Learning Hidden Markov Models
Department of Statistics, Texas A&M University College Station, Texas, USA
Hypothesis Testing with Conditional Queries: Learnability and the Value of Interaction
Aug 6, 2026Zonghuan XuO(\Bar{K}\Log N)$Two-Sample Testing
Fudan University
Statistical Mechanics of Learning on Product Wasserstein Manifolds
Aug 2, 2026Srinivasa Rao P Vangmayi P ReddyQuantum Learning Wasserstein Distance
Curlvee Technolabs, India; Former Scientist, C-DAC.
Perspectives on Tsallis Statistics for Artificial Intelligence
Aug 2, 2026Kleyton da Costa, Bernardo ModenesiLong-Tailed Distribution Boltzmann Distributions
University College London, London, United Kingdom · Division of Biostatistics & School of Computing, University of Utah, United States
Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting
Jul 30, 2026Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +14Quantum Learning Kolmogorov-Arnold Networks
Department of Physics and Center for Theoretical Physics, National Taiwan University, Taipei, Taiwan · Wells Fargo, New York, NY, USA · NVIDIA AI Technology Center, NVIDIA Corp., Taipei, Taiwan +10
Interpreting Quantum Learning Models via Stochastic Processes
Jul 19, 2026Johannes Fankhauser, Lukas J. Fiderer, Hans J. BriegelQuantum Learning Stochastic Processes
University of Innsbruck, Institute for Theoretical Physics, Technikerstrasse 21a, A-6020 Innsbruck, Austria
When Routes Run Out: Adversarial Co-Learning and Explainable Robustness in Quantum Repeater Networks
Jul 10, 2026Brennan Bell, Inti Gabriel Mendoza Estrada, Andreas Trügler +1Quantum Learning Entanglement
RFI-IRFOS and TU Graz, Graz, Austria · openmaind FlexCo and TU Graz, Graz, Austria · Know Center Research GmbH and University of Graz, Graz, Austria +1
Learning $\mathsf{AC}^0$ under Locally Sampleable Graphical Models
Jul 9, 2026Weiming Feng, Xiongxin Yang, Yixiao Yu +1Learnability Quantum Learning
School of Computing and Data Science, The University of Hong Kong. · Department of Computer Science, University of California, Santa Barbara. · State Key Laboratory for Novel Software Technology, New Cornerstone Science Laboratory, Nanjing University.
A Quantum Reservoir Architecture for Chaotic Forecasting and a Test of Whether Its High Dimension Helps
Jul 8, 2026Tushar PandeyReservoir Computing Quantum Learning
Texas A&M University, College Station, TX, USA
Provable learning separation for predicting time-evolution of quantum many-body systems
Jul 7, 2026Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert +2Quantum Learning Quantum Machine Learning
⟨aQaL⟩, Applied Quantum Algorithms, Leiden University, The Netherlands · Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands · Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany +2
Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates
Jul 2, 2026Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin +8Quantum Learning Recurrent Model
National Taiwan University, Taiwan · Stevens Institute of Technology, NJ, USA · Seoul National University, Korea +3
Learning the structure of open quantum systems
Jun 29, 2026Laura Lewis, Ewin Tang, John WrightQuantum Learning Qubit
UC Berkeley
Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
Jun 22, 2026Samuel Yen-Chi Chen, Yifeng Peng, Jiun-Cheng Jiang +8Quantum Learning Variational Quantum Circuits
Wells Fargo · Stevens Institute of Technology · National Taiwan University +3
Quantum-Inspired Contextual Learning for Sparse-Ring Fraud Detection in Dynamic Transaction Graphs
Jun 22, 2026Behnam Tonekaboni, Hiroshi YamauchiFraud Detection Graph Representation Learning
1Infleqtion Australia, Melbourne · 2SoftBank Corp., Japan, Tokyo
Quantum ring all-reduce: communication and privacy advantages for distributed learning
Jun 18, 2026María Gragera Garcés, Lirandë PiraQuantum Learning Post-Quantum Cryptography
University of Edinburgh, Edinburgh, United Kingdom · Centre for Quantum Technologies, National University of Singapore, Singapore
Optimal Ansatz-free Hamiltonian Learning In Situ
Jun 17, 2026Taiqi Zhou, Weiyuan GongQuantum Learning Qubit
Department of Information Engineering, The Chinese University of Hong Kong. · John A. Paulson School of Engineering and Applied Sciences, Harvard University. · California Institute of Technology
Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning
Jun 10, 2026Jeongho Bang, Kyoungho Cho, Jeongwoo JaeQuantum Learning Quantum Machine Learning
Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul 03722, Republic of Korea · Department of Quantum Information, Yonsei University, Incheon 21983, Republic of Korea · Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea +1
Perturbative Contrastive Physical Learning
Jun 8, 2026Kyungeun Kim, Amanuel Anteneh, Israel Klich +2Contrastive Learning Quantum Learning
Department of Mathematics, University of British Columbia, Vancouver, BC Canada · Department of Physics, University of Virginia, 382 McCormick Rd, Charlottesville, VA 22903, USA · Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany +2
QnRL: Quantum-Native Reinforcement Learning
Jun 6, 2026Alexander DeRieux, Walid SaadQuantum Learning Q-Learning
Bradley Department of Electrical and Computer Engineering, Virginia Tech Institute for Advanced Computing, Alexandria, VA 22305 USA
Repair Before Veto, When Repair Is Hidden: Quantum-Accessible Features for Repair-Augmented Constraint Learning
Jun 6, 2026Yifan WangQuantum Learning Quantum Error Correction
Department of Mechanical Engineering, McGill University, QC, H3A 2T7, Canada
Graph Transfer Learning via Shared Latent Geometry: Theory and Applications
May 30, 2026Tong Wu, Andrew Campbell, Anna ScaglioneDiscriminative Congruence Transform Physics-Informed Learning
University of Central Florida, USA · Cornell University, USA
Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design
May 21, 2026Atit Pokharel, Shaba Shaon, Thomas Morris +1Post-Quantum Cryptography Quantum Learning
Department of Electrical and Computer Engineering, University of Alabama in Huntsville, USA
Supervised Latent Restructuring for Small-Data Quantum Learning in Plant Phenomics
May 19, 2026Alakananda Mitra, David H. Fleisher, Vangimalla Reddy +1Quantum Kernels Quantum Learning
Nebraska Water Center, IANR University of Nebraska–Lincoln Lincoln, NE 68588 · Adaptive Cropping Systems Laboratory USDA-ARS Beltsville, MD 20705 · Nebraska Water Center, DWFI University of Nebraska–Lincoln Lincoln, NE 68588
Iterative Chow Filtering for Learning with Distribution Shift
May 17, 2026Gautam Chandrasekaran, Georgios Gkrinias, Adam R. Klivans +2O(\Bar{K}\Log N)$Distribution Shifts
UT Austin
What is Learnable in Valiant's Theory of the Learnable?
May 13, 2026Steve Hanneke, Anay Mehrotra, Grigoris Velegkas +1Learnability O(\Bar{K}\Log N)$
Purdue University · Stanford University · Google Research +1
Quantum End-to-End Learning for Contextual Combinatorial Optimization
May 13, 2026Jaehwan Lee, Changhyun KwonQuantum Learning Quantum Approximate Optimization Algorithm
1KAIST · 2Omelet
Quantum Transfer Learning Shows Improved Robustness in Low-Data Regimes
May 9, 2026Li-An Lo, Li-Yi Hsu, Hsien-Yi HsiehQuantum Learning Transfer Learning
Department of Engineering Science, National Cheng Kung University, Tainan 701401, Taiwan · Department of Physics, Chung Yuan Christian University, Chungli 32081, Taiwan · Physics Division, National Center for Theoretical Sciences, Taipei 106319, Taiwan +1
Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning
May 7, 2026Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +16Quantum Learning Kolmogorov-Arnold Networks
Department of Physics and Center for Theoretical Physics, National Taiwan University, Taipei, Taiwan · National Center for High-Performance Computing, National Institutes of Applied Research, Hsinchu, Taiwan · Wells Fargo, New York, NY, USA +11
Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
May 5, 2026Abhishek Sadhu, Sharu Theresa JoseVariational Quantum Algorithms Quantum Learning
School of Computer Science, University of Birmingham · Edgbaston, Birmingham B15 2TT, United Kingdom
Provable and scalable quantum Gaussian processes for quantum learning
Apr 30, 2026Jonas Jäger, Paolo Braccia, Pablo Bermejo +3Quantum Learning Gaussian Process
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA · Department of Computer Science and Institute of Applied Mathematics, University of British Columbia, Vancouver, V6T 1Z4 B.C., Canada · Stewart Blusson Quantum Matter Institute, Vancouver, V6T 1Z4 B.C., Canada +8
Cloning is as Hard as Learning for Stabilizer States
Apr 16, 2026Nikhil Bansal, Matthias C. Caro, Gaurav MahajanQuantum Learning Qubit
Department of Computer Science, University of Warwick, Coventry, UK · Department of Computer Science, Yale University, Connecticut, USA
Instance-optimal high-precision shadow tomography with few-copy measurements: A metrological approach
Feb 4, 2026Senrui Chen, Weiyuan Gong, Sisi ZhouOptimal Sample Complexity Sample Complexity
IQIM, California Institute of Technology · SEAS, Harvard University · Perimeter Institute
Learning thermodynamic master equations for open quantum systems
Jun 2, 2025Peter Sentz, Stanley Nicholson, Yujin Cho +3Quantum Learning Hamiltonian
Department of Data and Decision Sciences, Emory University, Atlanta, GA 30322 · 2Division of Applied Mathematics, Brown University, Providence, RI 02912 · 3Lawrence Livermore National Laboratory, Livermore, CA 94550
Interactive proofs for verifying (quantum) learning and testing
Oct 31, 2024Matthias C. Caro, Jens Eisert, Marcel Hinsche +3Quantum Learning Post-Quantum Cryptography
Department of Computer Science, University of Warwick, UK · Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, Berlin, Germany · Helmholtz-Zentrum Berlin für Materialien und Energie, Berlin, Germany +4
Spectral Geometry and Bosonic-Bloch Probes: Explorations in Quantum Learning
Date pendingSantanu Ganguly, Xing Liang, Dimitrios MakrisQuantum Learning Fisher Information Matrix
Quantum AI Research Group, School of Computer Science and Mathematics, Kingston University London, Penrhyn Road, Kingston upon Thames, KT1 2EE, United Kingdom

Oct 1, 2026, quant-phJ/K move · Enter open · S save

Classical Hardness of Learning Functions of Hamiltonians

Sota Hashimoto, Akinori Kawachi

Graduate School of Engineering, Mie University

Quantum Learning Quantum Machine Learning Np-Hard Hamiltonian Hard Hessian+1

Open paper AskPDF arXiv

Morohoshi, Nakayama, Manabe, and Mitarai proposed a physically motivated quantum machine learning problem in which the goal is to predict quantities of the form

\operatorname{Tr}[f(H)ρ]

from classical descriptions of a Hamiltonian

H

and a quantum state

ρ

, where

f

is an unknown function. We call this problem Hamiltonian function learning in this paper. They constructed an efficient quantum learning algorithm under suitable conditions, while leaving a rigorous proof of average-case classical hardness open. In this paper, we rigorously prove the average-case classical hardness for two distribution-specific Hamiltonian function learning problems for

f_{\cos,π}(λ)=\cos(πλ)

and

f_{\exp,β}(λ)=e^{-βλ}

discussed in the paper of Morohoshi et al. under the assumption of the average-case hardness of factoring random RSA moduli. More specifically, we show that an efficient classical randomized learner under squared loss whose output hypotheses are evaluable in classical polynomial time for either problem would yield a classical randomized polynomial-time algorithm for factoring random RSA moduli.

Momentum

Related topics

Latest papers 43