Continuous Noise Model for Quantum Circuits

TL;DR

This paper introduces a continuous coherent noise model for quantum circuits, compares it with discrete models, and develops an efficient analytical method to predict error propagation, revealing its impact on quantum error correction performance.

Contribution

It proposes a novel continuous noise model using von Mises-Fisher distribution, and an approximate analytical method for error propagation in quantum circuits.

Findings

Continuous noise can cause more severe logical errors than Pauli noise.

The analytical method accurately predicts error propagation in Clifford circuits.

The model-independent matching scheme isolates noise effects at fixed uncertainty.

Abstract

Quantum noise is a central challenge in quantum computing across many applications. Extensive work has examined how qubits couple to their environment, leading to decoherence and relaxation, which is irreversible. Current studies focus on coherent gate errors caused by control misalignment, which accumulate with circuit depth but can, in principle, be corrected. This work studies a continuous coherent noise model for quantum circuits and compares it with a discrete Pauli model. The focus is on small coherent gate errors that build up across circuit depth. These errors are modeled as random rotations on the Bloch sphere using a von Mises-Fisher distribution. In the small-angle limit, the model reduces to an isotropic Gaussian distribution. We test the model on quantum error-correction circuits based on the [[5,1,3]] and [[7,1,3]] codes. A variant of Grover's search circuit with different qubit counts is also examined. To enable fair comparison, we introduce a model-independent matching scheme. Pauli and continuous noise channels are aligned using the binary entropy at readout. This isolates the effect of noise structure at fixed uncertainty. An approximate analytical method for coherent error propagation is also developed. The method tracks error distributions through Clifford circuits without full Monte Carlo sampling. It reduces simulation cost while preserving accuracy for circuit-level error estimates. The approximation is validated against brute-force simulations, identifying its regime of validity with Clifford circuits and limits under error correction. Our results show that continuous coherent noise can degrade logical performance more strongly than Pauli noise. They also clarify when simplified propagation models succeed and when they break down.