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arXiv:2305.01178 · Sensors

Quantum Random Access Memory for Dummies

Koustubh Phalak, Avimita Chatterjee, Swaroop Ghosh

Published Aug 28, 2023• 43 citations
arXivHTMLPDF

Abstract

Quantum Random Access Memory (QRAM) has the potential to revolutionize the area of quantum computing. QRAM uses quantum computing principles to store and modify quantum or classical data efficiently, greatly accelerating a wide range of computer processes. Despite its importance, there is a lack of comprehensive surveys that cover the entire spectrum of QRAM architectures. We fill this gap by providing a comprehensive review of QRAM, emphasizing its significance and viability in existing noisy quantum computers. By drawing comparisons with conventional RAM for ease of understanding, this survey clarifies the fundamental ideas and actions of QRAM. QRAM provides an exponential time advantage compared to its classical counterpart by reading and writing all data at once, which is achieved owing to storage of data in a superposition of states. Overall, we compare six different QRAM technologies in terms of their structure and workings, circuit width and depth, unique qualities, practical implementation, and drawbacks. In general, with the exception of trainable machine learning-based QRAMs, we observe that QRAM has exponential depth/width requirements in terms of the number of qubits/qudits and that most QRAM implementations are practical for superconducting and trapped-ion qubit systems.

DOI
10.3390/s23177462

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Quantum Random Access Memory (QRAM) has the potential to revolutionize the area of quantum computing. QRAM uses quantum computing principles to store and modify quantum or classical data efficiently, greatly accelerating a wide range of computer processes. Despite its importance, there is a lack of comprehensive surveys that cover the entire spectrum of QRAM architectures. We fill this gap by providing a comprehensive review of QRAM, emphasizing its significance and viability in existing noisy quantum computers. By drawing comparisons with conventional RAM for ease of understanding, this survey clarifies the fundamental ideas and actions of QRAM. QRAM provides an exponential time advantage compared to its classical counterpart by reading and writing all data at once, which is achieved owing to storage of data in a superposition of states. Overall, we compare six different QRAM technologies in terms of their structure and workings, circuit width and depth, unique qualities, practical implementation, and drawbacks. In general, with the exception of trainable machine learning-based QRAMs, we observe that QRAM has exponential depth/width requirements in terms of the number of qubits/qudits and that most QRAM implementations are practical for superconducting and trapped-ion qubit systems.
Owing to the significant progress in manufacturing desktop quantum computers, the quest to achieve efficient quantum random access memory (QRAM) became inevitable. In this paper, we propose a novel efficient random access memory for quantum computers. The proposed QRAM has a fixed structure and can be used efficiently to store both known and unknown classical/quantum data. The storage capacity of the proposed QRAM is more efficient than that of the classical RAMs and can be used to store both classical and quantum information. Furthermore, the proposed model can access an arbitrary location in O(1) compared with other state-of-the-art models.
Quantum memory is proposed as an analogue to classical computer memory, like the random-access memory (RAM). RAM is a form of computer memory that can be read and changed in any order, typically used to store working data. In quantum machine learning domain, the usage of quantum random access memory (qRAM) is proposed to avoid input destruction problem. The method of qRAM is related to the entanglement of the index qubits that are in the superposition with the patterns. The method of qRAM is related to the entanglement of the index qubits that are in the superposition with the patterns. As in the method of the entanglement of the index qubits, the qubits of qRAM are entangled during the reading operation and have to be un-computed after computation.
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)\mathcal{O}(2^n)O(2n) required by the "Bucket Brigade" architecture by employing a number of quantum walkers that scales linearly with the address and message sizes, nnn and mmm, 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)\mathcal{O}(n+m)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.
Incompatibility of classical memory for quantum computing is explained. Efficient storage/modification of quantum or classical data is accomplished by using a quantum random-access memory (QRAM). A QRAM applies the principle of quantum superposition for simultaneously accessing all the data stored in the memory cell to assure the superior performance of quantum algorithms. Data reading and writing in a bucket-brigade QRAM are described and the feasibility of its optical implementation is discussed. Quantum computers are understood through layered architectures, viz., 3-, 4-, and 5-layer architectures. Organization of a superconducting quantum computer is illustrated by considering a generic architecture. The superconducting transmon qubits produced inside a dilution refrigerator are manipulated by sequences of microwave pulses. Electronic systems used for producing the control signals and reading the quantum information are described. Quantum computers have been built by several industry giants. Improved quantum processors are being assembled with continuous advancements and refinements. Future computing is likely to follow a hybrid paradigm in which the quantum computers will work in tandem with classical computers in a seamless, integrated environment. Each type of computing will complement the strengths and weaknesses of its counterpart.
Using quantum algorithms to carry out ML tasks is what is known as Quantum Machine Learning (QML) and the methods developed within this field have the potential to outperform their classical counterparts in solving certain learning problems. The development of the field is partly dependent on that of a functional quantum random access memory (QRAM), called for by some of the algorithms devised. Such a device would store data in a superposition and could then be queried when algorithms require it, similarly to its classical counterpart, allowing for efficient data access. Taking an axiomatic approach to QRAM, this thesis provides the main considerations, assumptions and results regarding QRAM and yields a QRAM handbook and comprehensive introduction to the literature pertaining to it.
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.