Efficient Quantum State Preparation with Bucket Brigade QRAM

• URL: http://arxiv.org/abs/2510.16149v1
• Date: Fri, 17 Oct 2025 18:50:07 GMT
• Title: Efficient Quantum State Preparation with Bucket Brigade QRAM
• Authors: Alessandro Berti, Francesco Ghisoni,
• Abstract summary: Preparation of data in quantum states is a critical component in the design of quantum algorithms.<n>One of the main approaches to achieve efficient state preparation is through the use of Quantum Random Access Memory (QRAM)<n>We present a framework that integrates the physical model of the Bucket Brigade QRAM (BBQRAM) with the classical data structure of the Segment Tree to achieve efficient state preparation.
• Score: 47.72095699729477
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The preparation of data in quantum states is a critical component in the design of quantum algorithms. The cost of this step can significantly limit the realization of quantum advantage in domains such as machine learning, finance, and chemistry. One of the main approaches to achieve efficient state preparation is through the use of Quantum Random Access Memory (QRAM), a theoretical device for coherent data access with several proposed physical implementations. In this work, we present a framework that integrates the physical model of the Bucket Brigade QRAM (BBQRAM) with the classical data structure of the Segment Tree to achieve efficient state preparation. We introduce a memory layout that embeds a segment tree within BBQRAM memory cells by preserving the segment tree's hierarchy and supporting data retrieval in logarithmic time via specialized access primitives. We demonstrate that, under the proposed memory layout, our method encodes a matrix $A \in \mathbb{R}^{M \times N}$ in a quantum register of $\Theta(\log_2(MN))$ qubits in $O(\log_2^2(MN))$ time using constant ancillary qubits under a fixed-precision assumption. We further illustrate the method through a numerical example. This framework provides theoretical support for quantum algorithms that assume negligible data loading overhead and establishes a foundation for designing classical-to-quantum encoding algorithms that are aware of the underlying physical QRAM architecture.

Related papers

• 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)
• Stab-QRAM: An All-Clifford Quantum Random Access Memory for Special Data [9.722458605511436]
  We introduce the Stabilizer-QRAM (Stab-QRAM), a domain-specific architecture tailored for data.<n>We show that Stab-QRAM achieves an optimal logical circuit depth of $O(log N)$ for $N$ data items, matching its $O(log N)$ space complexity.<n>This design completely circumvents the non-Clifford bottleneck, eliminating the need for costly magic state distillation.
  arXiv  Detail & Related papers  (2025-09-30T16:36:52Z)
• 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)
• 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)
• Fat-Tree QRAM: A High-Bandwidth Shared Quantum Random Access Memory for Parallel Queries [0.6976976250169952]
  We introduce Fat-Tree QRAM, a novel query architecture capable of pipelining multiple quantum queries simultaneously.<n>Fat-Tree QRAM performs $O(log (N))$ independent queries in $O(log (N))$ time using $O(N)$ qubits.
  arXiv  Detail & Related papers  (2025-02-10T18:47:16Z)
• Compilation of algorithm-specific graph states for quantum circuits [55.90903601048249]
  We present a quantum circuit compiler that prepares an algorithm-specific graph state from quantum circuits described in high level languages. The computation can then be implemented using a series of non-Pauli measurements on this graph state.
  arXiv  Detail & Related papers  (2022-09-15T14:52:31Z)
• Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach [47.19265172105025]
  We propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN) egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. The architecture is based on a novel mapping from real-world data to Hilbert space.
  arXiv  Detail & Related papers  (2022-01-13T16:35:45Z)
• Scalable and High-Fidelity Quantum Random Access Memory in Spin-Photon Networks [6.540771405203322]
  A quantum random access memory (qRAM) is considered an essential computing unit to enable speedups in quantum information processing. Here, we propose a photonic integrated circuit (PIC) architecture integrated with solid-state memories as a viable platform for constructing a qRAM. We also present an alternative scheme based on quantum teleportation and extend it to the context of quantum networks.
  arXiv  Detail & Related papers  (2021-03-13T05:39:03Z)
• Quantum Search for Scaled Hash Function Preimages [1.3299507495084417]
  We present the implementation of Grover's algorithm in a quantum simulator to perform a quantum search for preimages of two scaled hash functions. We show that strategies that suggest a shortcut based on sampling the quantum register after a few steps of Grover's algorithm can only provide some marginal practical advantage in terms of error mitigation.
  arXiv  Detail & Related papers  (2020-09-01T18:00:02Z)
• Quantum random access memory via quantum walk [0.0]
  A novel concept of quantum random access memory (qRAM) employing a quantum walk is provided. Our scheme is fully parallelized. Consequently, only O(n) steps are required to access and retrieve O(2n) data in the form of quantum superposition states.
  arXiv  Detail & Related papers  (2020-08-31T04:54:12Z)
• Quantum Gram-Schmidt Processes and Their Application to Efficient State Read-out for Quantum Algorithms [87.04438831673063]
  We present an efficient read-out protocol that yields the classical vector form of the generated state. Our protocol suits the case that the output state lies in the row space of the input matrix. One of our technical tools is an efficient quantum algorithm for performing the Gram-Schmidt orthonormal procedure.
  arXiv  Detail & Related papers  (2020-04-14T11:05:26Z)

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.