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Leading quantum at an inflection point

November 13, 2025

The MIT Quantum Initiative is taking shape, leveraging quantum breakthroughs to drive the future of scientific and technological progress.

MIT engineers advance toward a fault-tolerant quantum computer

May 12, 2025

Researchers achieved a type of coupling between artificial atoms and photons that could enable readout and processing of quantum information in a few nanoseconds.

Device enables direct communication among multiple quantum processors

March 24, 2025

MIT researchers developed a photon-shuttling “interconnect” that can facilitate remote entanglement, a key step toward a practical quantum computer.

Department of EECS announces 2025 promotions and appointments

March 18, 2025

All promotions and appointments will take effect July 1, 2025.

Physicists measure a key aspect of superconductivity in “magic-angle” graphene

February 6, 2025

By determining how readily electron pairs flow through this material, scientists have taken a big step toward understanding its remarkable properties.

Karl K. Berggren named faculty head of electrical engineering in EECS

January 15, 2025

Berggren, who develops technologies to push the envelope of what is possible with photonics and electronic devices, succeeds Joel Voldman.

Fast control methods enable record-setting fidelity in superconducting qubit

January 15, 2025

The advance holds the promise to reduce error-correction resource overhead.

Nanoscale transistors could enable more efficient electronics

November 13, 2024

Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.

Quantum simulator could help uncover materials for high-performance electronics

October 31, 2024

By emulating a magnetic field on a superconducting quantum computer, researchers can probe complex properties of materials.

New security protocol shields data from attackers during cloud-based computation

October 1, 2024

The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.