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:

• The legacy of Peter Higgs, perseverance and patience as keys to scientific progress

• sp-QCNNs as an optimized alternative to QCNNs

• A study on the code that can improve logical qubit lifetime by 24%

• Plus, how a partnership between Wolfram and providers such as Infleqtion & Q-CTRL among others is innovating quantum computing education

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

TOP NEWS & RESEARCH

NEWS

IN REMEMBERANCE OF PETER HIGGS

Peter Higgs, theoretical physicist best known for his proposal of the Higgs boson, has passed away at age 94. His revolutionary idea, published in 1964, suggested a mechanism by which fundamental particles gain mass. Though his work faced skepticism for decades, it was finally validated in 2012 when the Higgs Boson was detected at CERN’s LHC. The discovery led to Higgs’ award of the Nobel Prize in Physics in 2013.

Peter Higgs’ unwavering confidence in his theory is a great example of the perseverance necessary in pioneering fields, such as quantum computing. As quantum scientists, we should take inspiration from Higgs’ example by steadfastly pursuing innovative theories and research despite skepticism and despite the length of the journey (it will be a long one, after all).

RESEARCH

OVERVIEW OF “SPLITTING AND PARALLELIZING OF QUANTUM CONVOLUTIONAL NEURAL NETWORKS FOR LEARNING TRANSLATIONALLY SYMMETRIC DATA”

The Brief Byte: This study presents sp-QCNNs, split-parallelizing quantum convolutional neural networks, as an optimized alternative to QCNNs. The improved architecture leverages prior knowledge of quantum data to more efficiently lessen statistical errors and accelerate the learning process.

Highlights:

1. The split-parallelizing quantum convolutional neural network addresses the high measurement costs of traditional quantum neural networks by using translational symmetry in quantum data. This approach does not require increasing qubit count, all while improving measurement efficiency.

2. The circuit of the split-parallelizing quantum convolutional neural network features translationally symmetric layers and circuit splitting.

3. The sp-QCNN has high classification performance and the may improve measurement efficiency by a noted factor for quantum phase recognition tasks. This in turn accelerates and stabilizes the learning process in resource-limited settings.

RESEARCH

OVERVIEW OF “AUTONOMOUS QUANTUM ERROR CORRECTION OF GOTTESMAN-KITAEV-PRESKILL STATES”

The Brief Byte: This study demonstrates that the Gottesman-Kitaev-Preskill code can extend logical qubit lifetime through autonomous error correction in a superconducting device.

Highlights:

1. This study centers on enhancing quantum error correction methods focusing on the autonomous error correction of Gottesman-Kitaev-Preskill states using a superconducting device. This approach leverages a feedback-free reset of the auxiliary transmon qubit, showing promise over traditional two-level systems by potentially reducing the modes needed for effective quantum computation.

2. The experimental results demonstrate that the proposed autonomous QEC protocol improves the lifetime of logical qubits using GKP code by 24%.

MORE BRIEF BYTES

• NVIDIA stock as the best investment in QC

• Co-founder of Oxford Ionics talks state of quantum and outlook for the future

• CEA-Leti advances its research through spinoff Quobly, aiming for scalable, fault-tolerant systems

• Series from the Quantum Insider on benefits of participation in quantum computing competitions

• FBI reiterates its commitment to protecting quantum computing technologies from foreign adversaries ahead of World Quantum Day

ENTANGLED INSIGHTS

RECOMMENDED PLATFORM

WOLFRAM QUANTUM FRAMEWORK

The Wolfram Quantum Framework is a toolkit for modeling quantum circuits and designing algorithms, which can also translate these models into formats runnable on quantum hardware via services like: Amazon Braket, Classiq, Infleqtion, Q-CTRL, QuEra, and Strangeworks.

Categories include:

Quantum Circuits

Symbolic Quantum Computation

Time Evolution

Rich Visualization Capabilities

Compute Distances and Entanglements

Interoperability with External Quantum Platforms

EVENTS

• Monday April 15 | Quantinuum Workshop at Yale University (or virtual link available)

• Wednesday April 17 | Making Photons See Each Other featuring Professor Puneet Anantha Murthy of Quantum Center ETH Zurich

• Thursday, April 18 | C2QA Quantum Thursdays w/ Director of Quantum Systems Accelerator Bert de Jong

• Now - May 31 | Register for Google/X-Prize Quantum Challenge

FEATURED JOBS

• Intel Quantum Computing Measurement Engineer | Hillsboro, OR (Hybrid)

• AWS Quantum Research Scientist, Hardware - AWS Center for Quantum Computing | Pasadena, CA $124.1K - $212.8K

• Google Open Source Lead, Staff Software Engineer, Quantum AI | Los Angeles, CA $189K - $284K

• Argonne National Laboratory Postdoctoral Appointee - Quantum Materials | Lemont, IL

• Brookhaven National Laboratory Research Associate for Electron Microscopy Study of Quantum Materials | Upton, NY $70K - $90K

UNTIL TOMORROW.

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