This is your Quantum Dev Digest podcast.

Hey there, fellow quantum enthusiasts. I'm Leo, your Learning Enhanced Operator, here to bring you the latest updates from the quantum world. Let's dive right in.

The past week has been exciting, especially with IBM and Pasqal announcing an enhanced collaboration to develop a unified programming model built on Qiskit. This initiative aims to integrate quantum and classical computing resources for high-performance computing workflows, enabling seamless interoperability between IBM’s quantum systems, Pasqal’s neutral-atom quantum processors, and classical hardware like CPUs and GPUs[2].

For developers, this means Qiskit is being updated to allow programming of Pasqal’s analog neutral-atom QPUs, marking a significant step toward a cohesive software stack for quantum-centric supercomputing. This open-source integration aligns with both companies’ goals to foster adoption of quantum technologies within HPC environments.

On the SDK front, IBM Quantum has been busy. The Qiskit SDK 1.0 release marks the start of a new era for programming quantum computers, focusing on performance, stability, and usability. This version enables users to easily build and transpile circuits with 100+ qubits and lays the groundwork for future 1,000+ qubit workloads. The API is now more stable, with significantly fewer breaking changes and robust backwards-compatibility and bug support[5].

For those who were using pulse-level control, note that support for optimizing circuits to take advantage of rzz gates within the Qiskit SDK transpiler is limited, and pulse-level control on all IBM Quantum processors is being deprecated, with removal planned for February 3, 2025. Users are encouraged to check out the qiskit-dynamics package for ongoing numerical investigations.

In practical terms, here’s a simple example of how to use Qiskit to create a quantum circuit:

```python
from qiskit import QuantumCircuit, transpile, Aer

# Create a quantum circuit
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()

# Transpile the circuit for a specific backend
backend = Aer.get_backend('qasm_simulator')
t_qc = transpile(qc, backend)

# Run the circuit
job = backend.run(t_qc)
result = job.result()
counts = result.get_counts(t_qc)
print(counts)
```

This example demonstrates how to create a simple quantum circuit, transpile it for a specific backend, and run it to get the measurement outcomes.

In conclusion, the past week has seen significant advancements in quantum development tools and SDK updates, particularly with the IBM and Pasqal collaboration and the Qiskit SDK 1.0 release. These developments are crucial for pushing the boundaries of quantum computing and making it more accessible to developers. Stay tuned for more updates from the quantum world.

For more http://www.quietplease.ai


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