Subsystem Statistics and Conditional Self-Similarity of Random Quantum States

TL;DR

This paper analytically characterizes the statistical distributions of subsystems in random quantum states, revealing a universal Beta distribution law and a hidden self-similarity that persists under noise, with implications for quantum benchmarking.

Contribution

It introduces a unified finite-size description of subsystem statistics using the Beta distribution and proves the exact conditional self-similarity property of random quantum states.

Findings

Beta distribution as universal law of random states

Conditional self-similarity of subsystem distributions

Framework for validating quantum sampling methods

Abstract

We analytically derive the bit-string probability distributions of subsystems of random pure states and depolarized random states using the Dirichlet distribution. We identify the exact Beta distribution as the universal statistical law of random quantum states, providing a unified finite-size description of full-system, subsystem, and conditional statistics. In the presence of depolarizing noise, these distributions are scaled and shifted by the noise strength, producing a noise-induced gap in their support. Remarkably, we prove that random states exhibit exact conditional self-similarity: the distribution of subsystem bit-string probabilities conditioned on specific outcomes of the complementary subsystem is identical to that of the full system. This hidden scale invariance enables the exact restoration of the full-system statistics from the marginalized Beta distribution via post-selection, and persists under depolarizing noise. Our results uncover a fundamental symmetry of Hilbert space and provide a scalable, rigorous framework for validating random circuit sampling via subsystem or conditional cross-entropy benchmarking.