Integrated Multiresonator Quantum Memory.

Entropy (Basel)

Kazan Quantum Center, Kazan National Research Technical University, n.a. A.N.Tupolev-KAI, 10 K. Marx, 420111 Kazan, Russia.

Published: April 2023

We develop an integrated efficient multiresonator quantum memory scheme based on a system of three interacting resonators coupled through a common resonator to an external waveguide via switchable coupler. It is shown that high-precision parameter matching based on step-by-step optimization makes it possible to efficiently store the signal field and enables on-demand retrieval of the signal at specified time moments. Possible experimental implementations and practical applications of the proposed quantum memory scheme are discussed.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138295PMC
http://dx.doi.org/10.3390/e25040623DOI Listing

Publication Analysis

Top Keywords

quantum memory
12
multiresonator quantum
8
memory scheme
8
integrated multiresonator
4
memory develop
4
develop integrated
4
integrated efficient
4
efficient multiresonator
4
scheme based
4
based system
4

Similar Publications

In this paper, we study the Schrödinger cat state transfer in a quantum optical version of Newton's cradle in a non-Markovian environment. Based on a non-Markovian master equation, we show that the cat state can be transferred purely through the memory effect of the non-Markovian common environment, even without any direct couplings between neighbor cavities. The mechanism of the environment-induced cat state transfer is analyzed both analytically and numerically to demonstrate that the transfer is a unique phenomenon in a non-Markovian regime.

View Article and Find Full Text PDF

Faithful quantum state transfer between telecom photons and microwave frequency mechanical oscillations necessitate a fast conversion rate and low thermal noise. Two-dimensional (2D) optomechanical crystals (OMCs) are favorable candidates that satisfy those requirements. 2D OMCs enable sufficiently high mechanical frequency (1∼10 GHz) to make the resolved-sideband regime achievable, a prerequisite for many quantum protocols.

View Article and Find Full Text PDF

Re-locative guided search optimized self-sparse attention enabled deep learning decoder for quantum error correction.

Sci Rep

January 2025

Department of Mathematics, School of Advanced Sciences, VIT-AP University, Besides AP Secretariate, Amaravati, Andhra Pradesh, 522237, India.

Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes.

View Article and Find Full Text PDF

We argue that "processes versus objects" is not a useful dichotomy. There is, instead, substantial theoretical utility in viewing "objects" and "processes" as complementary ways of describing persistence through time, and hence the possibility of observation and manipulation. This way of thinking highlights the role of memory as an essential resource for observation, and makes it clear that "memory" and "time" are also mutually inter-defined, complementary concepts.

View Article and Find Full Text PDF

Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!