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Quantinuum to Partner with the Singapore Institute of Technology to Help Develop Singapore’s Future Quantum Workforce.
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Quantinuum to Partner with the Singapore Institute of Technology to Help Develop Singapore’s Future Quantum Workforce.

Quantinuum to Partner with the Singapore Institute of Technology to Help Develop Singapore’s Future Quantum Workforce. Quantinuum has signed a Memorandum of Understanding (MoU) with the Singapore Institute of Technology (SIT) to train and expand Singapore’s quantum workforce. Building on Quantinuum’s existing R&D footprint and the planned deployment of its Helios quantum processor in Singapore, the collaboration aims to prepare an industry-ready workforce across engineering, systems development, and applied technologies. Key Initiatives of the Partnership Practical Curriculum: Joint development of hands-on training modules tailored for both undergraduate students and working professionals. Tool Access: Direct access to Quantinuum’s suite of quantum software, development tools, and simulators for educational use. Community Engagement: Hosting regular workshops, seminars, and campus events to build local interest and technical literacy in quantum computing. This strategic alignment addresses the growing commercial demand for skilled talent, ensuring local developers and engineers gain direct exposure to state-of-the-art quantum hardware and software environments. Additional information can be found in a LinkedIn post here. August 15, 2026 dougfinke2026-08-15T20:43:39-07:00 Leave A Comment Cancel replyComment Type in the text displayed above Δ This site uses Akismet to reduce spam. Learn how your comment data is processed.

Aug 16, 2026

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Three-Point Quantum Identity Confirms Existing Performance Limits for GKP Error-Correcting Codesquantum-computing

Three-Point Quantum Identity Confirms Existing Performance Limits for GKP Error-Correcting Codes

Researchers have demonstrated an exact determination of the optimum for GKP lattice codes, revealing a surprising result: extending a quantum error-correction framework to incorporate three-point interactions yields no improvement over existing two-point methods. The work, led by Yinzi Xiao of Paderborn University’s Department of Computer Science, constructs a three-point continuous-variable quantum MacWilliams identity and explores its implications for code dimension and distance. This identity’s configuration space carries a symplectic invariant with no classical counterpart, encoding both the GKP quantization condition and a three-point sign phase. The team certifies a collapse of the three-point term for radial Choi forms on the first eight Laguerre levels at one mode, suggesting limitations to the complexity of this approach for certain conditions. GKP Codes and Bosonic Quantum Error Correction The configuration space of the identity carries a symplectic invariant with no classical counterpart, revealing a structural cause not found in classical packing. Researchers have constructed the three-point continuous-variable (CV) quantum MacWilliams identity, extending previous two-point frameworks, and derived its integral kernel, a complex mathematical function central to understanding code dimensions and protection distances. This identity incorporates not only the GKP quantization condition, essential for building robust codes, but also a three-point phase absent in classical systems. The study rigorously investigates whether this more complex three-point approach offers improvements over existing two-point methods, particularly for GKP lattice codes. Surprisingly, the team proved “for GKP lattice codes the three-point optimum equals the Burchards two-point linear-programming optimum identically,” meaning the added complexity yields no benefit in this specific case. This is an “exact determination of the lattice three-point optimum,” demonstrating a complete chara

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Shaanxi Normal University Maps Gate Design to Evolution-Level Controlquantum-computing

Shaanxi Normal University Maps Gate Design to Evolution-Level Control

Shaanxi Normal University and Xi’an University of Posts and Telecommunications researchers are shifting the focus of quantum gate design from optimizing pulse amplitudes to learning the entire process of quantum evolution, utilizing a method called physics-informed neural networks. The work represents a move beyond simply finding a control solution to understanding the underlying structure of how that control is achieved. Rather than pre-defining control pulse shapes or durations, the team’s approach allows the artificial intelligence to independently arrive at physically expected results. For rotation gates, the optimized evolutions recover the physical organization expected for bounded single-qubit control, with no prescribed pulse ansatz or duration scan. This method not only synthesizes gates but also makes optimized quantum controls physically readable, diagnosable, and locally refinable, identifying localized bottlenecks in maintaining the geometric condition and using this diagnosis as feedback. Researchers at Shaanxi Normal University and Xi’an University of Posts and Telecommunications are developing a new approach to quantum gate design, moving beyond traditional pulse optimization to directly learn quantum evolution. This represents a fundamental shift from controlling how to control to controlling the process itself. This work, detailed in recent findings, utilizes physics-informed neural networks (PINNs) to represent the entire evolution of a single-qubit gate, simultaneously learning the control fields, Bloch-state trajectories, and total duration under the governing Bloch equation. Unlike conventional methods that treat pulse parameters as the primary optimization target, this approach views the gate as a unified dynamical object, where control, evolution, and time are intrinsically linked. Crucially, the representation doesn’t merely synthesize gates, but also enables a level of diagnostic control previously unavailable. When applied to geometric gat

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