Perfect Structure on the Edge of Chaos

Rachel Pei Chin Won

Nir Bitansky, Omer Paneth, Daniel Wichs

We construct trapdoor permutations based on (sub-exponential) indistinguishability obfuscation and one-way functions, thereby providing the first candidate that is not based on the hardness of factoring. Our construction shows that even highly structured primitives, such as trapdoor permutations, can be potentially based on hardness assumptions with noisy structures such as those used in candidate constructions of indistinguishability obfuscation. It also suggest a possible way to construct trapdoor permutations that resist quantum attacks, and that their hardness may be based on problems outside the complexity class SZK — indeed, while factoring-based candidates do not possess such security, future constructions of indistinguishability obfuscation might. As a corollary, we eliminate the need to assume trapdoor permutations and injective one-way function in many recent constructions based on indistinguishability obfuscation. 1

D. Steck

Tomohiro Hashizume, Zhengjun Wang, Frank Schlawin +1 more

A key challenge in classical machine learning is to mitigate overparameterization by selecting sparse solutions. We translate this concept to the quantum domain, introducing quantum sparsity as a principle based on minimizing quantum information shared across multiple parties. This allows us to address fundamental issues in quantum data processing and convergence issues such as the barren plateau problem in Variational Quantum Algorithm (VQA). We propose a practical implementation of this principle using the topological Entanglement Entropy (TEE) as a cost function regularizer. A non-negative TEE is associated with states with a sparse structure in a suitable basis, while a negative TEE signals untrainable chaos. The regularizer, therefore, guides the optimization along the critical edge of chaos that separates these regimes. We link the TEE to structural complexity by analyzing quantum states encoding functions of tunable smoothness, deriving a quantum Nyquist-Shannon sampling theorem that bounds the resource requirements and error propagation in VQA. Numerically, our TEE regularizer demonstrates significantly improved convergence and precision for complex data encoding and ground-state search tasks. This work establishes quantum sparsity as a design principle for robust and efficient VQAs.

Elisa Fratini, Sebastiano Pilati

Anderson localization of atomic gases in three-dimensional optical speckle potentials is studied via energy-level statistics familiar from quantum-chaos theory, and it is shown how the structure of the spatial correlation of the disorder determines the position of the mobility edge.

Daniel A. Lidar