Re-uploading quantum data: A universal function approximator for quantum inputs

• URL: http://arxiv.org/abs/2509.18530v4
• Date: Mon, 20 Oct 2025 05:06:30 GMT
• Title: Re-uploading quantum data: A universal function approximator for quantum inputs
• Authors: Hyunho Cha, Daniel K. Park, Jungwoo Lee,
• Abstract summary: We analyze a quantum data re-uploading architecture in which a qubit interacts sequentially with fresh copies of an arbitrary input state.<n>Our framework provides a qubit-efficient and expressive approach to designing quantum machine learning models that operate directly on quantum data.
• Score: 5.268950041973641
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantum data re-uploading has proved powerful for classical inputs, where repeatedly encoding features into a small circuit yields universal function approximation. Extending this idea to quantum inputs remains underexplored, as the information contained in a quantum state is not directly accessible in classical form. We propose and analyze a quantum data re-uploading architecture in which a qubit interacts sequentially with fresh copies of an arbitrary input state. The circuit can approximate any bounded continuous function using only one ancilla qubit and single-qubit measurements. By alternating entangling unitaries with mid-circuit resets of the input register, the architecture realizes a discrete cascade of completely positive and trace-preserving maps, analogous to collision models in open quantum system dynamics. Our framework provides a qubit-efficient and expressive approach to designing quantum machine learning models that operate directly on quantum data.

Related papers

• Quantum Sequential Circuits [0.0]
  This work introduces and characterizes quantum sequential circuits (QSCs) as a hardware-oriented paradigm for quantum computing.<n>Unlike conventional qubit-based architectures, QSCs employ symmetry-protected topological junctions where quantum gates are encoded as Choi states via channel-state duality and activated through bulk measurements.
  arXiv  Detail & Related papers  (2026-02-05T00:33:07Z)
• 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)
• Non-perturbative switching rates in bistable open quantum systems: from driven Kerr oscillators to dissipative cat qubits [72.41778531863143]
  We use path integral techniques to predict the switching rate in a single-mode bistable open quantum system.<n>Our results open new avenues for exploring switching phenomena in multistable single- and many-body open quantum systems.
  arXiv  Detail & Related papers  (2025-07-24T18:01:36Z)
• Minimal Quantum Reservoirs with Hamiltonian Encoding [72.27323884094953]
  We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding.<n>This approach circumvents many of the experimental overheads typically associated with quantum machine learning.
  arXiv  Detail & Related papers  (2025-05-28T16:50:05Z)
• Capturing Quantum Snapshots from a Single Copy via Mid-Circuit Measurement and Dynamic Circuit [1.912429179274357]
  Quantum Snapshot with Dynamic Circuit (QSDC) is a hardware-agnostic, learning-driven framework for capturing quantum snapshots.<n>We introduce a guess-and-check methodology in which a classical model is trained to reconstruct an unknown quantum state.<n>Our approach supports single-copy, mid-circuit state reconstruction, assuming hardware with dynamic circuit support.
  arXiv  Detail & Related papers  (2025-04-30T01:18:21Z)
• Guess, SWAP, Repeat : Capturing Quantum Snapshots in Classical Memory [2.089191490381739]
  We introduce a novel technique that enables observation of quantum states without direct measurement, preserving them for reuse.<n>Our method allows multiple quantum states to be observed at different points within a single circuit, one at a time, and saved into classical memory without destruction.<n>We propose a hardware-agnostic, machine learning-driven framework to capture non-destructive estimates, or "snapshots," of quantum states at arbitrary points within a circuit.
  arXiv  Detail & Related papers  (2025-04-20T02:39:30Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
  Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Anticipative measurements in hybrid quantum-classical computation [68.8204255655161]
  We present an approach where the quantum computation is supplemented by a classical result. Taking advantage of its anticipation also leads to a new type of quantum measurements, which we call anticipative. In an anticipative quantum measurement the combination of the results from classical and quantum computations happens only in the end.
  arXiv  Detail & Related papers  (2022-09-12T15:47:44Z)
• Quantum-Classical Hybrid Information Processing via a Single Quantum System [1.1602089225841632]
  Current technologies in quantum-based communications bring a new integration of quantum data with classical data for hybrid processing. We propose a quantum reservoir processor to harness quantum dynamics in computational tasks requiring both classical and quantum inputs.
  arXiv  Detail & Related papers  (2022-09-01T14:33:40Z)
• An Amplitude-Based Implementation of the Unit Step Function on a Quantum Computer [0.0]
  We introduce an amplitude-based implementation for approximating non-linearity in the form of the unit step function on a quantum computer. We describe two distinct circuit types which receive their input either directly from a classical computer, or as a quantum state when embedded in a more advanced quantum algorithm.
  arXiv  Detail & Related papers  (2022-06-07T07:14:12Z)
• Trainable Discrete Feature Embeddings for Variational Quantum Classifier [4.40450723619303]
  We show how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC) We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning.
  arXiv  Detail & Related papers  (2021-06-17T12:02:01Z)
• Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
  We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
  arXiv  Detail & Related papers  (2021-01-21T22:18:49Z)

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.