aqora

How to ensure that quantum technologies continue thriving

Tushna Commissariat

Quantum physicist Mauro Paternostro shares his views on the most exciting quantum breakthroughs, its intersection with AI, and his vision of the quantum future.

Physics WorldJun 1, 20250 citations

Quantum-Classical Hybrid Architectures and How They are Solving Scientific Problems- A Short Review

Haris Manzoor Qazi, Asif Ali Banka

Jan 1, 20251 citation

Quantum Computing Architecture: Integration and Optimization Across the Stack

Kaiwen Gui

Quantum computing is a revolutionary approach to computation that leverages the principles of quantum mechanics, enabling computers to process information in fundamentally different ways than classical computers. Unlike traditional bits, which represent data as either 0s or 1s, quantum bits, or qubits, can exist in multiple states simultaneously due to superposition. Additionally, qubits can be entangled, allowing them to be interconnected in ways that enhance computational power and efficiency. This unique behavior has the potential to run certain types of computation tasks much faster than classical computers can, marking a significant leap forward in technology. When building actual quantum computers to run practical quantum algorithms, one needs efficient ways to map the algorithms to the hardware components, similar to compiling classical algorithms onto CPU/GPU hardware. This thesis aims to tackle some of the important problems in the mapping process. First, can we address hardware errors and algorithm errors at the same time? Second, can we build a better circuit complexity metric to inspire better quantum architecture design, and can we design better quantum algorithms and compilation techniques using this improved metric? Lastly, can we use physics-level simulation results to inspire better circuit-level optimization techniques? We hope to provide positive answers to all three questions above through three separate architectural innovations.Quantum computing is a revolutionary approach to computation that leverages the principles of quantum mechanics, enabling computers to process information in fundamentally different ways than classical computers. Unlike traditional bits, which represent data as either 0s or 1s, quantum bits, or qubits, can exist in multiple states simultaneously due to superposition. Additionally, qubits can be entangled, allowing them to be interconnected in ways that enhance computational power and efficiency. This unique behavior has the potential to run certain types of computation tasks much faster than classical computers can, marking a significant leap forward in technology. When building actual quantum computers to run practical quantum algorithms, one needs efficient ways to map the algorithms to the hardware components, similar to compiling classical algorithms onto CPU/GPU hardware. This thesis aims to tackle some of the important problems in the mapping process. First, can we address hardware errors and algorithm errors at the same time? Second, can we build a better circuit complexity metric to inspire better quantum architecture design, and can we design better quantum algorithms and compilation techniques using this improved metric? Lastly, can we use physics-level simulation results to inspire better circuit-level optimization techniques? We hope to provide positive answers to all three questions above through three separate architectural innovations.

Dec 1, 20240 citations

New Trends and Platforms for Quantum Technologies

Ramón Aguado, R. Citro, Maciej Lewenstein +1 more

Lecture notes in physicsJan 1, 202417 citations

Quantum Neuro-Morphic Computing Architecture

Jincheng Zhang

This paper proposes a novel quantum neuro-morphic computing architecture designed to enhance computational efficiency and parallelism. The core concept involves integrating quantum logic gates with neural networks to create a hybrid system capable of leveraging the strengths of both paradigms. We explore the potential of this architecture to achieve significant improvements in computational performance, particularly for complex problems that benefit from parallel processing and quantum phenomena. The architecture is predicated on a fundamentally new approach to computation, moving beyond traditional von Neumann architectures and exploring the synergistic relationship between quantum and neuromorphic computing. The primary goal is to develop a scalable and efficient computing solution that can tackle problems currently intractable for either quantum or classical systems alone. This paper outlines the key components of the architecture, the underlying mathematical framework, and the potential applications of this integrated system.This paper proposes a novel quantum neuro-morphic computing architecture designed to enhance computational efficiency and parallelism. The core concept involves integrating quantum logic gates with neural networks to create a hybrid system capable of leveraging the strengths of both paradigms. We explore the potential of this architecture to achieve significant improvements in computational performance, particularly for complex problems that benefit from parallel processing and quantum phenomena. The architecture is predicated on a fundamentally new approach to computation, moving beyond traditional von Neumann architectures and exploring the synergistic relationship between quantum and neuromorphic computing. The primary goal is to develop a scalable and efficient computing solution that can tackle problems currently intractable for either quantum or classical systems alone. This paper outlines the key components of the architecture, the underlying mathematical framework, and the potential applications of this integrated system.

Aug 29, 20260 citations

An Overview of the Latest Quantum Technologies and Their Accessibility for Science

Przemysław Duniec, Mariusz Zubert, Krzysztof Hałagan

International Conference Mixed Design of Integrated Circuits and SystemsJun 1, 20260 citations

Quantum leap

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Quantum leap

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