Entanglement rate quadruples IonQ’s previous record for ions

IonQ reports achieving entanglement rates exceeding 1,000 per second between a trapped ion qubit and a silicon vacancy quantum memory, establishing a new record for ion-based systems. This photonic interconnect couples the coherence of trapped ions with the light-coupling efficiency of solid-state memory, a combination the company claims is faster than any other platform. “IonQ has crossed a pivotal milestone for memory enhanced quantum interconnects, achieving more than 1,000 entanglement events per second,” said Niccolo de Masi, Chairman and CEO of IonQ, framing the advance as addressing a challenge in building networked quantum data centers.

Photonic Interconnect Achieves 1 kHz Entanglement for Trapped Ions

The demonstrated rate supports distributed quantum computing applications and represents a step toward scaling quantum systems. The architecture underpinning this advance is designed for broad compatibility, extending beyond trapped ion systems to potentially include neutral atoms and superconducting qubits with appropriate transducer technology. This versatility positions IonQ to address diverse hardware approaches within the expanding quantum computing field. The company states that their technology can interface with almost any qubit type, unlocking a wide range of opportunities across multiple hardware modalities, from modular computing to networked sensing and beyond.

Commercialization of the memory and interconnect platform is already underway, with systems sold to the University of Maryland in April and to SDT in South Korea in September. This achievement builds on over a decade of IonQ’s focused research into quantum interconnects, recognizing that transferring quantum information via photons will be essential for large-scale quantum computers, the company says.

Chris Monroe, Chief Scientist at IonQ and Gilhuly Family Presidential Distinguished Professor, said IonQ’s efforts in quantum interconnects go back over a decade, to the founding of the company. The team asserts this work demonstrates a photonic quantum interconnect does not limit distributed quantum computing.

IonQ has crossed a pivotal milestone for memory enhanced quantum interconnects, achieving more than 1,000 entanglement events per second.

Niccolo de Masi, Chairman and CEO of IonQ

DARPA Collaboration Expands Quantum Memory to Multiple Qubit Types

This performance is central to the company’s participation in the Defense Advanced Research Projects Agency’s HARQ program, which aims to establish high-speed quantum interconnects usable with diverse qubit technologies. The DARPA collaboration specifically focuses on broadening compatibility beyond trapped ions, with the architecture designed to interface with neutral atoms and superconducting systems equipped with necessary signal transducers.

IonQ frames this interconnect breakthrough as a step toward realizing “networked quantum data centers of the future,” a vision dependent on overcoming limitations in qubit communication. The technical details of this end-to-end link between a trapped ion system and a silicon vacancy qubit are detailed in a recently published paper, outlining the hardware testing and results achieved.

This work shows that a photonic quantum interconnect need not be a bottleneck for distributed quantum computing.

Mihir Bhaskar, IonQ SVP and GM of Quantum Technologies at SkyWater
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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