Quantum Feature Maps

Also known as MAP

Quantum feature maps transform classical data into quantum states or measurement outcomes so learning algorithms can use the resulting geometry and feature interactions. Common approaches include angle, amplitude, basis, IQP and ZZ encodings, often combined with entangling gates, data re-uploading or trainable circuits; quantum kernels compare the encoded states, while hybrid models feed measured features into classical learners. Applications range from image, audio and tabular classification to regression and time-series prediction, where the aim is to capture useful structure with compact representations. Results are mixed: benefits depend on how the encoding matches the task and on the readout and classical baseline, while noise, qubit limits, kernel cost and fair hardware comparisons remain important challenges.

Latest papers

37 papers10 papers in the last four weeks, up 150% on the four weeks before. 0.1% of all new papers.

Recurrent Quantum Feature Maps for Reservoir Computing
Apr 3, 2026Utkarsh Singh, Aaron Z. Goldberg, Christoph Simon +1Quantum Reservoir Computing Quantum Feature Maps
Department of Physics, University of Ottawa, 25 Templeton Street, Ottawa, Ontario, K1N 6N5 Canada · National Research Council of Canada, 100 Sussex Drive, Ottawa, Ontario K1N 5A2, Canada · Institute for Quantum Science and Technology, Department of Physics and Astronomy, University of Calgary, Alberta T2N 1N4, Canada +1
Quantifying Gate Contribution in Quantum Feature Maps for Scalable Circuit Optimization
Mar 20, 2026F. Rodríguez-Díaz, D. Gutiérrez-Avilés, A. Troncoso +1Quantum Circuit Optimization Quantum Feature Maps
Data Science and Big Data Lab, Pablo de Olavide University, Seville, ES 41013, Spain · Department of Computer Science, University of Seville, Seville, ES 41012, Spain
From Reachability to Learnability: Geometric Design Principles for Quantum Neural Networks
Mar 3, 2026Vishal S. Ngairangbam, Michael SpannowskyQuantum Neural Networks Quantum Machine Learning
Institute for Particle Physics Phenomenology, Department of Physics Durham University, Durham DH1 3LE, United Kingdom · Institute for Theoretical Physics, Karlsruhe Institute of Technology (KIT), Wolfgang-Gaede-Str. 1, 76131 Karlsruhe, Germany
Spectral Phase Encoding for Quantum Kernel Methods
Feb 23, 2026Pablo Herrero Gómez, Antonio Jimeno Morenilla, David Muñoz-Hernández +1Quantum Kernel Methods Quantum Feature Maps
Department of Computer Science Technology and Computation, University of Alicante, Alicante, Spain
Quantum-enhanced satellite image classification
Feb 20, 2026Qi Zhang, Anton Simen, Carlos Flores-Garrigós +7Hybrid Quantum-Classical ML Remote Sensing Image Understanding
Kipu Quantum, Greifswalderstrasse 212, 10405 Berlin, Germany · Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain · IDAL, Electronic Engineering Department, ETSE-UV, University of Valencia, Avgda. Universitat s/n, 46100 Burjassot, Valencia, Spain +2
A Versatile Variational Quantum Kernel Framework for Non-Trivial Classification
Nov 13, 2025Jiang Yuhan, Matthew OttenClassification Quantum Kernel Methods
Department of Physics, University of Wisconsin–Madison, Madison, WI 53706, USA · Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53706, USA
Metric Learning with Quantum Feature Spaces
Dec 4, 2023Vinayak Sharma, Lord Sen, Ashish Padhy +4Quantum Machine Learning Metric Learning
School of Computing and Augmented Intelligence Arizona State University, Tempe, AZ 85281 · National Institute of Technology, Rourkela Sector 1, Rourkela, Odisha - 769008

Apr 3, 2026J/K move · Enter open · S save

Recurrent Quantum Feature Maps for Reservoir Computing

Utkarsh Singh, Aaron Z. Goldberg, Christoph Simon, Khabat Heshami

Department of Physics, University of Ottawa, 25 Templeton Street, Ottawa, Ontario, K1N 6N5 Canada · National Research Council of Canada, 100 Sussex Drive, Ottawa, Ontario K1N 5A2, Canada · Institute for Quantum Science and Technology, Department of Physics and Astronomy, University of Calgary, Alberta T2N 1N4, Canada+1

Quantum Reservoir Computing Quantum Feature Maps Chaotic Time-Series Prediction

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Reservoir computing promises a fast method for handling large amounts of temporal data. This hinges on constructing a good reservoir--a dynamical system capable of transforming inputs into a high-dimensional representation while remembering properties of earlier data. In this work, we introduce a reservoir based on recurrent quantum feature maps where a fixed quantum circuit is reused to encode both current inputs and a classical feedback signal derived from previous outputs. We evaluate the model on the Mackey-Glass time-series prediction task using our recently introduced CP feature map, and find that it achieves lower mean squared error than standard classical baselines, including echo state networks and multilayer perceptrons, while maintaining compact circuit depth and qubit requirements. We further analyze memory capacity and show that the model effectively retains temporal information, consistent with its forecasting accuracy. Finally, we study the impact of realistic noise and find that performance is robust to several noise channels but remains sensitive to two-qubit gate errors, identifying a key limitation for near-term implementations.