Quantum random access memory (qRAM) is crucial for overcoming data-loading bottlenecks in quantum machine learning; however, current physical implementations face severe scalability constraints. Traditional fanout designs demand exponential decoherence-prone gates, while bucket-brigade schemes require highly error-prone active switches. Motivated by these limitations, we propose a scalable qRAM architecture that fundamentally replaces active nodes with phase-encoded quantum walkers. Our methodology maps a discrete-time quantum walk onto a cavity quantum electrodynamics framework utilizing an electromagnetically induced transparency (EIT)-based Rydberg atomic ensemble. Inside hollow-core waveguides, strong Rydberg dipole-dipole interactions and a solenoidal magnetic field create a robust routing operator. This operator imparts precise, polarization-dependent phase shifts, steering circularly polarized probe pulses to target memory cells. Our results demonstrate that operating within a strong control field regime suppresses emergent spatial attenuation, ensuring cumulative transmission probabilities for highly scaled memory addresses. Ultimately, this parallelized architecture successfully optimizes spatial resources to static gates and temporal complexity to an optimal logarithmic scale of $\mathcal{O}(n\log(n+m))$ by requiring $\mathcal{O}(n+m)$ physical walkers, establishing a practical, fault-tolerant hardware pathway for advanced quantum computation implementations.
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BibTeX, Version 0.99d (MiKTeX 25.4), 16 pages with 8 figures, submitted to Journal of Physics B
Quantum random access memory (qRAM) is crucial for overcoming data-loading bottlenecks in quantum machine learning; however, current physical implementations face severe scalability constraints. Traditional fanout designs demand exponential decoherence-prone gates, while bucket-brigade schemes require highly error-prone active switches. Motivated by these limitations, we propose a scalable qRAM architecture that fundamentally replaces active nodes with phase-encoded quantum walkers. Our methodology maps a discrete-time quantum walk onto a cavity quantum electrodynamics framework utilizing an electromagnetically induced transparency (EIT)-based Rydberg atomic ensemble. Inside hollow-core waveguides, strong Rydberg dipole-dipole interactions and a solenoidal magnetic field create a robust routing operator. This operator imparts precise, polarization-dependent phase shifts, steering circularly polarized probe pulses to target memory cells. Our results demonstrate that operating within a strong control field regime suppresses emergent spatial attenuation, ensuring cumulative transmission probabilities for highly scaled memory addresses. Ultimately, this parallelized architecture successfully optimizes spatial resources to static gates and temporal complexity to an optimal logarithmic scale of O(nlog(n+m)) by requiring O(n+m) physical walkers, establishing a practical, fault-tolerant hardware pathway for advanced quantum computation implementations.
We propose a novel theoretical architecture for implementing a quantum random access memory (qRAM) based on quantum random walks in a Rydberg blockaded atomic ensemble utilizing multilevel Electromagnetically Induced Transparency (EIT). Unlike previous approaches that rely on geometric phase gates in solid-state or trapped ion systems, our scheme harnesses the strong, coherent dipole dipole interactions between Rydberg atoms to achieve high-fidelity phase control of photonic qubits without the need for cryogenic temperatures. By generating conditional phase shifts through cross-phase modulation in multiple lambda-type EIT systems, we realize the controlled unitary operations requisite for an efficient qRAM. In the proposed architecture, Zeeman splitting is used to engineer a set of parallel lambda systems in a cavity, where pairs of magnetic sublevels of the ground state are coupled to highly excited Rydberg states via circularly polarized laser pulses. These Rydberg excited EIT systems serve as the elementary phase gates that form the nodes of a binary tree enabling quantum random walking. Address and data qubits are encoded into distinct probe fields and coherently mapped into the metastable atomic states, where their interactions within the EIT medium generate conditional phases required for state-selective routing. The system uses n+m layers of cold alkali atoms to form an n-level binary tree of Rydberg nodes connected to 2n cavity-trapped memory atoms, operated by n+m laser pulses acting as quantum walkers and address units. Our scheme offers a scalable, reducing operational complexity to O(n) and highly coherent pathway toward photonic qRAM, exploiting collective Rydberg interactions to realize programmable, parallel entangling operations in an atomic ensemble.
Quantum Random Access Memory (qRAM) is a critical component for achieving quantum advantage in algorithms ranging from database search to quantum machine learning. In a recently introduced model [arXiv:2508.02855], we proposed a resource-efficient qRAM architecture based on discrete-time quantum walkers. This article serves as a comprehensive technical follow-up, providing the full mathematical derivations, detailed protocol specifications, and in-depth resource analysis. Moreover, we extend the original proposal with novel techniques for the purpose of making the qRAM implementation more realistic. Our model resolves the primary drawbacks of leading qRAM proposals: it avoids the exponential number of active nodes O(2n) required by the "Bucket Brigade" architecture by employing a number of quantum walkers that scales linearly with the address and message sizes, n and m, respectively. Simultaneously, it overcomes the spatial bottlenecks of previous quantum-walker schemes by eliminating the need for multiple parallel trees. We propose two algorithmic paradigms: the long- and the short-range approaches, which differ by the length of interaction of the main routing gates employed in the qRAM. We formalize the routing, message-copy, and walker retrieval phases for both variants and we show how, while the long-range scheme employs controlled gates having a multitude of target systems, the short-range approach decomposes these interactions into sequences of 2- and 3-body local gates, which improves the architecture's feasibility for near-term experimental implementation. Finally, our comprehensive resource analysis confirms that the short-range approach achieves the optimal O(n+m) circuit depth. Within these two paradigms, we explore the potential of different types of quantum walkers, namely bosons, dual-rail qubits and four-level qudits.
The burgeoning field of machine learning (ML) is increasingly reliant on massive datasets for training complex models, particularly deep neural networks. However, the retrieval of data from conventional memory systems represents a significant bottleneck, limiting the scalability and efficiency of ML algorithms. This research proposes a Quantum Random Access Memory (QRAM) architecture designed to overcome this limitation by leveraging quantum mechanical principles. The core concept involves encoding data into qubits and utilizing quantum random access to achieve near-instantaneous data retrieval. This paper details a novel QRAM architecture, focusing on qubit arrangement, control mechanisms, and strategies for minimizing decoherence – a primary challenge in quantum computing. The theoretical framework explores the potential acceleration achievable through QRAM in deep neural network training, with the primary goal of dramatically reducing training times. We present a detailed mathematical model outlining the data encoding process, quantum access protocols, and the expected performance gains. The architecture incorporates error mitigation techniques and discusses the scalability of the design for increasingly complex datasets. The research highlights the transformative potential of QRAM in accelerating ML, addressing a fundamental constraint within the current landscape of computational learning.
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 hardware implementations. In this work, we present a framework that integrates the hardware 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 our method encodes a matrix A∈RM×N in a quantum register of Θ(log2(MN)) qubits in O(log22(MN)) time, {requiring a constant number of working qubits (under fixed precision) and O(MN) memory cells within the BBQRAM architecture.} 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 hardware QRAM architecture.
Abstract A novel concept of quantum random access memory (qRAM) employing a quantum walk is provided. Our qRAM relies on a bucket brigade scheme to access the memory cells. Introducing a bucket with chirality left and right as a quantum walker, and considering its quantum motion on a full binary tree, we can efficiently deliver the bucket to the designated memory cells, and fill the bucket with the desired information in the form of quantum superposition states. Our procedure has several advantages. First, we do not need to place any quantum devices at the nodes of the binary tree, and hence in our qRAM architecture, the cost to maintain the coherence can be significantly reduced. Second, our scheme is fully parallelized. Consequently, only O(n) steps are required to access and retrieve O(2 n ) data in the form of quantum superposition states. Finally, the simplicity of our procedure may allow the design of qRAM with simpler structures.
Quantum random access memory (QRAM)—memory which stores classical data but allows queries to be performed in superposition—is required for the implementation of numerous quantum algorithms. While naive implementations of QRAM are highly susceptible to decoherence and hence not scalable, it has been argued that the bucket-brigade QRAM architecture [Giovannetti et al., Phys. Rev. Lett. 100, 160501 (2008)] is highly resilient to noise, with the infidelity of a query scaling only logarithmically with the memory size. In prior analyses, however, this favorable scaling followed directly from the use of contrived noise models, thus leaving open the question of whether experimental implementations would actually enjoy the purported scaling advantage. In this work, we study the effects of decoherence on QRAM in full generality. Our main result is a proof that this favorable infidelity scaling holds for arbitrary error channels (including, e.g., depolarizing noise and coherent errors). Our proof identifies the origin of this noise resilience as the limited entanglement among the memory’s components, and it also reveals that significant architectural simplifications can be made while preserving the noise resilience. We verify these results numerically using a novel classical algorithm for the efficient simulation of noisy QRAM circuits. Our findings indicate that QRAM can be implemented with existing hardware in realistically noisy devices, and that high-fidelity queries are possible without quantum error correction. Furthermore, we also prove that the benefits of the bucket-brigade architecture persist when quantum error correction is used, in which case the scheme offers improved hardware efficiency and resilience to logical errors.