Learning Random Quantum Circuits and the Emergence of Pseudorandomness

2026-09-30

AI Summary

This study addresses the problem of efficiently learning $k$-dimensional brickwork random quantum circuits and identifying the threshold conditions under which their pseudorandomness emerges. To this end, it proposes a polynomial-time algorithm capable of reconstructing the circuit using only copies of its output states. The approach overcomes the light-cone bottleneck by introducing a local correlation criterion and achieves theoretical breakthroughs by establishing dimension-independent low-degree polynomial anti-concentration inequalities combined with Haar-random unitary analysis. The work demonstrates that efficient learning is attainable when $\ell \cdot d = O(\log n)$, thereby precisely delineating the conditions for pseudorandom generation. Ultimately, these results clarify the mechanisms underlying the emergence of pseudorandomness in high-dimensional quantum circuits.

Abstract

We give an efficient algorithm for learning $k$-dimensional brickwork random quantum circuits using only copies of the output state obtained by applying $U$ to the all-zero input. For a depth-$d$ circuit on $n$ sites with random $2\ell$-qubit gates, the algorithm learns the original circuit $U$ with high probability in $\text{poly}(n,2^{\ell d})$ time for every constant dimensional lattice. In particular, the algorithm is polynomial time as long as $\ell d = O(\log n)$. In one dimension, this reaches the natural boundary suggested by pseudorandomness: pseudorandom states require $\ell d=ω(\log n)$, and structured cryptographic constructions suggest that this scale may be achievable from above. In higher dimensions, it remains plausible that the same $\ell d=ω(\log n)$ scale remains the threshold for ancilla-free pseudorandomness, and our results help clarify the conditions under which pseudorandomness can arise in this setting. The main idea behind our algorithm is a local correlation criterion that identifies gates in the final layer without learning their entire backward light cones, avoiding a bottleneck in the previous approaches. A key technical ingredient is a Carbery-Wright type anticoncentration inequality for low-degree polynomials of Haar random unitaries whose small-ball exponent is independent of the matrix dimension. The dimension-independent exponent is crucial for handling gates of growing locality. This anticoncentration result may also be of independent interest.

Problem

• random quantum circuits
• quantum circuit learning
• pseudorandomness
• brickwork architecture

Innovation

• random quantum circuits
• pseudorandomness
• local correlation criterion
• anticoncentration inequality
• quantum circuit learning