Goal: efficient simulation of multi-user entanglement distribution networks using cost-vector analysis. ‘Costs’ are arbitrary properties that accumulate additively as qubits traverse networks. We can express loss, dephasing & depolarising channels, and monetary cost in this form.

— Peter Rohde (@peter_rohde) May 5, 2021

Primitive operations in quantum networks include entanglement swapping (for extending entanglement links), and entanglement purification (for boosting fidelity). pic.twitter.com/Eu2fmcnc9V

— Peter Rohde (@peter_rohde) May 5, 2021

Here Alice & Bob have the option of communicating via:
• A static ground-based fibre link.
• A LEO satellite passing overhead through atmospheric free-space channels, which dynamically update.
• Exploiting both and purifying them together (multi-path routing). pic.twitter.com/GS0NDFrZqu

— Peter Rohde (@peter_rohde) May 5, 2021

We accommodate for quantum memories by treating them as temporal channels between the respective nodes of identical copies of the underlying graph, where each layer represents the network at a particular point in time. pic.twitter.com/JAKVO5oBeh

— Peter Rohde (@peter_rohde) May 5, 2021

The compression ratio is the ratio between routing time with and without memories. Here we show the temporal compression ratio of our algorithm against increasing network congestion. pic.twitter.com/6Vi2Xh2d0M

— Peter Rohde (@peter_rohde) May 5, 2021

Here’s a multi-user network with 3 users (colour coded) and multi-path routing (maximum 3 paths per user). The stacked layers represent time. pic.twitter.com/jwJb70EDrW

— Peter Rohde (@peter_rohde) May 5, 2021

This heat map shows the fidelity/efficiency trade off for random user pairs on a square lattice network. The distinct heat curves correspond to different numbers of paths utilised. Superimposed contours show achievable per-user E91 QKD secret key rates for the network. pic.twitter.com/nb9RDnDnmQ

— Peter Rohde (@peter_rohde) May 5, 2021

Consider a distributed computer with N nodes, each with n bits/qubits, and a scaling function that indicates classical-equivalent compute power (classically this is linear, for quantum computers super-linear). The computational gain achieved by unifying remote devices is: pic.twitter.com/15lLbHhoVV

— Peter Rohde (@peter_rohde) May 5, 2021

The big question is “in a future world with scalable quantum computers, is it economically justified to network them together”. The quantum networking infrastructure will be expensive, but the computational gains enormous. This is the question we hope to answer.

— Peter Rohde (@peter_rohde) May 5, 2021

Our vision for the quantum internet is presented in my upcoming book:

“The Quantum Internet” https://t.co/K2SYW78eO5

— Peter Rohde (@peter_rohde) May 5, 2021