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Quantum compiling aims to construct a quantum circuit V by quantum gates drawn from a native gate alphabet, which is functionally equivalent to the target unitary U. It is a crucial stage for the running of quantum algorithms on noisy…

Quantum Physics · Physics 2021-03-23 Zhimin He , Lvzhou Li , Shenggen Zheng , Yongyao Li , Haozhen Situ

Quantum dots (QDs) made from semiconductors are among the most promising platforms for the developments of quantum computing and simulation chips, and have advantages over other platforms in high density integration and in compatibility to…

Mesoscale and Nanoscale Physics · Physics 2022-08-22 Jingwei Mu , Shaoyun Huang , Zhi-Hai Liu , Weijie Li , Ji-Yin Wang , Dong Pan , Guang-Yao Huang , Yuanjie Chen , Jianhua Zhao , H. Q. Xu

A quantum circuit transformation (QCT) is required when executing a quantum program in a real quantum processing unit (QPU). Through inserting auxiliary SWAP gates, a QCT algorithm transforms a quantum circuit to one that satisfies the…

Quantum Physics · Physics 2022-06-03 Xiangzhen Zhou , Yuan Feng , Sanjiang Li

In this paper, we introduce the design approach of integrated photonic devices by employing reinforcement learning known as attractor selection. Here, we combined three-dimensional finite-difference time-domain method with attractor…

The design of scalable quantum computers will benefit from predictive models for qubit performance that consider the design and layout of the qubit devices. This approach, has recently been adopted for superconducting qubits, but has…

Mesoscale and Nanoscale Physics · Physics 2022-09-27 A Ciocoiu , M Khalifa , J Salfi

Quantum architecture search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms. The framework finds a well-suited problem-specific structure of a variational ansatz. Among possible…

Quantum Physics · Physics 2025-12-01 Mikhail Sergeev , Georgii Paradezhenko , Daniil Rabinovich , Vladimir V. Palyulin

Quantum annealing processors typically control qubits in unison, attenuating quantum fluctuations uniformly until the applied system Hamiltonian is diagonal in the computational basis. This simplifies control requirements, allowing…

The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, breakthroughs in artificial intelligence offer opportunities to accelerate this…

Machine Learning · Computer Science 2023-08-16 Qinghe Gao , Artur M. Schweidtmann

Recent years have seen unprecedented advance in the design and control of quantum computers. Nonetheless, their applicability is still restricted and access remains expensive. Therefore, a substantial amount of quantum algorithms research…

Quantum Physics · Physics 2020-12-11 Thomas Grurl , Richard Kueng , Jürgen Fuß , Robert Wille

The practical realization of quantum programs that require large-scale qubit systems is hindered by current technological limitations. Distributed Quantum Computing (DQC) presents a viable path to scalability by interconnecting multiple…

This study presents a novel computer system performance optimization and adaptive workload management scheduling algorithm based on Q-learning. In modern computing environments, characterized by increasing data volumes, task complexity, and…

Machine Learning · Computer Science 2024-11-11 Pochun Li , Yuyang Xiao , Jinghua Yan , Xuan Li , Xiaoye Wang

The execution of quantum circuits on real systems has largely been limited to those which are simply time-ordered sequences of unitary operations followed by a projective measurement. As hardware platforms for quantum computing continue to…

Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum…

Quantum Physics · Physics 2026-01-28 Marin Bukov , Florian Marquardt

Understanding the power and limitations of quantum access to data in machine learning tasks is primordial to assess the potential of quantum computing in artificial intelligence. Previous works have already shown that speed-ups in learning…

Quantum Physics · Physics 2023-07-21 Sofiene Jerbi , Arjan Cornelissen , Māris Ozols , Vedran Dunjko

Quantum computers may outperform classical computers on machine learning tasks. In recent years, a variety of quantum algorithms promising unparalleled potential to enhance, speed up, or innovate machine learning have been proposed. Yet,…

We implement silicon quantum dot devices with two layers of gate electrodes using a self-alignment technique, which allows for ultra-small gate lengths and intrinsically perfect layer-to-layer alignment. In a double quantum dot system, we…

Quantum machine learning is among the most exciting potential applications of quantum computing. However, the vulnerability of quantum information to environmental noises and the consequent high cost for realizing fault tolerance has…

Silicon is a leading qubit platform thanks to the exceptional coherence times that can be achieved and to the available commercial manufacturing platform for integration. Building scalable quantum processing architectures relies on accurate…

Mesoscale and Nanoscale Physics · Physics 2021-07-21 B. Voisin , J. Salfi , R. Rahman , S. Rogge

Variational quantum circuits are one of the promising ways to exploit the advantages of quantum computing in the noisy intermediate-scale quantum technology era. The design of the quantum circuit architecture might greatly affect the…

Quantum Physics · Physics 2024-05-14 Gang Wang , Bang-Hai Wang , Shao-Ming Fei

Recent advances in quantum computing have drawn considerable attention to building realistic application for and using quantum computers. However, designing a suitable quantum circuit architecture requires expert knowledge. For example, it…

Quantum Physics · Physics 2021-04-19 En-Jui Kuo , Yao-Lung L. Fang , Samuel Yen-Chi Chen