Fundamental causal bounds of quantum random access memories

• URL: http://arxiv.org/abs/2307.13460v1
• Date: Tue, 25 Jul 2023 12:40:48 GMT
• Title: Fundamental causal bounds of quantum random access memories
• Authors: Yunfei Wang, Yuri Alexeev, Liang Jiang, Frederic T. Chong, Junyu Liu
• Abstract summary: We study the intrinsic bounds of rapid quantum memories based on causality. We show that QRAM can accommodate up to $mathcalO(107)$ logical qubits in 1 dimension, $mathcalO(1015)$ to $mathcalO(1020)$ in various 2D architectures, and $mathcalO(1024)$ in 3 dimensions.
• Score: 13.19534468575575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum devices should operate in adherence to quantum physics principles. Quantum random access memory (QRAM), a fundamental component of many essential quantum algorithms for tasks such as linear algebra, data search, and machine learning, is often proposed to offer $\mathcal{O}(\log N)$ circuit depth for $\mathcal{O}(N)$ data size, given $N$ qubits. However, this claim appears to breach the principle of relativity when dealing with a large number of qubits in quantum materials interacting locally. In our study we critically explore the intrinsic bounds of rapid quantum memories based on causality, employing the relativistic quantum field theory and Lieb-Robinson bounds in quantum many-body systems. In this paper, we consider a hardware-efficient QRAM design in hybrid quantum acoustic systems. Assuming clock cycle times of approximately $10^{-3}$ seconds and a lattice spacing of about 1 micrometer, we show that QRAM can accommodate up to $\mathcal{O}(10^7)$ logical qubits in 1 dimension, $\mathcal{O}(10^{15})$ to $\mathcal{O}(10^{20})$ in various 2D architectures, and $\mathcal{O}(10^{24})$ in 3 dimensions. We contend that this causality bound broadly applies to other quantum hardware systems. Our findings highlight the impact of fundamental quantum physics constraints on the long-term performance of quantum computing applications in data science and suggest potential quantum memory designs for performance enhancement.

Related papers

• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Limitations of Noisy Quantum Devices in Computational and Entangling Power [5.178527492542246]
  We show that noisy quantum devices with a circuit depth of more than $O(log n)$ provide no advantages in any quantum algorithms. We also study the maximal entanglement that noisy quantum devices can produce under one- and two-dimensional qubit connections.
  arXiv  Detail & Related papers  (2023-06-05T12:29:55Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• $T$-depth-optimized Quantum Search with Quantum Data-access Machine [0.0]
  We introduce an efficient quantum data-access process, dubbed as quantum data-access machine (QDAM) We analyze the runtime of our algorithm in view of the fault-tolerant quantum computation (FTQC) consisting of logical qubits within an effective quantum error correction code.
  arXiv  Detail & Related papers  (2022-11-08T01:36:02Z)
• Field-deployable Quantum Memory for Quantum Networking [62.72060057360206]
  We present a quantum memory engineered to meet real-world deployment and scaling challenges. The memory technology utilizes a warm rubidium vapor as the storage medium, and operates at room temperature. We demonstrate performance specifications of high-fidelity retrieval (95%) and low operation error $(10-2)$ at a storage time of 160 $mu s$ for single-photon level quantum memory operations.
  arXiv  Detail & Related papers  (2022-05-26T00:33:13Z)
• Quantum State Preparation with Optimal Circuit Depth: Implementations and Applications [10.436969366019015]
  We show that any $Theta(n)$-depth circuit can be prepared with a $Theta(log(nd)) with $O(ndlog d)$ ancillary qubits. We discuss applications of the results in different quantum computing tasks, such as Hamiltonian simulation, solving linear systems of equations, and realizing quantum random access memories.
  arXiv  Detail & Related papers  (2022-01-27T13:16:30Z)
• Exponential separations between learning with and without quantum memory [17.763817187554096]
  We study the power of quantum memory for learning properties of quantum systems and dynamics. Many state-of-the-art learning algorithms require access to an additional external quantum memory. We show that this trade-off is inherent in a wide range of learning problems.
  arXiv  Detail & Related papers  (2021-11-10T19:03:49Z)
• Towards understanding the power of quantum kernels in the NISQ era [79.8341515283403]
  We show that the advantage of quantum kernels is vanished for large size datasets, few number of measurements, and large system noise. Our work provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices.
  arXiv  Detail & Related papers  (2021-03-31T02:41:36Z)
• Quantum walk processes in quantum devices [55.41644538483948]
  We study how to represent quantum walk on a graph as a quantum circuit. Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
  arXiv  Detail & Related papers  (2020-12-28T18:04:16Z)
• Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits [6.56216604465389]
  Quantum machine learning could become a powerful tool for data analysis in high energy physics. We employ the quantum variational classifier method in two recent LHC flagship physics analyses. We foresee the usage of quantum machine learning in future high-luminosity LHC physics analyses.
  arXiv  Detail & Related papers  (2020-12-21T18:39:36Z)
• 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.