New Quantum LDPC Codes Improve Error Correction

Researchers have developed new quantum low-density parity-check (LDPC) codes based on circulant permutation matrices (CPMs). These Calderbank-Shor-Steane (CSS) type codes are crucial for quantum computing, as they enable the protection of quantum information from errors inherent in qubits. The construction is parameterized by column weight J, row weight L, and prime lift size P, and uses an array of pair partitions to impose linear equations that ensure CSS orthogonality. Quantum LDPC codes are a promising avenue for quantum error correction due to their sparse structure, which facilitates decoding. Specific examples presented include a (J,L)=(4,12) code with a rate of 0.349 and a distance [[372,130,16]], and another (J,L)=(4,14) code with a rate of 0.440 and a distance [[518,228,16]]. Instances of (J,L)=(3,8) with distances [[472,122,14]] and [[488,126,14]] for lift sizes P=59 and P=61, respectively, are also reported. The distance of these codes has been established through exhaustive low-weight exclusion and the use of explicit non-stabilizer witnesses, ensuring their ability to detect and correct errors. The improvement in the coding rate and minimum distance of these new LDPC codes is a significant step towards the construction of fault-tolerant quantum computers, a fundamental requirement for the development of large-scale quantum computing.

Neutrino Day 2026: Exploring the Underground DUNE Experiment

Neutrino Day 2026 has highlighted the Deep Underground Neutrino Experiment (DUNE) from the Sanford Underground Research Facility (SURF) in Lead, South Dakota. This annual event, broadcast live from a mile underground, offers insight into the ambitious DUNE project, designed to study the fundamental properties of neutrinos and their role in the universe. During the broadcast, key figures such as Mike Headley, SURF laboratory director, and Dr. Sowjanya Gollapinni, senior scientist for the DUNE experiment, participated. Their interventions provided details on the progress and scientific objectives of DUNE, which seeks to understand neutrino oscillation, mass hierarchy, and potential CP violation in the leptonic sector, which could explain the matter-antimatter asymmetry in the universe.

Fermilab and Qblox Commercialize QICK Platform for Quantum Control

The U.S. Department of Energy (DOE), Fermilab, and the company Qblox have formalized a collaboration for the commercialization of the QICK (Quantum Instrumentation Control Kit) platform. This agreement includes a commercial licensing structure to manage the manufacturing, supply chain, and distribution of the system developed by Fermilab. QICK is an open-source platform designed for the control and readout of superconducting qubits, which is crucial for the development of quantum computers. The initiative aims to accelerate the availability of high-performance quantum control hardware and reduce entry barriers for researchers and developers. The QICK platform enables more efficient integration between control software and experimental hardware, facilitating experimentation and innovation in the field of quantum computing. This step towards commercialization is fundamental for translating laboratory advances into practical applications and for standardizing certain components in the growing quantum industry. In addition to commercial distribution, the collaboration also aims to strengthen the quantum technology workforce. By making the QICK platform more accessible and supporting its use in academic and industrial settings, it is expected to foster the training of new talent and the creation of a broader community of quantum instrumentation experts. This focus on education and skill development is vital to sustain the rapid growth and complexity of quantum research and development.

AI-Assisted Formalization of Shor's Algorithm in Lean for Cryptanalysis

Scientists have successfully formalized the Shor's algorithm family within the Lean proof assistant, marking a significant milestone for machine-checked quantum cryptanalysis. This work employs an "agentic" approach, where software agents analyze sources, generate Lean code, and repair proofs, with human review of the scientific claims and machine verification of the resulting formal proofs. The formalization lays the mathematical foundations for analyzing quantum attacks in two key cryptographic settings: a 2048-bit modulus for RSA-2048 and the standardized elliptic curve over a 256-bit prime field (P-256). The formalization spans from quantum algorithms for order finding to reversible quantum circuits for modular and elliptic-curve arithmetic. Drawing upon previous work published in Quantum and ASIACRYPT, the team has formalized the logical resource estimates for RSA-2048 and P-256, respectively, and has provided additional estimates for the classical operations required. This advancement is crucial for understanding the computational requirements of quantum attacks on current cryptographic systems. This development represents an important step towards AI-assisted design and verification of quantum algorithms. The ability to rigorously formalize and verify quantum algorithms, especially those with security implications, is essential as quantum computing matures. These results are expected to pave the way for broader machine-checked quantum cryptanalysis, enhancing confidence in the security assessments of post-quantum cryptographic systems.

Renormalizing two-photon contribution to K_L→μ⁺μ⁻ decay

A new study addresses the Standard Model prediction for the rare K_L→μ⁺μ⁻ decay, a process critically dependent on the long-distance contribution from the exchange of two photons. This calculation, fundamental for the precision of theoretical predictions, is typically performed using lattice quantum chromodynamics (lattice QCD) with an effective three-flavor theory (u, d, and s quarks), assuming that terms decaying as the inverse square of the charm quark mass (1/m_c²) are negligible. The challenge with this three-flavor approximation lies in the absence of the Glashow-Iliopoulos-Maiani (GIM) cancellation, which introduces additional low-energy constants that explicitly depend on the charm quark mass. The novelty of this work consists in demonstrating how these constants can be practically determined. To achieve this, the researchers propose a strategy involving a four-flavor lattice QCD simulation. This simulation is performed on a small volume and with u and d quark masses that are heavier than their physical values. The method allows for the renormalization of the two-photon contribution, improving the precision of the Standard Model prediction for this kaon decay. The ability to determine these low-energy constants from four-flavor lattice QCD calculations is a significant advancement for reducing theoretical uncertainties in particle physics.

Diagonal catalysts enhance quantum annealing

Researchers have developed a new technique to optimize quantum annealing, a computational method designed to solve complex optimization problems. The technique, dubbed "ZZ-catalysts," is based on manipulating the energy landscape of the problem, making state configurations far from the optimal solution less energetically favorable. This helps prevent the quantum system from getting trapped in local minima, a common obstacle that limits the efficiency of quantum annealing. Quantum annealing seeks to solve a problem by encoding its possible states as spin configurations in an energy landscape. The optimal solution corresponds to the global energy minimum. However, the presence of multiple local minima can trap the system, preventing it from reaching the true solution. The new methodology introduces a mathematical framework to understand the connection between energy and Hamming distance (the number of differing spins between configurations) in optimization problems. Using this framework, ZZ-catalysts are built from ground-state patterns of small, frustration-free subproblems. Experiments show that these catalysts multiply the probability of finding near-solutions in short sweeps for sparse problems. Furthermore, the gains persist on fully-connected models, and their effectiveness can be tuned via subproblem choice. This advancement could significantly improve the ability of quantum annealers to tackle large-scale optimization problems, with implications in fields such as logistics, materials design, and drug discovery, where finding optimal configurations is crucial.