The Daily Qubit

👩‍🔬 Rare Chiral Interface State Captured & QGNNs for QUBOS

Author

Cierra Choucair
April 10, 2024 • Estimated Reading Time: 9 minutes

Welcome to the Quantum Realm. 

Enjoy today’s breakdown of news, research, events & jobs within quantum.

I love to hear from you! Send me a message at [email protected] for musings, for fun, or for insight if it so appeals to you.

IN TODAY’S ISSUE:

  • How an atomic-resolution image is providing unique insight into the future of quantum computing technology

  • QGNNs for QUBOs (and other acronyms you should know)

  • Suggested unifying methodology to bridge the gap between physics communities

  • Plus, a tutorial from PennyLaneAI featuring the D-Wave quantum annealer for the QUBOs you heard about

  • AND check out the poll in the “Featured Jobs” section so I can continue molding this newsletter to your needs

TOP NEWS & RESEARCH

NEWS

FIRST ATOMIC-RESOLUTION IMAGE AND ELECTRICAL CONTROL OF RARE CHIRAL INTERFACE STATE

The Brief Byte: Scientists have captured atomic-resolution images of a unique quantum phenomenon and demonstrated controlled electron flow in a study that has implications for efficient quantum computing.

Highlights:

  1. This rare chiral interface state is a conducting channel that prevents energy-wasting electrical resistance by preventing electrons to be scattered backwards. These are notoriously difficult to visualize which has made it hard to understand their unique properties, but the team at Berkeley Lab has captured these using atomic-resolution for the first time.

  2. In addition to the breakthrough image, the team was also able to demonstrate on-demand creation of the conducting channels in a 2D insulator. The image itself gives researchers more insight into how the channels can be manipulated as well.

  3. The potential benefits for quantum computing include controlling chiral interface states in QAH insulators which would lead to enhanced manipulation of their conductive properties for improved device performance. Additionally, the research group hopes that insights gained from this study could further the understanding of anyons, critical for the development of topological quantum computers.

RESEARCH

OVERVIEW OF “QUANTUM GRAPH OPTIMIZATION ALGORITHM”

Representation of quantum graph | DALL-E

The Brief Byte: This study introduces a novel variational quantum graph optimization algorithm that incorporates message-passing mechanisms as a way to enhance performance in solving QUBO (quantum unconstrained binary optimization) problems, which are important applications in chemistry and finance among other domains.

Highlights:

  1. Quantum computing in the NISQ era has led to the development of hybrid classical-quantum variational quantum algorithms (VQAs) for optimization and learning tasks. These VQAs are mostly used to process Euclidean distance data, but there are situations in which processing non-Euclidean data constrained to graphs would be beneficial, which has led us to quantum graph neural networks (QGNNs).

  2. The research group argues that QGNNs have demonstrated the potential to handle graph-based tasks, and developing a quantum graph optimization algorithm specifically for QUBO problems would enhance the capability of quantum computing. The team’s study is based on leveraging a graph’s geometric properties for efficient information exchange and problem-solving.

  3. While is still in early stages, this paper introduces a quantum graph optimization algorithm leveraging message-passing to address non-Euclidean, graph-related problems, showing significant improvement over traditional methods like QAOA for tasks such as portfolio assignment and minimum vertex cover.

RESEARCH

OVERVIEW OF “CURRENT FLUCTUATIONS IN OPEN QUANTUM SYSTEMS: BRIDGING THE GAP BETWEEN QUANTUM CONTINUOUS MEASUREMENTS AND FULL COUNTING STATISTICS”

The Brief Byte: This tutorial aims to provide a unified approach to describing continuously measured quantum systems, offering a comprehensive toolkit for describing current fluctuations that can be used from quantum optics to condensed-matter physics and everything in between.

Highlights:

  1. The tutorial emphasizes the role of continuously measured quantum systems, where output currents offer insights into the underlying quantum mechanics of the system. These currents are often characterized by stochastic and correlated time series. They provide a comprehensive toolkit for describing these fluctuations, integrating approaches from different physics communities.

  2. It addresses the disconnect between communities in physics, with the divide between quantum opticians and condensed-matter physicists as an example. Despite focusing on similar problems, the two groups employ distinct methodologies and terminologies. The tutorial seeks to unify these approaches under a common framework, demonstrating that seemingly different methods like stochastic master equations and full counting statistics are closely related.

  3. The ultimate success in being able to form a unified approach encourages further collaboration across disciplines to ultimately enhance our understanding of quantum measurements and what they mean for quantum technology and information processing.

MORE BRIEF BYTES

ENTANGLED INSIGHTS

COMMUNITY RESOURCE

PENNYLANEAI TUTORIAL ON SOLVING QUBO PROBLEM USING D-WAVE QUANTUM ANNEALER

Interested in solving QUBO problems using QML? Check out the below GitHub tutorial from PennyLaneAI.

Once you’ve sharpened your skills, check out OpenQAOA for more problems to be solved.

qml/demonstrations/tutorial_QUBO.py at 67d16b8f8630b30e746f2a7407f55a49c84d2597 ¡ PennyLaneAI/qml

Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research. - PennyLaneAI/qml

EVENTS

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