Distributed Quantum Computing

A path to large scale quantum computation and quantum networks

Introduction

• Quantum computing is a powerful paradigm that can outperform classical computing for certain tasks
• However, building large-scale and robust quantum computers is challenging due to physical and technological limitations
• Distributed quantum computing is a model where multiple quantum computers communicate and cooperate via a network
• Distributed quantum computing can enable scaling up quantum computation and quantum networks
• Distributed quantum computing has many applications and challenges

Distributed Quantum Memory

• Quantum memory is a device that can store and retrieve quantum information
• Quantum memory is essential for distributed quantum computing as it enables long-lived storage and processing of quantum information
• Different types of quantum memory devices have different advantages and disadvantages, such as atom-based, ion-based, spin-based, etc.
• Some recent progress on distributed quantum memory are:
  • Robust quantum memory in trapped-ion network node
  • Quantum entanglement between atom-based memory devices 12.5 km apart
  • Quantum memory based on spin qubits in diamond
  • Quantum memory based on electromagnetically induced transparency for non-classical light
• Some open problems on distributed quantum memory are:
  • How to optimize the trade-off between storage time and retrieval efficiency?
  • How to achieve high-fidelity entanglement distribution over long distances and noisy channels?
  • How to integrate different types of quantum memory devices in a heterogeneous network?
  • How to protect quantum memory from environmental noise and decoherence?

Distributed Quantum Algorithms

• Quantum algorithms are procedures that perform specific tasks on quantum data
• Quantum algorithms are essential for distributed quantum computing as they enable solving complex problems that are intractable for classical computers