Papers

A. Bonomi, Thomas De Min, Enrico Zardini +3 more·Feb 1, 2022

This paper presents the details and testing of two implementations (in C++ and Python) of the hybrid quantum-classical algorithm Quantum Annealing Learning Search (QALS) on a D-Wave quantum annealer. QALS was proposed in 2019 as a novel technique to ...

Zongkang Zhang, Yongdan Yang, Xiaosi Xu +1 more·Jan 31, 2022

The Schrieffer-Wolff transformation aims to solve degenerate perturbation problems and give an effective Hamiltonian that describes the low-energy dynamics of the exact Hamiltonian in the low-energy subspace of unperturbed Hamiltonian. This unitary t...

Priyanka Batra, M. H. Ram, T. S. Mahesh·Jan 29, 2022

Quantum control optimization algorithms are routinely used to generate optimal quantum gates or efficient quantum state transfers. However, there are two main challenges in designing efficient optimization algorithms, namely overcoming the sensitivit...

F. Metz, M. Bukov·Jan 27, 2022

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body syst...

Anh-Dzung Doan, M. Sasdelli, D. Suter +1 more·Jan 25, 2022

Fitting geometric models onto outlier contaminated data is provably intractable. Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do not provide optimality guarantees and error bounds. It is therefore cri...

Joel Bierman, Yingzhou Li, Jianfeng Lu·Jan 20, 2022

We propose a quantum-classical hybrid variational algorithm, the quantum orbital minimization method (qOMM), for obtaining the ground state and low-lying excited states of a Hermitian operator. Given parametrized ansatz circuits representing eigensta...

Xiaozhen Ge, R. Wu, H. Rabitz·Jan 19, 2022

This review investigates the landscapes of hybrid quantum-classical optimization algorithms that are prevalent in many rapidly developing quantum technologies, where the objective function is computed by either a natural quantum system or an engineer...

O. M. Raisuddin, S. De·Jan 19, 2022

: The solution of physical problems discretized using the finite element methods using quantum computers remains relatively unexplored. Here, we present a unified formulation (FEqa) to solve such problems using quantum annealers. FEqa is a hybrid tec...

Fang Gao, Guojian Wu, Suhang Guo +2 more·Jan 13, 2022

Power flow calculation plays an important role in planning, operation, and control of the power system. The quantum HHL algorithm can achieve theoretical exponential speedup over classical algorithms on DC power flow calculation. Since the qubit reso...

Xing Ai, Zhihong Zhang, Luzhe Sun +2 more·Jan 13, 2022

Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an effective means to map real-world data from Euclidean space to Hilbert sp...

A. Anaya, F. Delgado·Jan 8, 2022

Introduction Feynman’s ideas to employ quantum systems for simulating other quantum systems [1] gave rise to quantum simulation [2]. While current quantum computers are still prone to decoherence and rely on error correction [3], the development of h...

D. Huerga·Jan 7, 2022

We introduce a hybrid quantum-classical variational algorithm to simulate ground-state phase diagrams of frustrated quantum spin models in the thermodynamic limit. The method is based on a cluster-Gutzwiller ansatz where the wave function of the clus...

Reza Mahroo, A. Kargarian·Jan 7, 2022

This paper proposes a hybrid quantum-classical algorithm to solve a fundamental power system problem called unit commitment (UC). The UC problem is decomposed into a quadratic subproblem, a quadratic unconstrained binary optimization (QUBO) subproble...

A. S. Arora, A. Gheorghiu, Uttam Singh·Jan 6, 2022

An important theoretical problem in the study of quantum computation, that is also practically relevant in the context of near-term quantum devices, is to understand the computational power of hybrid models, that combine poly-time classical computati...

S. Kempkes, P. Capiod, S. Ismaili +10 more·Jan 6, 2022

Zero-energy modes localized at the ends of one-dimensional (1D) wires hold great potential as qubits for fault-tolerant quantum computing. However, all the candidates known to date exhibit a wave function that decays exponentially into the bulk and h...

Davis Arthur, Prasanna Date·Jan 5, 2022

Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited by the current hardware used to train them. These concerns have become ...

Tong Dou, Guofeng Zhang, W. Cui·Jan 4, 2022

Recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as general function approximators. In this work, we propose a quantum-classical deep network structure to enhance classical CNN model discriminability. The convolu...

N. Guseynov, W. Pogosov·Dec 30, 2021

We consider a hybrid digital–analog quantum computing approach, which allows implementing any quantum algorithm without standard two-qubit gates. This approach is based on the always-on interaction between qubits, which can provide an alternative to ...

T. Shirakawa, H. Ueda, S. Yunoki·Dec 29, 2021

We introduce an quantum-classical hybrid algorithm, named automatic quantum circuit encoding (AQCE), that is designed to encode an arbitrary quantum state |Ψ〉 onto an optimal quantum circuit Ĉ composed of a finite set of single- and two-qubit quantu...

Jingwei Wen, Zheng-an Wang, Chi-Tong Chen +7 more·Dec 28, 2021

Utilizing quantum computer to investigate quantum chemistry is an important research field nowadays. In addition to the ground-state problems that have been widely studied, the determination of excited-states plays a crucial role in the prediction an...