Noisy Quantum Simulation Using Tracking, Uncomputation and Sampling

TL;DR

TUSQ is a novel method for noisy quantum simulation that significantly reduces computational overhead by leveraging error characterization and structural circuit similarities, enabling faster and more scalable simulations.

Contribution

The paper introduces TUSQ, combining error characterization and depth-first traversal to improve noisy quantum simulation efficiency under compute and memory constraints.

Findings

Achieves up to 7878x speedup over Qiskit.

Reduces simulation time by up to 439x compared to CUDA-Q.

Outperforms TQSim with up to 3134x speedup in constrained settings.

Abstract

Quantum computers have rapidly improved in scale and fidelity, yet access to large systems remains limited for most researchers. This makes accurate and scalable noisy quantum simulation essential. While density matrix simulation provides the most faithful representation of noisy quantum systems, its exponential memory overhead severely limits scalability. Consequently, noisy simulations are commonly performed by: (a) sampling multiple circuit instances with fixed noise realizations from stochastic noise channels, and (b) executing simulations of these sampled circuits and averaging the results. However, this introduces significant computational overhead due to the large number of circuit evaluations required. Existing approaches reduce this overhead by caching intermediate states for reuse, but such methods become impractical when simulations are both compute and memory constrained. To address this challenge, we propose TUSQ - Tracking, Uncomputation, and Sampling for Noisy Quantum Simulation. TUSQ consists of two components: the Error Characterization Module (ECM) and Depth-First Tree Traversal (DFTT). ECM reduces redundant simulation by identifying equivalent error configurations through Error Tallying and Error Commutation, followed by importance sampling during pruning to reduce the number of circuit instances requiring simulation. DFTT then exploits structural similarity across the remaining circuits by organizing them into a tree and traversing it using compute/uncompute operations to efficiently reuse intermediate computation without additional memory overhead. We evaluate TUSQ across 198 benchmarks with 1 million shots each. TUSQ achieves average(maximum) speedups of 59.06x(7878.03x) over Qiskit and 13.38x(439.38x) over CUDA-Q. Compared to TQSim in compute and memory constrained settings, TUSQ achieves average and maximum speedups of 39.32x and 3134.31x, respectively.