Table of Contents

• 👩‍💻 Brief Bytes: The news you want, quickly

• 🔬 Research Spotlight: Today's top research

• 📚 Entangled Insights: Level up your skills

• 📅 Events: Webinars, Conferences, Hackathons

• 💼 Jobs: In & Around Quantum

• 🗳️ Survey: Tell Us What You Think!

• 😎 Share: Tell Your Friends We're Cool

QUANTUM PULSE

BRIEF BYTES

• China surpasses the US in quantum computing development patents

• Quantum Tetris, anyone? UCL’s 97% accuracy in atom placement demonstrates potential for industrial-scale qubit arrays.

• Quantum dot qubits can function at temps around 1K, lowering operating costs. Because if your quantum dots can't handle the heat, they should stay out of the chip.

• IonQ adds Dr. Martin Roetteler (ex-Microsoft, ex-NEC) to its roster, aiming to make quantum computers that don’t just sound cool but actually do cool stuff.

• Study combines scanning tunneling microscopy with electron microscopy to analyze disorder-induced qubit variability

RESEARCH SPOTLIGHT

On Optimizing Hyperparameters for Quantum Neural Networks

🧪 Tell Me Quickly: The study focuses on identifying key hyperparameters affecting quantum machine learning model performance.

🧪 The How:

1. Utilizing four classical datasets, the research highlights the impact of optimizers and initialization methods on QML models, emphasizing the importance of COBYLA and SPSA optimizers and beta distribution for initialization.

2. QNNs on NISQ tech face challenges such as barren plateaus, which the study addresses through strategic hyperparameter selection.

3. The research provides a comprehensive dataset on QML model performance, offering valuable insights for researchers and practitioners in the field.

🧪 The Why: This study sheds light on the dynamics of hyperparameter influence on QML models. With QML on its way to revolutionize computing by overcoming current hardware limitations, understanding and improving model performance through hyperparameter optimization is crucial for advancing the field.

QUANTUM LAB

ENTANGLED INSIGHTS

Incorporating Noise Models

If you really want to role-play quantum computing engineer, consider incorporating noise models into your quantum circuits to simulate the imperfections found in actual quantum hardware. This will provide a more accurate picture of how your algorithms will perform outside of the idealized simulation environment.

Qiskit offers tools to create and apply noise models based on the characteristics of actual quantum hardware or custom specifications. See below code snippet for an example:

# Create an empty noise model
noise_model = NoiseModel()

# Add depolarizing error (error rate, number of qubits) to u3 gate
dep_error = depolarizing_error(0.05, 1) 
noise_model.add_all_qubit_quantum_error(dep_error, ['u3'])

# Print noise model info
print(noise_model)

Noise Model Tips:

1. Start with predefined noise models from real devices for a baseline, then adjust.

2. Pay attention to the trade-off between simulation time and noise detail level. More complex noise models increase the realism…but also the computation time.

3. Regularly update your noise models to reflect improvements in quantum hardware and error rates.

QUANTUM HAPPENINGS

EVENTS

• Today, March 28, 11:00am - 12:00pm CST | FREE Interactive Interview w/ Director of Q-NEXT by Brookhaven National Laboratory

• Today, March 28, 12:00pm - 1:00pm CST | FREE Inside MIT xPRO’s Quantum Computing Online Certificate Program by MIT xPro

JOBS

• IBM Quantum Computing Scientist | San Jose, CA $145K - $219K

• AWS Quantum Research Scientist | Pasadena, CA $124.1K - $212.8K

• Air Force Quantum Physicist | Albuquerque, NM

• USRA Associate Scientist, Quantum | Mountain View, CA

• Argonne Natl Laboratory Postdoctoral Appointee Quantum Materials | Lemont, IL

SUPPORT SCIENCE

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