@article {3021, title = {Computational self-testing of multi-qubit states and measurements}, year = {2022}, month = {1/31/2022}, abstract = {

Self-testing is a fundamental technique within quantum information theory that allows a classical verifier to force (untrusted) quantum devices to prepare certain states and perform certain measurements on them. The standard approach assumes at least two spatially separated devices. Recently, Metger and Vidick [Quantum, 2021] showed that a single EPR pair of a single quantum device can be self-tested under standard computational assumptions. In this work, we generalize their techniques to give the first protocol that self-tests N EPR pairs and measurements in the single-device setting under the same computational assumptions. We show that our protocol can be passed with probability negligibly close to 1 by an honest quantum device using poly(N) resources. Moreover, we show that any quantum device that fails our protocol with probability at most ϵ must be poly(N,ϵ)-close to being honest in the appropriate sense. In particular, a simplified version of our protocol is the first that can efficiently certify an arbitrary number of qubits of a cloud quantum computer, on which we cannot enforce spatial separation, using only classical communication.

}, keywords = {FOS: Physical sciences, Quantum Physics (quant-ph)}, doi = {10.48550/ARXIV.2201.13430}, url = {https://arxiv.org/abs/2201.13430}, author = {Fu, Honghao and Daochen Wang and Zhao, Qi} } @article {3205, title = {Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning}, year = {2022}, month = {4/14/2022}, abstract = {

Efficient quantum compiling tactics greatly enhance the capability of quantum computers to execute complicated quantum algorithms. Due to its fundamental importance, a plethora of quantum compilers has been designed in past years. However, there are several caveats to current protocols, which are low optimality, high inference time, limited scalability, and lack of universality. To compensate for these defects, here we devise an efficient and practical quantum compiler assisted by advanced deep reinforcement learning (RL) techniques, i.e., data generation, deep Q-learning, and AQ* search. In this way, our protocol is compatible with various quantum machines and can be used to compile multi-qubit operators. We systematically evaluate the performance of our proposal in compiling quantum operators with both inverse-closed and inverse-free universal basis sets. In the task of single-qubit operator compiling, our proposal outperforms other RL-based quantum compilers in the measure of compiling sequence length and inference time. Meanwhile, the output solution is near-optimal, guaranteed by the Solovay-Kitaev theorem. Notably, for the inverse-free universal basis set, the achieved sequence length complexity is comparable with the inverse-based setting and dramatically advances previous methods. These empirical results contribute to improving the inverse-free Solovay-Kitaev theorem. In addition, for the first time, we demonstrate how to leverage RL-based quantum compilers to accomplish two-qubit operator compiling. The achieved results open an avenue for integrating RL with quantum compiling to unify efficiency and practicality and thus facilitate the exploration of quantum advantages.

}, keywords = {FOS: Computer and information sciences, FOS: Physical sciences, Machine Learning (cs.LG), Quantum Physics (quant-ph)}, doi = {10.48550/ARXIV.2204.06904}, url = {https://arxiv.org/abs/2204.06904}, author = {Chen, Qiuhao and Du, Yuxuan and Zhao, Qi and Jiao, Yuling and Lu, Xiliang and Wu, Xingyao} } @article {3192, title = {A theory of quantum differential equation solvers: limitations and fast-forwarding}, year = {2022}, month = {11/9/2022}, abstract = {

We study the limitations and fast-forwarding of quantum algorithms for solving linear ordinary differential equation (ODE) systems with particular focus on non-quantum dynamics, where the coefficient matrix in the ODE is not anti-Hermitian or the ODE is inhomogeneous. On the one hand, for generic homogeneous linear ODEs, by proving worst-case lower bounds, we show that quantum algorithms suffer from computational overheads due to two types of {\textquoteleft}{\textquoteleft}non-quantumness\&$\#$39;\&$\#$39;: real part gap and non-normality of the coefficient matrix. We then show that ODEs in the absence of both types of {\textquoteleft}{\textquoteleft}non-quantumness\&$\#$39;\&$\#$39; are equivalent to quantum dynamics, and reach the conclusion that quantum algorithms for quantum dynamics work best. We generalize our results to the inhomogeneous case and find that existing generic quantum ODE solvers cannot be substantially improved. To obtain these lower bounds, we propose a general framework for proving lower bounds on quantum algorithms that are amplifiers, meaning that they amplify the difference between a pair of input quantum states. On the other hand, we show how to fast-forward quantum algorithms for solving special classes of ODEs which leads to improved efficiency. More specifically, we obtain quadratic to exponential improvements in terms of the evolution time T and the spectral norm of the coefficient matrix for the following classes of ODEs: inhomogeneous ODEs with a negative definite coefficient matrix, inhomogeneous ODEs with a coefficient matrix having an eigenbasis that can be efficiently prepared on a quantum computer and eigenvalues that can be efficiently computed classically, and the spatially discretized inhomogeneous heat equation and advection-diffusion equation. We give fast-forwarding algorithms that are conceptually different from existing ones in the sense that they neither require time discretization nor solving high-dimensional linear systems.

}, keywords = {FOS: Mathematics, FOS: Physical sciences, Numerical Analysis (math.NA), Quantum Physics (quant-ph)}, doi = {10.48550/ARXIV.2211.05246}, url = {https://arxiv.org/abs/2211.05246}, author = {Dong An and Liu, Jin-Peng and Wang, Daochen and Zhao, Qi} }