ArticleNature2017
Quantum machine learning.
Article in Nature, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 306 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
306 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The temporal trend of placebo response in migraine prevention from 1990 to 2021: a systematic literature review and meta-analysis with regression.The journal of headache and pain · 2023Pooled it
- Cervical cancer survival prediction by machine learning algorithms: a systematic review.BMC cancer · 2023Pooled it
- A unified framework for calibration, decoding and resource-aware processing in hybrid quantum-classical machine learning.Royal Society open science · 2026Article
- Quantum Computing for Polymeric Composites: Principles, Methods, Applications, and Future Perspectives.Polymers · 2026Review
- AI In Leukemia Diagnostics: Complementing the Pathologist's Role.International journal of laboratory hematology · 2026Review
- Anonymization of Portuguese Clinical Notes Using Large Language Models and Quantum-Enhanced Hybrid Architectures: Comparative Evaluation Study.Journal of medical Internet research · 2026Article
- Geometric and quantum kernel methods for predicting skeletal muscle outcomes in experimental chronic obstructive pulmonary disease.Scientific reports · 2026Article
- Proxy-Based Diagnostics of Quantum Oracle Sketching Robustness for Non-IID Sensor and Telemetry Streams.Sensors (Basel, Switzerland) · 2026Article
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Opportunities for Quantum Sensing in Epilepsy Diagnostics.Sensors (Basel, Switzerland) · 2026Review
- Kernel-Weighted Aggregation for Hyperparameter-Free Quantum Federated Learning.Entropy (Basel, Switzerland) · 2026Article
- Federated Quantum Machine Learning over Satellite Networks: Toward Scalable Distributed Quantum Classification.Entropy (Basel, Switzerland) · 2026Article
- Hardware-Aware Quantum Kernel Design Based on Graph Neural Networks.Entropy (Basel, Switzerland) · 2026Article
- H-QDCT: hierarchical quantum DCT for structural-textural feature fusion in medical imaging.International journal of computer assisted radiology and surgery · 2026Article
- Qubit-efficient quantum intrusion detection using dual-parameter encoding and multi-metric calibration.Scientific reports · 2026Article
- Quantum convolutional HLA immunogenic peptide prediction (Q-CHIPP): Next-generation neoantigen prediction with quantum neural network.Science advances · 2026Article
- Utility-Scale Quantum Computational Chemistry.The journal of physical chemistry letters · 2026Review
- SPA-QNAS: Improving Search Efficiency and Stability in Evolutionary Quantum Neural Architecture Search.Entropy (Basel, Switzerland) · 2026Article
- Platonic Projection Structures: Operator-Induced Observability in Representation Learning.Entropy (Basel, Switzerland) · 2026Article
- Multi-Filter Quantum Neural Networks for Efficient Channel Estimation in RIS-Assisted Systems.Sensors (Basel, Switzerland) · 2026Article
246 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.
Identifiers
28905917What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.