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Infleqtion has achieved 30 entangled logical qubits on its commercial Sqale quantum computing platform, utilizing 80 physical neutral-atom qubits. This breakthrough, announced at Quantum World Congress 2026, was enabled by an AI-assisted logical circuit discovery and software-based loss correction. This positions Infleqtion to scale to 100 logical qubits by 2028 and 1,000 by 2030, with current applications in biomarker discovery.

Mphasis and Copa Airlines partnered on a global Quantum Computing Challenge, bringing together academic teams to develop quantum optimization solutions for airline passenger re-accommodation during disruptions. The hackathon leveraged quantum algorithms and real operational data to improve efficiency in reassigning passengers while maintaining operational constraints. This initiative is part of Mphasis's broader enterprise quantum software portfolio.

IonQ's research shows that quantum generative machine learning models on their trapped-ion quantum processing units can more accurately detect ground-level changes from satellite radar imagery, especially with high-resolution data causing sparse pixel statistics. Their Quantum Circuit Born Machines (QCBMs) outperformed classical methods in detecting changes at MCAS Miramar Airfield and showed strong generalization capabilities. This advancement has implications for defense, intelligence, infrastructure monitoring, and disaster response.

IonQ has signed an agreement with Florida International University (FIU) to provide and install an on-premises IonQ Superion 256 trapped-ion quantum computer by late 2027. This makes FIU IonQ's main academic partner in Florida and the first to have a 256-qubit system in the state. The quantum computer will be used for research in various fields, including material science, AI, and cybersecurity, and will be accessible to FIU's faculty, students, and regional partners.

QC Design has introduced Meridian, an AI-powered platform for optimizing fault-tolerant quantum computing (FTQC) hardware. Meridian has demonstrated a 14.6x median reduction in logical error rates compared to existing methods and a 43% reduction against other frontier AI agents like GPT-6 Astra. This platform aims to accelerate the development of scalable quantum processors by automating multi-layer architectural design.

memQ Inc. has open-sourced its Distributed Quantum Compiler (memQ DQC), a modality-agnostic software framework for scaling quantum computing across multi-vendor QPU networks. This release aligns with U.S. federal directives and allows developers to model multi-QPU execution and analyze resource trade-offs without manual low-level programming. The toolchain features an interactive Quantum Network Constructor and dynamic partitioning, with accompanying research highlighting the importance of compiler selection based on hardware topology.

memQ Inc. has released memQ DQC, an open-source Python framework for distributed quantum computing. It compiles and schedules quantum programs across multi-processor quantum architectures, addressing challenges in scaling quantum processing units beyond physical limitations. The framework allows researchers to define network topologies, partitions operations, routes remote gates, and includes a multi-algorithm compiler and schedulers to optimize distributed quantum program execution.

By Sinan Utku Example 2: Implementing a Logical Qubit in a Surface Code Surface codes are a promising class of quantum error correction codes that will likely be used in fault-tolerant quantum computing. In a surface code, many physical data qubits collectively encode a logical qubit in a nonlocal, topologically protected manner, making it robust against physical errors. The qubits are arranged on a surface lattice, such as a plane, in which data qubits are interleaved with ancilla (measurement) qubits. These measurement qubits interact with neighboring data qubits to perform stabilizer measurements, extracting error syndromes without collapsing the encoded logical state. [...]

Classiq Technologies and Israel Natural Gas Lines (INGL) have published research on using hybrid quantum-classical optimization for natural gas pipeline networks. Their study, detailed on arXiv (arXiv:2609.00825), demonstrates a Quantum Approximate Optimization Algorithm (QAOA) workflow to maximize gas throughput, validated on the IonQ Forte-1 quantum computer. This approach, which complements existing engineering tools, shows how quantum processors can address complex energy infrastructure challenges.

CGI and D-Wave have partnered to bring quantum optimization solutions to enterprises, integrating D-Wave's quantum hardware into CGI's IT services. This collaboration aims to develop quantum-powered applications for complex challenges in transportation, logistics, and retail, leveraging CGI's global reach and D-Wave's annealing quantum technology. The initiative focuses on moving clients from proof-of-concept to full production deployment of hybrid quantum-classical solutions.