mlsanityData to 7 Oct 2026

A divide-and-conquer algorithm for quantum state preparation

arXiv 2008.01511 ↗First posted 4 Aug 2020quant-ph cs.LGcited by 25

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Abstract (arXiv)

Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Aug 2020.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 8; 2024: 1; 2025: 6; 2026: 1.

25 papers.

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  1. 2022Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuitscited by 42
  2. 2022Classical-to-quantum convolutional neural network transfer learningcited by 18
  3. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  4. 2021Representation of binary classification trees with binary features by quantum circuitscited by 7
  5. 2022Variational Quantum Approximate Support Vector Machine with Inference Transfercited by 7
  6. 2022Near-Term Advances in Quantum Natural Language Processingcited by 6
  7. 2023Quantum Graph Learning: Frontiers and Outlookcited by 6
  8. 2021Automatically Differentiable Quantum Circuit for Many-qubit State Preparationcited by 4
  9. 2023A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discoverycited by 4
  10. 2023Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Datacited by 3
  11. 2023Spacetime-Efficient Low-Depth Quantum State Preparation with Applicationscited by 2
  12. 2023Data is often loadable in short depth: Quantum circuits from tensor networks for finance, images, fluids, and proteinscited by 2
13 more
  1. 2024Multivariate trace estimation using quantum state space linear algebracited by 2
  2. 2025Quantum State Preparation with the QNN-based SRBB Algorithmcited by 2
  3. 2025EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Datacited by 2
  4. 2025Benchmarking data encoding methods in Quantum Machine Learningcited by 2
  5. 2025Hybrid Quantum-Classical Learning for Multiclass Image Classificationcited by 2
  6. 2022Optimisation-free Classification and Density Estimation with Quantum Circuitscited by 1
  7. 2022An efficient combination strategy for hybird quantum ensemble classifiercited by 1
  8. 2023A Post-Training Approach for Mitigating Overfitting in Quantum Convolutional Neural Networkscited by 1
  9. 2021Investigating the usefulness of Quantum Blurcited by 0
  10. 2023Impact of Data Augmentation on QCNNscited by 0
  11. 2025On Encoding Matrices using Quantum Circuitscited by 0
  12. 2025Wavelet-Accelerated Physics-Informed Quantum Neural Network for Multiscale Partial Differential Equationscited by 0
  13. 2026Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networkscited by 0