Quantum Image Loading: Hierarchical Learning and Block-Amplitude Encoding

• URL: http://arxiv.org/abs/2504.10592v1
• Date: Mon, 14 Apr 2025 18:00:10 GMT
• Title: Quantum Image Loading: Hierarchical Learning and Block-Amplitude Encoding
• Authors: Hrant Gharibyan, Hovnatan Karapetyan, Tigran Sedrakyan, Pero Subasic, Vincent P. Su, Rudy H. Tanin, Hayk Tepanyan,
• Abstract summary: We extend the hierarchical learning framework to encode images into quantum states.<n>We successfully load digits from the MNIST dataset as well as road scenes from the Honda Scenes dataset.<n>We deploy our learned circuits on both IBM and Quantinuum hardware and find that these loading circuits are sufficiently shallow to fit within existing noise rates.
• Score: 0.5889536104474146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given the excitement for the potential of quantum computing for machine learning methods, a natural subproblem is how to load classical data into a quantum state. Leveraging insights from [GST24] where certain qubits play an outsized role in the amplitude encoding, we extend the hierarchical learning framework to encode images into quantum states. We successfully load digits from the MNIST dataset as well as road scenes from the Honda Scenes dataset. Additionally, we consider the use of block amplitude encoding, where different parts of the image are encoded in a tensor product of smaller states. The simulations and overall orchestration of workflows was done on the BlueQubit platform. Finally, we deploy our learned circuits on both IBM and Quantinuum hardware and find that these loading circuits are sufficiently shallow to fit within existing noise rates.

Related papers

• Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding [33.951713386684425]
  TNQE is a circuit-efficient quantum data encoding framework built on structured unitary tensor network representations.<n> TNQE compiles the resulting tensor cores into an encoding circuit through two complementary core-to-circuit strategies.<n>Across a range of benchmarks, TNQE achieves encoding circuits as shallow as $0.04times$ the depth of amplitude encoding.
  arXiv  Detail & Related papers  (2026-02-18T08:36:07Z)
• AQER: a scalable and efficient data loader for digital quantum computers [62.40228216126285]
  We develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states.<n>We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits.
  arXiv  Detail & Related papers  (2026-02-02T14:39:42Z)
• Typical Machine Learning Datasets as Low-Depth Quantum Circuits [0.40329768057075643]
  We develop an efficient algorithm for finding low-depth quantum circuits to load classical image data as quantum states.<n>We conduct systematic studies on the MNIST, Fashion-MNIST, CIFAR-10, and Imagenette datasets.
  arXiv  Detail & Related papers  (2025-05-06T10:27:51Z)
• Quantum Image Classification: Experiments on Utility-Scale Quantum Computers [0.5889536104474146]
  We perform image classification on Quantinuum's H-2 and IBM's Heron chips utilizing up to 72 qubits and thousands of two-qubit gates. For data loading, we extend the hierarchical learning to the task of approximate amplitude encoding and block amplitude encoding for commercially relevant images up to 2 million pixels.
  arXiv  Detail & Related papers  (2025-04-14T18:00:13Z)
• An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
  Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.<n>We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.<n>We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
  arXiv  Detail & Related papers  (2025-04-13T11:49:53Z)
• Tensor Network Based Efficient Quantum Data Loading of Images [0.0]
  We present a novel method for creating quantum states that approximately encode images as amplitudes. We experimentally demonstrate our technique on 8 qubits of a trapped ion quantum computer for complex images of road scenes.
  arXiv  Detail & Related papers  (2023-10-09T17:40:41Z)
• Data is often loadable in short depth: Quantum circuits from tensor networks for finance, images, fluids, and proteins [0.0]
  We introduce a circuit compilation method based on tensor network (TN) theory. We perform numerical experiments on real-world classical data from four distinct areas. This is the broadest numerical analysis to date of loading classical data into a quantum computer.
  arXiv  Detail & Related papers  (2023-09-22T18:00:01Z)
• Hybrid quantum transfer learning for crack image classification on NISQ hardware [62.997667081978825]
  We present an application of quantum transfer learning for detecting cracks in gray value images. We compare the performance and training time of PennyLane's standard qubits with IBM's qasm_simulator and real backends.
  arXiv  Detail & Related papers  (2023-07-31T14:45:29Z)
• Simulation of Entanglement Generation between Absorptive Quantum Memories [56.24769206561207]
  We use the open-source Simulator of QUantum Network Communication (SeQUeNCe), developed by our team, to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. We realize the representation of photonic quantum states within truncated Fock spaces in SeQUeNCe. We observe varying fidelity with SPDC source mean photon number, and varying entanglement generation rate with both mean photon number and memory mode number.
  arXiv  Detail & Related papers  (2022-12-17T05:51:17Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• A hybrid quantum image edge detector for the NISQ era [62.997667081978825]
  We propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era.
  arXiv  Detail & Related papers  (2022-03-22T22:02:09Z)
• Improved FRQI on superconducting processors and its restrictions in the NISQ era [62.997667081978825]
  We study the feasibility of the Flexible Representation of Quantum Images (FRQI) We also check experimentally what is the limit in the current noisy intermediate-scale quantum era. We propose a method for simplifying the circuits needed for the FRQI.
  arXiv  Detail & Related papers  (2021-10-29T10:42:43Z)
• Dense Coding with Locality Restriction for Decoder: Quantum Encoders vs. Super-Quantum Encoders [67.12391801199688]
  We investigate dense coding by imposing various locality restrictions to our decoder. In this task, the sender Alice and the receiver Bob share an entangled state.
  arXiv  Detail & Related papers  (2021-09-26T07:29:54Z)
• Supervised Learning Using a Dressed Quantum Network with "Super Compressed Encoding": Algorithm and Quantum-Hardware-Based Implementation [7.599675376503671]
  Implementation of variational Quantum Machine Learning (QML) algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices has issues related to the high number of qubits needed and the noise associated with multi-qubit gates. We propose a variational QML algorithm using a dressed quantum network to address these issues. Unlike in most other existing QML algorithms, our quantum circuit consists only of single-qubit gates, making it robust against noise.
  arXiv  Detail & Related papers  (2020-07-20T16:29:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.