An interdisciplinary institute at the frontier of quantum information theory, emergent computational systems, and applied intelligence architectures.
Our research spans six interconnected domains, from foundational mathematical proofs to experimental quantum hardware. The most significant breakthroughs emerge at disciplinary intersections.
Investigating entanglement entropy bounds, quantum error-correcting codes, and computational complexity of quantum circuits. Current focus: topological qubits under thermal decoherence.
Studying how complex cognitive behaviors arise from sparse, distributed computation. We build and analyze neural substrate models inspired by cortical microcircuit dynamics.
Developing automated theorem-proving systems across higher-order type theories. Applications in cryptographic protocol verification and safety-critical software certification.
Applying high-dimensional statistical models to cosmological large-scale structure data. Building simulation frameworks for dark energy evolution and baryon acoustic oscillations.
Probing the boundaries of P vs NP through parameterized complexity analysis and circuit lower bound techniques. Active collaboration on derandomization and pseudorandomness.
Lattice-based, code-based, and hash-based constructions. Active evaluation of NIST post-quantum standardization candidates and applied deployment in critical infrastructure contexts.
We derive improved fault-tolerance thresholds for surface codes subject to spatially correlated noise, demonstrating a 3.2% improvement over prior analytic bounds using tensor network methods on toric geometries.
A novel framework for super-polynomial lower bounds on depth-4 Boolean circuits using multilinear rank techniques and shifted partial derivatives applied to the iterated matrix multiplication polynomial.
A Falcon-variant signature scheme achieving 40% smaller signatures while maintaining tight security under the Short Integer Solution problem against both classical and quantum adversaries.
Analysis of how biologically-plausible Hebbian rules give rise to sparse, high-capacity memory attractors in multilayer networks, with implications for cortical circuit models and continual learning.
Our landmark study demonstrates provable quantum speedup for a class of constraint satisfaction problems on sparse random graphs. Using a modified QAOA framework with adaptive angle protocols, we achieve an asymptotic improvement over the best known classical algorithms for Max-k-Cut instances with bounded degree. The result has broad implications for combinatorial optimization and the theory of quantum speedup.
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