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quantum SCIENCE AND TECHNOLOGY 2023年第3期8卷 035001-035001页

作者： Ezzell, Nic Ball, Elliott M. Siddiqui, Aliza U. Wilde, Mark M. Sornborger, Andrew T. Coles, Patrick J. Holmes, Zoe Univ Southern Calif Dept Phys & Astron Los Angeles CA 90089 USA Los Alamos Natl Lab Informat Sci Los Alamos NM 87545 USA Univ Lancaster Phys Dept Lancaster LA14YB England Louisiana State Univ Hearne Inst Theoret Phys Dept Phys & Astron Baton Rouge LA 70803 USA Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Louisiana State Univ Div Comp Sci & Engn Baton Rouge LA 70803 USA Cornell Univ Sch Elect & Comp Engn Ithaca NY 14850 USA Quantum Sci Ctr Oak Ridge TN 37931 USA Los Alamos Natl Lab Theoret Div Los Alamos NM 87545 USA Ecole Polytech Fed Lausanne EPFL Inst Phys CH-1015 Lausanne Switzerland

The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states which is suitable for near-term hardware. Our algorithm represents a generalization of previous VQAs that aimed at learning preparation circuits for pure states. We consider two different ansatze for compiling the target state;the first is based on learning a purification of the state and the second on representing it as a convex combination of pure states. In both cases, the resources required to store and manipulate the compiled state grow with the rank of the approximation. Thus, by learning a lower rank approximation of the target state, our algorithm provides a means of compressing a state for more efficient processing. As a byproduct of our algorithm, one effectively learns the principal components of the target state, and hence our algorithm further provides a new method for principal component analysis. We investigate the efficacy of our algorithm through extensive numerical implementations, showing that typical random states and thermal states of many body systems may be learnt this way. Additionally, we demonstrate on quantum hardware how our algorithm can be used to study hardware noise-induced states.

关键词： variational quantum algorithm quantum mixed states Hilbert-Schmidt distance mixed state compiling quantum principal component analysis quantum state compression

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quantum neural networks for power flow analysis

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 235卷

作者： Kaseb, Zeynab Moller, Matthias Balducci, Giorgio Tosti Palensky, Peter Vergara, Pedro P. Delft Univ Technol Elect Sustainable Energy Delft Netherlands Delft Univ Technol Appl Math Delft Netherlands Delft Univ Technol Aerosp Struct & Mat Delft Netherlands

This paper explores the potential application of quantum and hybrid quantum-classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus and 33-bus test systems. A systematic performance comparison is also conducted among quantum, hybrid quantum-classical, and classical neural networks. The comparison is based on (i) generalization ability, (ii) robustness, (iii) training dataset size needed, (iv) training error, and (v) training process stability. The results show that the developed hybrid quantum-classical neural network outperforms both quantum and classical neural networks, and hence can improve deep learning-based power flow analysis in the noisy-intermediate-scale quantum (NISQ) and fault-tolerant quantum (FTQ) era.

关键词： Distribution networks Load flow calculation Gate-based quantum computing model Parameterized quantum circuit variational quantum algorithm

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A GNN-based predictor for quantum architecture search

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quantum INFORMATION PROCESSING 2023年第2期22卷 128-128页

作者： He, Zhimin Zhang, Xuefen Chen, Chuangtao Huang, Zhiming Zhou, Yan Situ, Haozhen Foshan Univ Sch Elect & Informat Engn Foshan 528000 Peoples R China Foshan Univ Sch Mechatron Engn & Automat Foshan 528000 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Wuyi Univ Sch Econ & Management Jiangmen 529020 Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China

The performance of the variational quantum algorithm (VQA) highly depends on the structure of the quantum circuit. quantum architecture search (QAS) algorithm aims to automatically search out high-performance quantum circuits for given VQA tasks. However, current QAS algorithms need to calculate the ground-truth performances of a large number of quantum circuits during the searching process, especially for large-scale quantum circuits, which is very time-consuming. In this paper, we propose a predictor based on a graph neural network (GNN), which can largely reduce the computational complexity of the performance evaluation and accelerate the QAS algorithm. We denote the quantum circuit with a directed acyclic graph (DAG), which can well represent the structural and topological information of the quantum circuit. A GNN-based encoder with an asynchronous message-passing scheme is used to encode discrete circuit structures into continuous feature representations, which mimics the computational routine of a quantum circuit on the quantum data. Simulations on the 6-qubit and 10-qubit variational quantum eigensolver (VQE) show that the proposed predictor can learn the latent relationship between circuit structures and their performances. It effectively filters out poorly performing circuits and samples the most promising quantum circuits for evaluation, which avoids a significant computational cost in the performance evaluation and largely improves the sample efficiency.

关键词： quantum architecture search variational quantum algorithm Graph neural network

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Modularized and scalable compilation for double quantum dot quantum computing

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quantum SCIENCE AND TECHNOLOGY 2024年第1期9卷 015004-015004页

作者： He, Run-Hong Xu, Xu-Sheng Byrd, Mark S. Wang, Zhao-Ming Ocean Univ China Coll Phys & Optoelect Engn Qingdao 266100 Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 101408 Peoples R China Tsinghua Univ Dept Phys State Key Lab Low Dimens Quantum Phys Beijing 100084 Peoples R China Southern Illinois Univ Dept Phys Carbondale IL 62901 USA

Any quantum program on a realistic quantum device must be compiled into an executable form while taking into account the underlying hardware constraints. Stringent restrictions on architecture and control imposed by physical platforms make this very challenging. In this paper, based on the quantum variational algorithm, we propose a novel scheme to train an Ansatz circuit and realize high-fidelity compilation of a set of universal quantum gates for singlet-triplet qubits in semiconductor double quantum dots, a fairly heavily constrained system. Furthermore, we propose a scalable architecture for a modular implementation of quantum programs in this constrained systems and validate its performance with two representative demonstrations, the Grover's algorithm for the database searching (static compilation) and a variant of variational quantum eigensolver for the Max-Cut optimization (dynamic compilation). Our methods are potentially applicable to a wide range of physical devices. This work constitutes an important stepping-stone for exploiting the potential for advanced and complicated quantum algorithms on near-term devices.

关键词： semiconductor double quantum dots quantum gate compilation quantum program compilation variational quantum algorithm

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Intel quantum Simulator: a cloud-ready high-performance simulator of quantum circuits

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quantum SCIENCE AND TECHNOLOGY 2020年第3期5卷

作者： Guerreschi, Gian Giacomo Hogaboam, Justin Baruffa, Fabio Sawaya, Nicolas P. D. Intel Labs Hillsboro OR 97124 USA Intel Deutschland GmbH Feldkirchen Germany

Classical simulation of quantum computers will continue to play an essential role in the progress of quantum information science, both for numerical studies of quantum algorithms and for modeling noise and errors. Here we introduce the latest release of Intel quantum Simulator (IQS), formerly known as qHiPSTER. The high-performance computing (HPC) capability of the software allows users to leverage the available hardware resources provided by supercomputers, as well as available public cloud computing infrastructure. To take advantage of the latter platform, together with the distributed simulation of each separate quantum state, IQS allows to subdivide the computational resources to simulate a pool of related circuits in parallel. We highlight the technical implementation of the distributed algorithm and details about the new pool functionality. We also include some basic benchmarks (up to 42 qubits) and performance results obtained using HPC infrastructure. Finally, we use IQS to emulate a scenario in which many quantum devices are running in parallel to implement the quantum approximate optimization algorithm, using particle swarm optimization as the classical subroutine. The results demonstrate that the hyperparameters of this classical optimization algorithm depends on the total number of quantum circuit simulations one has the bandwidth to perform. Intel quantum Simulator has been released open-source with permissive licensing and is designed to simulate a large number of qubits, to emulate multiple quantum devices running in parallel, and/or to study the effects of decoherence and other hardware errors on calculation results.

关键词： quantum simulation cloud computing quantum emulation quantum information variational quantum algorithm particle swarm optimization

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Beyond quantum annealing: optimal control solutions to maxcut problems

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quantum SCIENCE AND TECHNOLOGY 2024年第4期9卷 045013-045013页

作者： Pecci, Giovanni Wang, Ruiyi Torta, Pietro Mbeng, Glen Bigan Santoro, Giuseppe CNR Ist Officina Mat CNR IOM C O SISSA Via Bonomea 265 I-34136 Trieste Italy SISSA Via Bonomea 265 I-34136 Trieste Italy Univ Milan Dipartimento Fis Via Celoria 16 I-20133 Milan Italy Univ Innsbruck Technikerstr 21 A-6020 Innsbruck Austria Par Quantum Comp GmbH A-6020 Innsbruck Austria Abdus Salaam Int Ctr Theoret Phys POB 586 I-34014 Trieste Italy

quantum Annealing (QA) relies on mixing two Hamiltonian terms, a simple driver and a complex problem Hamiltonian, in a linear combination. The time-dependent schedule for this mixing is often taken to be linear in time: improving on this linear choice is known to be essential and has proven to be difficult. Here, we present different techniques for improving on the linear-schedule QA along two directions, conceptually distinct but leading to similar outcomes: 1) the first approach consists of constructing a Trotter-digitized QA (dQA) with schedules parameterized in terms of Fourier modes or Chebyshev polynomials, inspired by the Chopped Random Basis algorithm for optimal control in continuous time;2) the second approach is technically a quantum Approximate Optimization algorithm (QAOA), whose solutions are found iteratively using linear interpolation or expansion in Fourier modes. Both approaches emphasize finding smooth optimal schedule parameters, ultimately leading to hybrid quantum-classical variational algorithms of the alternating Hamiltonian Ansatz type. We apply these techniques to MaxCut problems on weighted 3-regular graphs with N = 14 sites, focusing on hard instances that exhibit a small spectral gap, for which a standard linear-schedule QA performs poorly. We characterize the physics behind the optimal protocols for both the dQA and QAOA approaches, discovering shortcuts to adiabaticity-like dynamics. Furthermore, we study the transferability of such smooth solutions among hard instances of MaxCut at different circuit depths. Finally, we show that the smoothness pattern of these protocols obtained in a digital setting enables us to adapt them to continuous-time evolution, contrarily to generic non-smooth solutions. This procedure results in an optimized QA schedule that is implementable on analog devices.

关键词： quantum annealing quantum optimal control variational quantum algorithm shortcut to adiabaticity

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Hardware efficient decomposition of the Laplace operator and its application to the Helmholtz and the Poisson equation on quantum computer

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quantum INFORMATION PROCESSING 2024年第7期23卷 270-270页

作者： Bae, Jaehyun Yoo, Gwangsu Nakamura, Satoshi Ohnishi, Shota Kim, Dae Sin Samsung Elect Innovat Ctr Computat Sci & Engn CSE Team Hwaseong 18448 South Korea Samsung Devise Solut R&D Japan Proc TCAD Lab 2-7Sugasawa-ChoTsurumi Ku Yokohama Japan

With the rapid advancement of quantum computers in the past few years, there is ongoing development of algorithms aimed at solving problems that are difficult to tackle with classical computers. A pertinent instance of this is the resolution of partial differential equations (PDEs), where a current trend involves the exploration of variational quantum algorithms (VQAs) tailored to efficiently function on the noisy intermediate-scale quantum (NISQ) devices. Recently, VQAs for solving the Poisson equation have been proposed, and these algorithms require highly entangled quantum states or specific types of qubit entanglement to compute the expectation value of the Laplace operator. Implementing such requirements on NISQ devices poses a significant challenge. To overcome this problem, we propose a new method for representing the Laplace operator in the finite difference formulation. Since the quantum circuits introduced for evaluating the expectation value of the Laplace operator through proposed method do not require processes that degrade the fidelity of computation, such as swap operations or generation of highly entangled states, they can be easily implemented on NISQ devices. In the regime of quantum supremacy (the number of qubits is approximately 50), our proposed approach necessitates approximately one-third fewer CNOT operations compared to conventional methods. To assess the effectiveness of the proposed method, we conduct computations for finding the eigenvalues of the Helmholtz equation and solving the Poisson equation on cloud-based quantum hardware. We calculate the fidelity of quantum states required for each method through quantum tomography and also estimate the fidelity in the quantum supremacy regime. We believe that the proposed method can be applied to other PDEs having the Laplace operator and greatly assists in solving them.

关键词： Partial differential equation variational quantum algorithm Laplace operator decomposition Finite difference method

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Molecular electronic structure calculation via a quantum computer

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COMPUTATIONAL AND THEORETICAL CHEMISTRY 2024年 1242卷

作者： Naeij, Hamid Reza Mahmoudi, Erfan Yeganeh, Hossein Davoodi Akbari, Mohsen Iranian Quantum Technol Res Ctr IQTEC Tehran Iran Kharazmi Univ Dept Phys Quantum Opt Lab Tehran Iran

quantum computers can be used to calculate the electronic structure and estimate the ground state energy of many-electron molecular systems. In the present study, we implement the variational quantum Eigensolver (VQE) algorithm, as a hybrid quantum-classical algorithm to calculate the ground state energy of the molecules such as H + 3 , OH-, HF and BH3 in which the number of qubits has an increasing trend. We use the parity transformation for fermion to qubit encoding and the Unitary Coupled Cluster for Single and Double excitations (UCCSD) to construct an ansatz. We compare our quantum simulation results with the computational chemistry approaches including Full Configuration Interaction (FCI), as benchmark energy and Unrestricted Hartree- Fock (UHF), as a common computational method. Our results show that there is a good agreement between molecular ground state energy obtained from VQE and FCI. Moreover, the accuracy of the ground state energies obtained from VQE in our work is higher than the previously reported values. This work aims to benchmark the VQE algorithm to calculate the electronic ground state energy for a new set of molecules that can be good candidates for molecular simulation on areal quantum computer.

关键词： quantum computer variational quantum algorithm Ansatz Ground state energy

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A Brief Survey of quantum Architecture Search 22

A Brief Survey of Quantum Architecture Search

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Proceedings of the 6th International Conference on algorithms, Computing and Systems

作者： Weiwei Zhu Jiangtao Pi Qiuyuan Peng Sichuan Branch of National Computer Network Emergency Response Technique Team/Coordination Center of China China Chengdu Internet Information Center China Sichuan Branch of National Computer Network Emergency Response Technique Team/Coordination Center of China. China

ISBN: (纸本)9781450397407

With the rapid development of quantum computing, the variational quantum algorithms capitalize on the classical optimizer and parametrized quantum circuit to provide outperformance on specific tasks such as combinatorial optimization problems. However, the performance of these hybrid quantum-classical algorithms heavily relies on the design of quantum circuit architecture. Being restricted by the noise of the near-term quantum device, how to trade off the computational power of quantum circuits and the noise of quantum gates is a challenging task for design circuit architecture. In this paper, we give a brief view of the recently proposed methods for quantum architecture search including the differentiable circuit search method, deep reinforcement learning based method, and evolutionary based methods.

关键词： variational quantum algorithm combinatorial optimization quantum architecture search

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