Efficient and Error-Resilient Data Access Protocols for a Limited-Sized Quantum Random Access Memory

• URL: http://arxiv.org/abs/2303.05207v2
• Date: Wed, 21 Jun 2023 09:29:24 GMT
• Title: Efficient and Error-Resilient Data Access Protocols for a Limited-Sized Quantum Random Access Memory
• Authors: Zhao-Yun Chen, Cheng Xue, Yun-Jie Wang, Tai-Ping Sun, Huan-Yu Liu, Xi-Ning Zhuang, Meng-Han Dou, Tian-Rui Zou, Yuan Fang, Yu-Chun Wu and Guo-Ping Guo
• Abstract summary: We focus on the access of larger data sizes without keeping on increasing the size of the QRAM. We propose a novel protocol for loading data with larger word lengths $k$ without increasing the number of QRAM levels $n$. By exploiting the parallelism in the data query process, our protocol achieves a time complexity of $O(n+k)$ and improves error scaling performance.
• Score: 7.304498344470287
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Quantum Random Access Memory (QRAM) is a critical component for loading classical data into quantum computers. While constructing a practical QRAM presents several challenges, including the impracticality of an infinitely large QRAM size and a fully error-correction implementation, it is essential to consider a practical case where the QRAM has a limited size. In this work, we focus on the access of larger data sizes without keeping on increasing the size of the QRAM. Firstly, we address the challenge of word length, as real-world datasets typically have larger word lengths than the single-bit data that most previous studies have focused on. We propose a novel protocol for loading data with larger word lengths $k$ without increasing the number of QRAM levels $n$. By exploiting the parallelism in the data query process, our protocol achieves a time complexity of $O(n+k)$ and improves error scaling performance compared to existing approaches. Secondly, we provide a data-loading method for general-sized data access tasks when the number of data items exceeds $2^n$, which outperforms the existing hybrid QRAM+QROM architecture. Our method contributes to the development of time and error-optimized data access protocols for QRAM devices, reducing the qubit count and error requirements for QRAM implementation, and making it easier to construct practical QRAM devices with a limited number of physical qubits.

Related papers

• BitStack: Fine-Grained Size Control for Compressed Large Language Models in Variable Memory Environments [53.71158537264695]
  Large language models (LLMs) have revolutionized numerous applications, yet their deployment remains challenged by memory constraints on local devices. We introduce textbfBitStack, a novel, training-free weight compression approach that enables megabyte-level trade-offs between memory usage and model performance.
  arXiv  Detail & Related papers  (2024-10-31T13:26:11Z)
• Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs [61.40047491337793]
  We present Hierarchical cOntext MERging (HOMER), a new training-free scheme designed to overcome the limitations of large language models. HomeR uses a divide-and-conquer algorithm, dividing long inputs into manageable chunks. A token reduction technique precedes each merging, ensuring memory usage efficiency.
  arXiv  Detail & Related papers  (2024-04-16T06:34:08Z)
• Systems Architecture for Quantum Random Access Memory [0.6386668251980657]
  Quantum random access memory (QRAM) is a promising architecture for realizing quantum queries. We show how to leverage the intrinsic biased-noise resilience of the proposed QRAM for implementation on either Noisy Intermediate-Scale Quantum (NISQ) or Fault-Tolerant Quantum Computing (FTQC) hardware.
  arXiv  Detail & Related papers  (2023-06-05T20:52:28Z)
• QRAM: A Survey and Critique [1.52292571922932]
  Quantum random-access memory (QRAM) is a mechanism to access data based on addresses which are themselves a quantum state. We use two primary categories of QRAM from the literature: active and passive. In summary, we conclude that cheap, scalableally passive QRAM is unlikely with existing proposals.
  arXiv  Detail & Related papers  (2023-05-17T15:48:48Z)
• Quantum Random Access Memory For Dummies [4.608607664709314]
  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.
  arXiv  Detail & Related papers  (2023-05-02T03:24:16Z)
• DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables [49.965024476651706]
  DeepGEMM is a lookup table based approach for the execution of ultra low-precision convolutional neural networks on SIMD hardware. Our implementation outperforms corresponding 8-bit integer kernels by up to 1.74x on x86 platforms.
  arXiv  Detail & Related papers  (2023-04-18T15:13:10Z)
• Approximate Quantum Random Access Memory Architectures [7.509129971169722]
  Quantum supremacy in many applications using well-known quantum algorithms rely on availability of data in quantum format. We propose an approximate Parametric Quantum Circuit (PQC) based QRAM which takes address lines as input and gives out the corresponding data in these address lines as the output. We present two applications of the proposed PQC-based QRAM namely, storage of binary data and storage of machine learning (ML) dataset for classification.
  arXiv  Detail & Related papers  (2022-10-24T19:53:28Z)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• Improved FRQI on superconducting processors and its restrictions in the NISQ era [62.997667081978825]
  We study the feasibility of the Flexible Representation of Quantum Images (FRQI) We also check experimentally what is the limit in the current noisy intermediate-scale quantum era. We propose a method for simplifying the circuits needed for the FRQI.
  arXiv  Detail & Related papers  (2021-10-29T10:42:43Z)
• Parallelising the Queries in Bucket Brigade Quantum RAM [69.43216268165402]
  Quantum algorithms often use quantum RAMs (QRAM) for accessing information stored in a database-like manner. We show a systematic method to significantly reduce the effective query time by using Clifford+T gate parallelism. We conclude that, in theory, fault-tolerant bucket brigade quantum RAM queries can be performed approximately with the speed of classical RAM.
  arXiv  Detail & Related papers  (2020-02-21T14:50:03Z)

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.