arXiv:2605. 27729v2 Announce Type: cross Abstract: The 2024-2025 Nobel and Turing awards recognised AI and quantum science simultaneously.
By Dongping Liu, Aoyu Zhang, Luyao Zhang
With $25 million investment from the Commonwealth of Massachusetts, MIT to build a new shared-use facility to serve as a statewide quantum toolbox.
The fellowships in applied sciences, engineering, and mathematics recognize doctoral students who are pursuing solutions to the most pressing challenges in science and technology.
By Division of Graduate and Undergraduate Education
The article describes how an MIT researcher employs GPT‑5.6 Sol alongside Codex to autonomously conduct quantum computing experiments. It explains that the system runs the experiments, analyzes the results, and calibrates qubits without human intervention.
QuantumBoostNet is a hybrid classical‑quantum architecture designed to improve cardiac ultrasound view identification. It couples a classical backbone with two heads—one classical and a 10‑qubit quantum circuit—trained in two stages with an adaptive mixing parameter that balances the heads based on loss dynamics. Experiments demonstrate that QuantumBoostNet surpasses baseline models on standard benchmarks and shows statistically significant gains on FashionMNIST and MNIST, with a modest improvement on the echocardiography task and robustness to noise.
By Mihai Udrescu-Milosav, Stefan-Alexandru Jura, Mihai Udrescu, Gerhard-Paul Diller
Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment introduces Sim‑HVQC, a hybrid deep quantum neural network that integrates an adaptive, parameter‑free SimAM weighting module with classical feature extraction to retain class‑discriminative information before encoding into a Variational Quantum Circuit. Unlike prior work limited to binary classification, this framework is trained and evaluated on multiple multi‑class datasets such as MNIST, KMNIST, Fashion‑MNIST, and EMNIST. The study highlights reproducibility, parameter efficiency, and interpretability through multi‑seed evaluation, parameter analysis, and latent/quantum feature inspection, with source code publicly available on GitHub.
By Dilli Hang Rai