Quantum computing promises exponential speedups for certain problems, yet fully universal quantum computers remain out of reach and near-term devices are inherently noisy. Motivated by this, we study noisy quantum algorithms and the landscape between BQP and BPP. We build on a powerful technique to differentiate quantum and classical algorithms called the level-$\ell$ Fourier growth (the sum of absolute values of Fourier coefficients of sets of size $\ell$) and show that it can also be used to differentiate quantum algorithms based on the types of resources used. We show that noise acting on a quantum algorithm dampens its Fourier growth in ways intricately linked to the type of noise.
Concretely, we study noisy models of quantum computation where highly mixed states are prevalent, namely: DQC$_k$ algorithms, where $k$ qubits are clean and the rest are maximally mixed, and $\frac{1}{2}$-BQP algorithms, where the initial state is maximally mixed, but the algorithm is given knowledge of the initial state at the end of the computation. We establish upper bounds on the Fourier growth of DQC$_k$, $\frac{1}{2}$-BQP and BQP algorithms and leverage the differences between these bounds to derive oracle separations between these models. In particular, we show that 2-Forrelation and 3-Forrelation require $N^{\Omega(1)}$ queries in the DQC$_1$ and $\frac{1}{2}$-BQP models respectively. Our results are proved using a new matrix decomposition lemma that might be of independent interest.

This version corrects an important issue in the proofs of the main theorems in an earlier version of the paper. We thank Francisco Escudero Gutierrez and Miquel Saucedo Cuesta for bringing this issue to our attention.

Quantum computing promises exponential speedups for certain problems, yet fully universal quantum computers remain out of reach and near-term devices are inherently noisy. Motivated by this, we study noisy quantum algorithms and the landscape between BQP and BPP. We build on a powerful technique to differentiate quantum and classical algorithms called the level-$\ell$ Fourier growth (the sum of absolute values of Fourier coefficients of sets of size $\ell$) and show that it can also be used to differentiate quantum algorithms based on the types of resources used. We show that noise acting on a quantum algorithm dampens its Fourier growth in ways intricately linked to the type of noise.
Concretely, we study noisy models of quantum computation where highly mixed states are prevalent, namely: DQC$_k$ algorithms, where $k$ qubits are clean and the rest are maximally mixed, and $\frac{1}{2}$-BQP algorithms, where the initial state is maximally mixed, but the algorithm is given knowledge of the initial state at the end of the computation. We establish upper bounds on the Fourier growth of DQC$_k$, $\frac{1}{2}$-BQP and BQP algorithms and leverage the differences between these bounds to derive oracle separations between these models. In particular, we show that 2-Forrelation and 3-Forrelation require $N^{\Omega(1)}$ queries in the DQC$_1$ and $\frac{1}{2}$-BQP models respectively. Our results are proved using a new matrix decomposition lemma that might be of independent interest.