Variational Quantum Fidelity Estimation

Marco Cerezo1,2, Alexander Poremba3, Lukasz Cincio1, and Patrick J. Coles1

1Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
2Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
3Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.

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Computing quantum state fidelity will be important to verify and characterize states prepared on a quantum computer. In this work, we propose novel lower and upper bounds for the fidelity $F(\rho,\sigma)$ based on the ``truncated fidelity'' $F(\rho_m, \sigma)$, which is evaluated for a state $\rho_m$ obtained by projecting $\rho$ onto its $m$-largest eigenvalues. Our bounds can be refined, i.e., they tighten monotonically with $m$. To compute our bounds, we introduce a hybrid quantum-classical algorithm, called Variational Quantum Fidelity Estimation, that involves three steps: (1) variationally diagonalize $\rho$, (2) compute matrix elements of $\sigma$ in the eigenbasis of $\rho$, and (3) combine these matrix elements to compute our bounds. Our algorithm is aimed at the case where $\sigma$ is arbitrary and $\rho$ is low rank, which we call low-rank fidelity estimation, and we prove that no classical algorithm can efficiently solve this problem under reasonable assumptions. Finally, we demonstrate that our bounds can detect quantum phase transitions and are often tighter than previously known computable bounds for realistic situations.

In the past few years, there has been tremendous progress towards the development of near-term quantum devices. Today's quantum computers have limited number of qubits to work with, and are prone to noise-induced error. Nevertheless, despite those limitations, it has been recently shown that they can outperform our current supercomputers, being able to prepare quantum states that cannot be classically simulated. Hence, a very important question becomes: How do we verify and characterize these states prepared on a quantum computer?

Estimating a state's fidelity to a target state provides a $mean$ to verify and characterize the state. However, estimating the fidelity is known to $\textit{be a computationally}$ difficult task for a classical or even a quantum computer. Specifically, no classical or quantum algorithm exists to efficiently compute such quantities. In this work we introduce a hybrid quantum-classical algorithm to estimate the fidelity for the practically important case when one of the two states is low rank. Specifically, we introduce new lower and upper bounds for the fidelity which can be refined to arbitrary tightness, and we show that these bounds can outperform other known computable state-of-the-art bounds for the fidelity. Second, we introduce a near-term algorithm for computing our bounds.

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Cited by

[1] Santosh Kumar, "Wishart and random density matrices: Analytical results for the mean-square Hilbert-Schmidt distance", Physical Review A 102 1, 012405 (2020).

[2] Jonas M. Kübler, Andrew Arrasmith, Lukasz Cincio, and Patrick J. Coles, "An Adaptive Optimizer for Measurement-Frugal Variational Algorithms", Quantum 4, 263 (2020).

[3] Patrick J. Coles, M. Cerezo, and Lukasz Cincio, "Strong bound between trace distance and Hilbert-Schmidt distance for low-rank states", Physical Review A 100 2, 022103 (2019).

[4] M. Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J. Coles, "Cost-Function-Dependent Barren Plateaus in Shallow Quantum Neural Networks", arXiv:2001.00550.

[5] Andrew Arrasmith, Lukasz Cincio, Rolando D. Somma, and Patrick J. Coles, "Operator Sampling for Shot-frugal Optimization in Variational Algorithms", arXiv:2004.06252.

[6] Kunal Sharma, Sumeet Khatri, M. Cerezo, and Patrick J. Coles, "Noise resilience of variational quantum compiling", New Journal of Physics 22 4, 043006 (2020).

[7] Xin Wang, Zhixin Song, and Youle Wang, "Variational Quantum Singular Value Decomposition", arXiv:2006.02336.

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