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🤖 Microsoft Just Catapulted Us To The Next Era
Welcome to the Quantum Realm.
It is a fantastic day in the world of quantum computing, unless Microsoft is your competitor. Power-couple Microsoft & Quantinuum just validated that the quantum era is indeed upon us. Even the skeptics will shed a tear today.
The duo ran more than 14K individual experiments using a combination of Quantinuum’s ion-trap quantum computer and Microsoft’s in-house qubit-virtualization system and the result.
Microsoft declares that we are no longer in the NISQ era, but one step closer to quantum advantage. Read more below.
Otherwise, enjoy today’s news and send me a message at [email protected] for musings, for fun, or for insight if it so appeals to you.
Cheers,
Cierra
IN TODAY’S ISSUE:
That thing you’ll see all over your newsfeed today — Microsoft & Quantinuum’s breakthrough error rates
Affordable QML via Amazon Braket
A novel approach for analyzing strongly correlated systems using quantum circuits
Plus, how to simulate depolarization using Cirq
TOP NEWS & RESEARCH
NEWS
Breakthrough Logical Qubit Error Rates from Microsoft & Quantinuum
The Brief Byte: Microsoft & Quantinuum have achieved a groundbreaking milestone by demonstrating logical qubits with an error rate 800 times lower than that of physical qubits. This is a pivotal moment in quantum computing as it represents the shift away from the NISQ era towards fault-tolerant quantum computers.
Highlights:
Demonstrated entanglement of logical qubit states using the [[7, 1, 3]] Steane code and a [[12, 2, 4]] Carbon code with error rates significantly lower than physical levels
Showed repeated error correction with the [[12, 2, 4]] code, marking a notable step away from NISQ computing towards Resilient computing.
Built on Daniel Gottesman’s proposal for benchmarking quantum circuits by contrasting classical outputs from unencoded physical circuits to those from fault-tolerantly encoded circuits on the same hardware, using a modified metric that leverages simplified analysis to preserve resources and reduce uncertainty. Read more on Gottesman’s proposal here.
Performed demonstrations on Quantinuum’s shuttling-based trapped-ion QCCD (H2) which features top of the line QEC. For commercial use, H2 processor will be available through both Quantinuum and Azure Quantum.
Used [[7, 1, 3]] Steane code and a [[12, 2, 4]] Carbon code to demonstrate the lower logical error rates. More on QEC codes here.
Azure Quantum Elements customers will be the first to get a private preview in the coming months of the advanced capabilities stemming from these logical qubits. Sign up for Azure Quantum Cloud service here.
NEWS
Amazon Braket’s Cost-Effective Hyperparameter Optimization for QML
The Brief Byte: From the AWS Quantum Technologies blog, here is an implementation of a hybrid quantum-classical machine learning algorithm with hyperparameter optimization on Amazon Braket, focused on reproducibility and cost-effective development.
Highlights:
Utilize Amazon Braket for developing a variational quantum algorithm tailored for image classification, employing hyperparameter optimization to refine the algorithm's efficiency.
Learn how to implement a three-step cycle in Braket notebooks for quick testing, scaling through hybrid jobs for optimal hyperparameter discovery, and final verification on QPUs.
Showcases a cost-effective approach to quantum machine learning development that doesn’t require an enterprise budget
RESEARCH
Summary of Utilizing Quantum Processor for the Analysis of Strongly Correlated Materials
Representation of a Hadamard Test Circuit
The Brief Byte: This study introduces a novel approach for analyzing strongly correlated systems using quantum circuits, which has the potential to unlock new possibilities in both condensed matter physics and quantum processors.
Highlights:
Emphasizes the importance of the cluster Green's function in understanding local interactions within strongly correlated systems
Introduces a streamlined process for calculating the cluster's Green's function on quantum circuits, using VQE to determine the system's ground state. This approach significantly simplifies computations on quantum circuits..
Discusses preliminary results on the Hubbard model, including ground state energy, Green’s function, and one-particle excitation spectra. These results demonstrate the potential of quantum computing to unlock new physics in condensed matter.
MORE BRIEF BYTES
Congresswoman Elise Stefanik introduces the Defense Quantum Acceleration Act to boost the DoD's acceleration towards quantum technology
NSA calls for collaboration between industry and government to prepare for quantum, highlighting the need for low barrier to entry
.Cryptographers urged to have agility as quantum technology progress intensifies; encouraged to not assume we are safe from post-NISQ era security concerns
PsiQuantum plans to transform two industrial sites in Chicagoland into a quantum computing hub
Universities are adding new certificate programs and collaborative research initiatives to prepare students for future technological advancements
ENTANGLED INSIGHTS
TOOL TIP
Representing Noise with Cirq
Quantum computing simulators, such as Google's open-source Cirq, are particularly valuable for simulating real-world, noisy conditions that reflect the environmental interference and qubit quality limitations faced by actual quantum systems.
Today, we'll demonstrate one way to simulate depolarization with Cirq, which indicates an error where qubit’s state becomes randomized and the original state is diluted.
# Define a line qubit
q0 = cirq.LineQubit(0)
# Create circuit by applying an initial Hadamard gate, a 30% depolarizing channel, and then a second Hadamard gate. Measure qubit state and assign to 'results'.
circuit = cirq.Circuit(
cirq.H(q0),
cirq.depolarize(p=0.3).on(q0),
cirq.H(q0),
cirq.measure(q0, key='results')
)
# Simulate the circuit with reproducibility, execute simulation 500 times
#TIP: Increase number of simulations for a clearer picture of expected results. 100 - 500 might be best for rapid prototyping where computational resources are limited
results = cirq.Simulator(seed=0).run(circuit, repetitions=500)
# Create a histogram with results to see distribution of measurement outcomes
print(results.histogram(key='results'))
More noise representation breakdowns coming this week!
EVENTS
Friday, April 5 | Quantum Computing vs Cybersecurity by Quarks Interactive & OctogonHUB
Sunday, April 7 | FREE Quantum Computing Workshop by Classiq
Now | Register for unitaryHack 2024
Now - April 21 | Register for NATO Women & Girls in Science Challenge
Now - April 30 | Register for Airbus & BMW Quantum Computing Challenge
Now - May 31 | Register for Google/X-Prize Quantum Challenge
FEATURED JOBS
Quantum Futures Quantum Algorithms Researcher | Remote $125K - $180K
Orion Group Quantum Scientist | Virginia, US $81.2K - $146.9K
The University of Texas at Austin Quantum Sensing Research Associate | Austin, TX $110K - $155K
Google Software Engineer, Quantum Error Correction, Quantum AI | Los Angeles, CA $136K - $200K
SandboxAQ Senior Technical Program Manager, Simulation | Remote
Leidos Quantum Scientist | Arlington, VA $81.2K - $146.8K
UNTIL TOMORROW.
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