Year: 2026
Type: preprint
Abstract: Tensor networks are powerful tools for simulating quantum many-body systems, but the growth of spatial entanglement severely limits the direct evolution of pure states under chaotic unitary circuits. Here we address the task of evaluating specified output probabilities of one-dimensional circuits at finite relative precision. We introduce a sweeping algorithm that contracts the circuit transversely and compresses its two temporal boundaries jointly through reduced transition matrices (RTMs), preserving their overlap rather than either boundary separately. At fixed target precision, the retained bond dimension exhibits a slowdown consistent with subexponential growth over the accessible time window, in a regime where the circuit is already deeply anticoncentrated. A noisy random-operator model illustrates how finite precision can induce a crossover from linear to logarithmic temporal entropy growth. These results open a route to probability queries for benchmarking and learning without representing the full quantum state.
Source: arXiv (Cornell University)
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7215772182