The Daily Qubit

🤖 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:

  1. 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

  2. Showed repeated error correction with the [[12, 2, 4]] code, marking a notable step away from NISQ computing towards Resilient computing.

  3. 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.

  4. 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.

  5. 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:

  1. Utilize Amazon Braket for developing a variational quantum algorithm tailored for image classification, employing hyperparameter optimization to refine the algorithm's efficiency.

  2. 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.

  3. 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:

  1. Emphasizes the importance of the cluster Green's function in understanding local interactions within strongly correlated systems

  2. 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..

  3. 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

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

FEATURED JOBS

UNTIL TOMORROW.

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