Yale's ERASE project has received a $4 million NSF grant to fund a two-year second phase focused on developing a blueprint for a large-scale, error-correcting quantum computer. The project centers on erasure qubits — dual-resonator quantum bits that can flag dominant errors as they occur, simplifying error correction. Partners include D-Wave Quantum (which acquired Yale spin-off Quantum Circuits Inc.), Princeton, the University of Maryland, and Southern Connecticut State University. The phase will expand research, software, algorithm development, and quantum workforce training in Connecticut, with a detailed hardware plan for a future Phase 3.
Nguồn: https://thequantuminsider.com/2026/06/26/yale-officials-say-4-million-nsf-grant-will-boost-unique-approach-to-quantum-computing. 8sync News chỉ tóm tắt và dẫn link; bản quyền nội dung thuộc tác giả và nguồn gốc.
Sắc lệnh hành pháp 14409 của Mỹ yêu cầu các cơ quan liên bang và nhà thầu phải chuyển sang mã hóa hậu lượng tử (PQC) vào năm 2030 và xác thực hậu lượng tử vào năm 2031, nhằm ngăn chặn các cuộc tấn công "thu thập giờ đây giải mã sau". Cloudflare khuyến nghị cần làm rõ tiêu chuẩn "chuyển đổi", ưu tiên khả năng thích ứng mật mã (crypto agility) và thúc đẩy sự thống nhất toàn cầu về thuật toán NIST để tránh phân mảnh.
Lập trình viên nên đọc bài này để hiểu cách chuyển đổi sang các giải pháp mã hóa chống lượng tử (post-quantum) không chỉ là một yêu cầu pháp lý mà là một chiến lược bảo mật cấp hệ thống, giúp bảo vệ ứng dụng của bạn trước các mối đe dọa tương lai từ máy tính lượng tử trong thời gian ngắn nhất.

STMicroelectronics has launched the ST54M, a single-die secure mobile chip integrating post-quantum cryptography (PQC) hardware acceleration, NFC, secure element, and eSIM functionality. The chip supports PQC algorithms ML-KEM and ML-DSA, and is designed to help OEMs meet anticipated quantum-ready security mandates expected around 2030. It targets applications like contactless payments, digital identity, transit ticketing, and digital car keys. The ST54M has completed Common Criteria EUCC and EMVCo certification testing, includes up to 4.5 MB nonvolatile memory, and is available for sampling with production expected in July 2026.
AQSolotl and QuantrolOx have announced a strategic partnership to integrate AQSolotl's Chronos-Q quantum control hardware with QuantrolOx's Quantum EDGE machine learning-based automation platform. The goal is to automate qubit calibration, reduce manual tuning cycles, and improve qubit stability as quantum systems scale toward commercial deployment. The collaboration will proceed in two phases: near-term technical integration and performance benchmarking, followed by deeper hardware-software co-design and joint commercial offerings for research and enterprise customers.
WISeKey and SEALSQ have established Quantisimo Corp., a joint venture special purpose vehicle, and signed a non-binding letter of intent with GigCapital8 (a SPAC) to pursue a Nasdaq listing. The proposed business combination values Quantisimo at approximately $575 million pre-money, with ambitions to grow to $2 billion through acquisitions of up to five additional quantum companies. Quantisimo aims to become a publicly traded platform focused on trusted quantum technologies, drawing on WISeKey's cybersecurity expertise and SEALSQ's semiconductor and post-quantum security portfolio. The transaction is expected to close in Q1 2027, pending regulatory and shareholder approvals.
Quantinuum has launched Helios, a 98-qubit trapped-ion quantum computer published in Nature, notable for its high accuracy rather than just qubit count. Single-qubit gate error rates average 2.5 in 100,000, while two-qubit gate errors average 7.9 in 10,000 — competitive with the best demonstrated results. Helios uses a quantum charge-coupled device (QCCD) architecture with all-to-all qubit connectivity, allowing any qubit to interact with any other without routing through intermediate steps. The machine separates storage, movement, and computation zones, and includes real-time software for routing and control decisions. While Helios can run random quantum circuits beyond easy classical simulation, this benchmark does not yet equate to solving real-world problems — but the combination of scale, accuracy, connectivity, and programmability marks a meaningful step forward.
An Economist opinion piece by Joshua Zoffer and Chris Miller argues quantum computing is one of the strongest cases for U.S. industrial policy due to its national-security implications and immature supply chain. The Trump administration's $2 billion investment across nine quantum companies — spanning multiple hardware architectures — is praised as a smart diversified bet. The authors warn, however, that broader federal equity investments across tech sectors need clearer guiding principles, suggesting warrants over direct equity stakes and emphasizing that intervention should be reserved for areas with genuine national-security needs that markets won't address alone.

Researchers use quantum optimal control techniques to design single continuous laser pulses that implement multi-qubit controlled-phase and controlled-SWAP (Fredkin) gates on Rydberg atom quantum processors. The approach reduces operation time and decoherence while providing continuous protection from environmental noise. The Fredkin gate achieves 99.88% fidelity even when accounting for real-world imperfections such as spontaneous emission, laser fluctuations, and Doppler dephasing.
The University of Maryland is funding a research project combining quantum computing and machine learning to accelerate the discovery of single-atom catalysts for cancer detection and treatment. Part of the university's Grand Challenges Grants Program, the project brings together engineers and computer scientists to build a predictive framework that models complex atomic and chemical behaviors — tasks difficult for classical computers. Quantum simulations would generate reliable databases of electronic structures and catalytic pathways, which machine learning models would then search to identify promising catalyst configurations. The team also plans to release benchmark datasets and reproducible computational tools to support open science. The research is preclinical and focused on discovery, not immediate clinical application.