Memory is an indispensable component in classical computing systems. While the development of quantum computing is still in its early stages, current quantum processing units mainly function as quantum registers. Consequently, the actual role of quantum memory in future advanced quantum computing architectures remains unclear. With the rapid scaling of qubits, it is opportune to explore the potential and feasibility of quantum memory across different substrate device technologies and application scenarios. In this paper, we provide a full design stack view of quantum memory. We start from the elementary component of a quantum memory device, quantum memory cells. We provide an abstraction to a quantum memory cell and define metrics to measure the performance of physical platforms. Combined with addressing functionality, we then review two types of quantum memory devices: random access quantum memory (RAQM) and quantum random access memory (QRAM). Building on top of these devices, quantum memory units in the computing architecture, including building a quantum memory unit, quantum cache, quantum buffer, and using QRAM for the quantum input-output module, are discussed. We further propose the programming model for the quantum memory units and discuss their possible applications. By presenting this work, we aim to attract more researchers from both the Quantum Information Science (QIS) and classical memory communities to enter this emerging and exciting area.
Memory is an indispensable component in classical computing systems. While the development of quantum computing is still in its early stages, current quantum processing units mainly function as quantum registers. Consequently, the actual role of quantum memory in future advanced quantum computing architectures remains unclear. With the rapid scaling of qubits, it is opportune to explore the potential and feasibility of quantum memory across different substrate device technologies and application scenarios. In this paper, we provide a full design stack view of quantum memory. We start from the elementary component of a quantum memory device, quantum memory cells. We provide an abstraction to a quantum memory cell and define metrics to measure the performance of physical platforms. Combined with addressing functionality, we then review two types of quantum memory devices: random access quantum memory (RAQM) and quantum random access memory (QRAM). Building on top of these devices, quantum memory units in the computing architecture, including building a quantum memory unit, quantum cache, quantum buffer, and using QRAM for the quantum input-output module, are discussed. We further propose the programming model for the quantum memory units and discuss their possible applications. By presenting this work, we aim to attract more researchers from both the Quantum Information Science (QIS) and classical memory communities to enter this emerging and exciting area.
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.
In the near future, as quantum and classical systems coexist, managing hybrid systems will require efficient conversion between bits and qubits. Therefore, understanding memory management for both classical and quantum systems is essential. Along that line, this survey provides a comparative analysis of memory management in classical and quantum architectures. The traditional memory hierarchy and allocation strategies used in classical systems are examined along with caching and paging techniques which optimize performance and resource utilization. On the other hand, quantum memory management focuses on addressing unique challenges such as quantum decoherence and error correction using advanced techniques such as Shor’s code, Surface codes, and other specialized error correction codes. This study explores Quantum Random Access Memory (QRAM) and its role in enabling efficient data retrieval in quantum systems. Quantum read/write operations and their impact on coherence and fidelity are analyzed, highlighting advancements in qubit state measurement and error mitigation. While classical systems take benefits of deterministic and hierarchical memory structures, quantum systems must overcome environmental noise and decoherence to maintain data stability. Furthermore, scalability issues and emerging trends in quantum architecture are discussed, emphasizing how advancements in Quantum Error Correction (QEC) and hybrid systems can address current limitations in memory management.
The approaching era will require computing resources that are future proof across various domains, extending from communications, computing and sensing. It is anticipated that quantum computing will play a major role due to limitations of classical computing. A vital factor in meeting this capability is the need of a non-classical storage unit to facilitate the quantum devices. Therefore, quantum memory becomes a necessity. This paper aims to present detailed discussion on quantum memory, which would begin from fundamental concepts and would range to a more advanced level, with the ultimate goal of identifying the most suitable method for developing quantum applications. The current protocols utilized in quantum memories rely on optically driven and photon echo based techniques. The survey will deeply analyze the processes and principles involved in storing and retrieving information using these methods. It would also assess their key features, including retention time, efficiency, measurement fidelity, bandwidth, and necessary conditions. In addition, the focus would also be on studying the neoteric realms of quantum dots and the Bose Einstein condensates. The paper concludes with challenges in building quantum memories and possible future directions.
The domain of computing, commonly referred to as quantum computing, is dedicated to the development of computer technology that is based on the fundamental principles of quantum theory, elucidates the behavior of matter and energy on the atomic and subatomic scales. In the realm of quantum computing, quantum memory serves as the quantum-mechanical equivalent of conventional computer memory. Quantum memory plays a pivotal role in the development of several quantum information processing devices, including but not limited to a synchronization tool that enables the matching of multiple procedures in a quantum computer, a quantum gate that preserves the identity of any given state, and a method for transforming preset photons into on-demand photons. To take advantage of the fundamental capabilities of quantum computing to perform parallel calculations at incredibly high speeds, a Quantum-based Random Access Memory (QRAM) architecture is proposed herein. This architecture utilizes basic quantum CNOT, V, and V+ gate operations to enable the implementation of basic quantum AND, OR, NOT, and NOR operations, respectively.
A random access memory, or RAM, is a device that, when interrogated, returns the content of a memory location in a memory array. A quantum RAM, or qRAM, allows one to access superpositions of memory sites, which may contain either quantum or classical information. RAMs and qRAMs with n-bit addresses can access 2n memory sites. Any design for a RAM or qRAM then requires O(2n) two-bit logic gates. At first sight this requirement might seem to make large scale quantum versions of such devices impractical, due to the difficulty of constructing and operating coherent devices with large numbers of quantum logic gates. Here we analyze two different RAM architectures (the conventional fanout and the ``bucket brigade'') and propose some proof-of-principle implementations, which show that, in principle, only O(n) two-qubit physical interactions need take place during each qRAM call. That is, although a qRAM needs O(2n) quantum logic gates, only O(n) need to be activated during a memory call. The resulting decrease in resources could give rise to the construction of large qRAMs that could operate without the need for extensive quantum error correction.
Building a quantum computer is not only about having qubits or quantum processors, analogously to classical computers are not only based on transistors or integrated circuits. Accordingly, in the very recent times, a few unique research groups in academia and companies have been exploring and proposing high-level and low-level programming languages, compilers, microarchitecture and instruction set architectures for quantum computation as well as different computer architecture approaches. In other words, developing a quantum computer requires bridging quantum applications and quantum devices. This talk will address the main challenges of building a scalable full-stack quantum computing system followed by a discussion on its architecture focusing on fault tolerance and compilation of quantum algorithms on NISQ devices. I will also provide my vision on how the research community could accelerate the process towards building such a scalable quantum machine, potentially through vertical cross-layer co-design structured methodologies.