Pauli quantum computing: $I$ as $|0\rangle$ and $X$ as $|1\rangle$

TL;DR

This paper introduces Pauli quantum computing, a new formalism that encodes information using Pauli basis operators, offering advantages in state preparation, amplitude estimation, and search algorithms, distinct from standard quantum computing.

Contribution

The paper presents a novel quantum computing framework based on Pauli operators, with new methods for state preparation, amplitude estimation, and search algorithms, highlighting its advantages over traditional models.

Findings

Efficient preparation of stabilizer ground states via Lindbladians.

Exponential reduction in amplitude estimation complexity for certain circuits.

Linear query complexity for search problems using Pauli-encoded oracles.

Abstract

We propose a new quantum computing formalism named Pauli quantum computing. In this formalism, we use the Pauli basis $I$ and $X$ on the non-diagonal blocks of density matrices to encode information and treat them as the computational basis $|0\rangle$ and $|1\rangle$ in standard quantum computing. There are significant differences between Pauli quantum computing and standard quantum computing from the achievable operations to the meaning of measurements, resulting in novel features and comparative advantages for certain tasks. We will give three examples in particular. First, we show how to design Lindbladians to realize imaginary time evolutions and prepare stabilizer ground states in Pauli quantum computing. These stabilizer states can characterize the coherence in the steady subspace of Lindbladians. Second, for quantum amplitudes of the form $\langle +|^{\otimes n}U|0\rangle^{\otimes n}$ with $U$ composed of $\{H,S,T,\text{CNOT}\}$, as long as the number of Hadamard gates in the unitary circuit $U$ is sub-linear $\mathit{o}(n)$, the gate (time) complexity of estimating such amplitudes using Pauli quantum computing formalism can be exponentially reduced compared with the standard formalism ($\mathcal{O}(\epsilon^{-1})$ to $\mathcal{O}(2^{-(n-\mathit{o}(n))/2}\epsilon^{-1})$). Third, given access to a searching oracle under the Pauli encoding picture manifested as a quantum channel, which mimics the phase oracle in Grover's algorithm, the searching problem can be solved with $\mathcal{O}(n)$ scaling for the query complexity and $\mathcal{O}(\text{poly}(n))$ scaling for the time complexity. While so, how to construct such an oracle is highly non-trivial and unlikely efficient due to the hardness of the problem.