The content discusses the concept of distributed quantum computing (DQC) and its potential applications. It identifies two main types of DQC:
Resource DQC: Where the local quantum computation resources are insufficient, and DQC can be used to scale the available resources by linking multiple quantum devices.
Data DQC: Where the relevant information is distributed over multiple parties, and quantum computers can be used to perform computations on the shared data collaboratively.
The paper then explores several use cases where DQC can provide benefits:
Quantum Machine Learning: DQC can facilitate the implementation of larger quantum machine learning models that exceed the qubit capacity of individual devices. It also enables collaborative training of models when data is distributed across multiple parties.
Secure Computations: DQC can enhance security by avoiding the need to send input and algorithm data to a central quantum computer provider. It also enables multi-party computations without explicit data sharing.
Breaking Cryptography: Quantum algorithms like Grover's and Shor's can benefit from DQC implementations, leading to potential speedups in breaking classical cryptographic protocols.
Quantum Interferometry: DQC can improve the performance of quantum interferometers by allowing the quantum information from multiple interferometers to be processed collaboratively without the need to physically combine the signals.
The paper also discusses technical considerations for DQC, including the impact on quantum algorithms, quantum communication architecture, and quantum hardware requirements. It highlights the importance of minimizing communication between devices, leveraging commuting operations, and optimizing the coupling of quantum devices to achieve the benefits of DQC.
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by Juan C. Bosc... alle arxiv.org 10-02-2024
https://arxiv.org/pdf/2410.00609.pdfDomande più approfondite