Qubit-efficient entanglement spectroscopy using qubit resets

TL;DR

This paper introduces qubit-efficient algorithms for entanglement spectroscopy that reduce qubit requirements and improve noise resilience on NISQ devices by utilizing qubit resets and reinitializations, enabling larger system analysis.

Contribution

The authors develop novel algorithms for computing $Tr( ho^n)$ that require fewer qubits and incorporate qubit resets, outperforming previous methods in noisy environments.

Findings

Algorithms perform similarly to previous variants under noise.

Numerical simulations confirm noise resilience.

Experimental implementation estimates $Tr( ho^n)$ for larger n.

Abstract

One strategy to fit larger problems on NISQ devices is to exploit a tradeoff between circuit width and circuit depth. Unfortunately, this tradeoff still limits the size of tractable problems since the increased depth is often not realizable before noise dominates. Here, we develop qubit-efficient quantum algorithms for entanglement spectroscopy which avoid this tradeoff. In particular, we develop algorithms for computing the trace of the n-th power of the density operator of a quantum system, $Tr(\rho^n)$, (related to the R\'enyi entropy of order n) that use fewer qubits than any previous efficient algorithm while achieving similar performance in the presence of noise, thus enabling spectroscopy of larger quantum systems on NISQ devices. Our algorithms, which require a number of qubits independent of n, are variants of previous algorithms with width proportional to n, an asymptotic difference. The crucial ingredient in these new algorithms is the ability to measure and reinitialize subsets of qubits in the course of the computation, allowing us to reuse qubits and increase the circuit depth without suffering the usual noisy consequences. We also introduce the notion of effective circuit depth as a generalization of standard circuit depth suitable for circuits with qubit resets. This tool helps explain the noise-resilience of our qubit-efficient algorithms and should aid in designing future algorithms. We perform numerical simulations to compare our algorithms to the original variants and show they perform similarly when subjected to noise. Additionally, we experimentally implement one of our qubit-efficient algorithms on the Honeywell System Model H0, estimating $Tr(\rho^n)$ for larger n than possible with previous algorithms.