The Daily Qubit

🧲 Magnets are no longer the enemy, and we've got something for Eve, too

Author

Cierra Choucair
April 05, 2024 • Estimated Reading Time: 9 minutes

Welcome to the Quantum Realm. 

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

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IN TODAY’S ISSUE:

  • New research in quantum physics has significant implications for superconducting quantum computers

  • Classiq and QIC are teaming up to take on pharmaceutical development

  • A QML defense strategy to improve prediction stability (Alice & Bob are safe from Eve)

  • Plus, how to implement a confusion map in Cirq to simulate more complex errors

TOP NEWS & RESEARCH

NEWS

SUPERCONDUCTIVITY & MAGNETISM JOIN FORCES IN QUANTUM PHYSICS BREAKTHROUGH

The Meissner Effect, as represented here by the levitating magnet, is a property of superconductors that allows magnetic fields to be expelled up to a certain point. If the magnetic field becomes too strong, the Cooper pairs responsible for superconductivity in the first place will break and effectively destroy the superconducting state.

The Brief Byte: By merging superconductors with topological insulators, researchers achieved a proximity-induced Fulde-Ferrell-Larkin-Ovchinnikov state, enabling precise control over the Zeeman effect. The additional control over the system has implications for being able to further stabilize qubits in superconducting quantum computers.

Highlights:

  1. Researchers have engineered a hybrid superconducting device by combining a superconductor with a topological insulator, resulting in a Josephson Junction.

  2. This hybrid device enters the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state, merging magnetism with superconductivity to maintain superconductor characteristics while enabling magnetic control of its state.

  3. The fine-tuned superconducting properties, particularly the FFLO state, has implications in regards to reducing qubit sensitivity to external fields, which would in turn enhance the reliability of superconducting quantum computers.

NEWS

CLASSIQ AND QIC ARE COLLABORATING TO ACCELERATE PHARMACEUTICAL DEVELOPMENT

The Brief Byte: Classiq’s advanced quantum computing technology and Quantum Intelligence Corp’s innovative drug development platform, QUEST, are uniting to pioneer the discovery and design of new pharmaceutical candidates.

Highlights:

  1. Classiq and Quantum Intelligence Corp launch a collaboration, combining quantum computing with pharmacology to fast-track drug development. Classiq’s quantum computing platform accelerates the development of quantum software. The Classiq github repo boasts the “largest collection of quantum algorithms, applications.” QIC leverages quantum and AI technology to differentiate itself from traditional biopharmaceutical companies.

  2. Leveraging Classiq’s quantum computing platform and QIC’s QUEST-ADMET, the partnership aims to enhance drug discovery, accurately predict side effects, and improve the safety and efficacy of new drugs.

RESEARCH

Summary of Enhancing quantum adversarial robustness by randomized encodings

The Brief Byte: QML is vulnerable to attacks causing misclassification, but researchers have proposed a defense strategy leveraging random unitary encoding and quantum error correction to improve predication stability.

Highlights:

  1. This paper introduces a defense strategy for quantum learning systems against adversarial perturbations by leveraging randomness through randomized encoding along with quantum error correction and the barren plateaus phenomenon.

  2. The proposed method aims to obscure gradient information from attackers, by employing random unitary and QEC encoders to increase the resilience of quantum classifiers to both deliberately engineered attacks and experimental noise.

  3. This approach is validated for both near-term quantum devices and future fault-tolerant quantum computing.

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 show you how to use the confusion_map field within Cirq. This field is useful for representing complex errors, such as probabilistic error and correlated error across more than one qubit.

# Define a line qubit

q0 = cirq.LineQubit(0)
# Define a confusion map to simulate noisy measurements. Give a 30% chance that |0⟩ will be incorrectly reported as |1⟩ and a 10% chance that |1⟩ will be incorrectly reported as |0⟩

cmap = {(0,): np.array([[0.7, 0.3], [0.1, 0.9]])}
# Create circuit, apply a Pauli X gate, measure qubit state, and assign to 'result'.

circuit = cirq.Circuit(
   cirq.X(q0),
   cirq.measure(q0, key='result', confusion_map=cmap)
)
# 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

result = cirq.Simulator(seed=0).run(circuit, repetitions=500)
# Create a histogram with results to see distribution of measurement outcomes

print(result.histogram(key='result'))

EVENTS

FEATURED JOBS

UNTIL TOMORROW.

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