Active Computing & AI Physics & Astronomy

Microfabricated Ion-Cavity nodes for Robust, Optically-Networked Quantum Computing (MICRON-QC)

In plain English

AI plain-English summary

A trapped-ion quantum computer will be split into five separate processors linked by light pulses sent through optical fibres, rather than cramming all the qubits into a single chip. This matters because no existing qubit platform has shown a clear path to scaling up to the millions of qubits needed for useful fault-tolerant computation. Trapped ions achieve the highest-fidelity operations and allow highly-connected architectures, but a single monolithic processor hits a ceiling far below what is required. The only viable route is to distribute the computer across a network of smaller processors. If successful, this project will build and operate the first reconfigurable network of trapped-ion nodes linked by single photons. It will demonstrate all the elements needed for efficient large-scale networked computation: a flying qubit encoding suitable for long-range transmission, a reconfigurable photonic network enabling any-to-any node connectivity, and sufficient qubit resource within each node to assemble arbitrary entangled graph states across the network. The principal objective is to prove that a network of hundreds or thousands of nodes is within reach. This is fundamental science aimed at removing the central bottleneck to practical quantum computing, which could eventually transform fields such as drug discovery, materials design, and cryptography.

View original technical description
Quantum computing is poised to transform the way we tackle humanity's hardest computational problems, but despite recent progress in qubit control and error-correcting code design, no qubit platform has convincingly demonstrated a route to free scalability. Of all platforms, trapped ions retain the record for high-fidelity qubit operations, and enable highly-connected architectures, greatly reducing gate-count for large devices. However, scaling of qubit numbers on a single, monolithic processor is expected to hit a ceiling far below that required for useful fault-tolerant computation, and the ultimate route to scalability lies in distributing the quantum computer across a network of smaller processors. This project will construct and operate the first reconfigurable network of trapped ion processors linked by single photons emitted over fibre interconnects, ultimately consisting of 5 nodes. I will demonstrate all the elements required for efficient large-scale networked computation including: a flying qubit encoding and wavelength suitable for high-fidelity long-range transmission; a reconfigurable photonic network enabling any-to-any node connectivity and entanglement of multiple node-pairs in parallel; and sufficient qubit resource within each node to permit the assembly of arbitrary entangled graph states across the network. I will construct cavity-based network interfaces at each node capable of near-deterministic ion-photon entanglement at 1MHz attempt rates, allowing remote ion-ion entanglement creation at 100kHz rates, close to those of local gates. Through a combination of informed protocol design and advanced microsystem engineering, I will demonstrate that this can be achieved with nodes of remarkably simple and robust construction, enabling near-autonomous operation. While the 5-node network will enable many fascinating experiments, the principal objective of the project will be to prove that a network of hundreds or thousands of nodes is within reach.

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Researchers

Joseph Goodwin (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Ion trap-integrated optical cavities for fast networked quantum computation
Engineered photonic qubits for integrated optical quantum computing networks
HyperIon : Demonstrating a Scalable, Industrialised Qubit-Photon Interface (QPI) for Distributed Quantum Computing
Building Large Quantum States out of Light
INTERCOM: A high-performance ion-photon interface to enable multi-core trapped ion quantum computing

Original classification

Research Grant

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