Quantum Dev Digest

By: Quiet. Please
  • Summary

  • This is your Quantum Dev Digest podcast.

    Quantum Dev Digest is your daily go-to podcast for the latest in quantum software development. Stay ahead with fresh updates on new quantum development tools, SDKs, programming frameworks, and essential developer resources released this week. Dive deep with code examples and practical implementation strategies, ensuring you're always equipped to innovate in the quantum computing landscape. Tune in to Quantum Dev Digest and transform how you approach quantum development.

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Episodes
  • Quantum Bombshell: IBM Unleashes Qiskit 1.0, Revs Up for 1,000+ Qubit Quantum Domination
    Dec 17 2024
    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. Today, I'm excited to share with you some significant advancements in quantum development tools, SDK updates, and programming frameworks that have been released in the past week.

    Let's dive right in. IBM has just released Qiskit SDK 1.0, marking a new era in quantum computing centered on performance, stability, and usability. This release is the culmination of years of improvements, enabling users to easily build and transpile circuits with over 100 qubits and laying the groundwork for future 1,000+ qubit workloads. The new SDK features a more stable API with fewer breaking changes and robust backwards-compatibility and bug support[1].

    One of the key features of Qiskit 1.0 is its ability to handle larger circuits. For instance, the recent addition of the 127-qubit backend, ibm_kyoto, allows developers to explore more complex quantum applications. Here's a simple example of how you can use Qiskit to create a quantum circuit:

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

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

    # Execute the circuit
    simulator = Aer.get_backend('qasm_simulator')
    job = execute(qc, simulator)
    result = job.result()
    counts = result.get_counts(qc)
    print(counts)
    ```

    In addition to Qiskit 1.0, IBM has also introduced new capabilities in Middleware for Quantum, which includes tools for building quantum-classical workflows and managing their execution on heterogeneous compute resources. This beta release offers features like classical compute for remote execution of workloads, easy distribution and parallelization of tasks, and compatibility with Qiskit Runtime Primitives and sessions[1].

    Looking ahead, IBM's roadmap for 2025 includes plans to introduce error mitigation and suppression techniques into Qiskit Runtime, enabling users to focus on improving the quality of results obtained from quantum hardware. The company also plans to introduce quantum communication between processors to support quantum parallelization, starting with the 462-qubit "Flamingo" processor and eventually leading to a 1,386-qubit system[2].

    These advancements are crucial for developers who use quantum circuits within classical routines to demonstrate quantum advantage. IBM is maturing the Qiskit Runtime Service's primitives to help developers work efficiently with non-classical probability distributions, which are at the heart of quantum algorithm development.

    That's all for today, folks. Stay tuned for more updates from the quantum world, and keep experimenting with these new tools and frameworks. Happy coding

    For more http://www.quietplease.ai


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    3 mins
  • Quantum Gossip: IBMs Qiskit Speedup, Flamin go Processor, and Mozillas AI Moves
    Dec 14 2024
    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 from the quantum world. Let's dive right into the exciting updates from the past week.

    First off, IBM has just released Qiskit SDK v1.3, and it's packed with some fantastic improvements. One of the biggest updates is the migration of most transpilation passes to Rust, which has resulted in a whopping 6x speedup for transpiling tasks. This means that running the full Benchpress suite of performance benchmarks now takes less than an hour, compared to the 6+ hours required for Qiskit SDK v1.2[1].

    But that's not all. The circuit library has undergone a major refactor to clarify the distinction between circuits defined by their structure and those defined by abstract mathematical operations. This includes new gates support for HighLevelSynthesis plugins, with ancilla support and the integration of Rustiq, a popular external library, into the core stack. Specifically, the PauliEvolution gate now offers the option to use Rustiq, which is a significant enhancement.

    Additionally, the circuit library now includes new observable classes like SparseObservable, which stores observables as a sum of terms in a memory-efficient way. There are also new functions like evolved_operator_ansatz(), hamiltonian_variational_ansatz(), and qaoa_ansatz() to implement variational circuits based on operator evolutions. These are more performant versions of the existing EvolvedOperatorAnsatz and QAOAAnsatz.

    On a different note, IBM is also making strides in quantum hardware. Their roadmap for 2025 includes the introduction of the 462-qubit "Flamingo" processor with built-in quantum communication links, which will be followed by the 1,386-qubit "Kookaburra" processor. These advancements will enable quantum parallelization and lay the groundwork for quantum error correction in the future[3].

    For developers looking to get hands-on experience with the latest tools, the IBM Quantum Developer Conference 2024 was a huge success. It provided attendees with practical experience using Qiskit to map use cases to quantum circuits and execute them on hardware, optimizing the quality of results while balancing runtime costs[4].

    In contrast to quantum computing, other tech communities are focusing on different areas. For instance, Mozilla has been exploring AI solutions that make a practical difference in everyday life, hosting numerous online events and fostering a community of developers working with open-source AI[5].

    However, back to quantum. If you're interested in exploring more about the latest developments, I recommend checking out the Qiskit v1.3 release notes and the IBM Quantum roadmap. These resources are invaluable for staying up-to-date with the rapidly evolving quantum landscape.

    That's all for today. Keep coding, and let's push the boundaries of quantum computing together. Until next time, stay quantum.

    For more http://www.quietplease.ai


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    3 mins
  • Quantum Gossip: Qiskit's Sizzling Update, IBM's Kooky Kookaburra, and Free Qubits Galore!
    Dec 12 2024
    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 from the quantum world. Today, I'm excited to dive into the recent updates in quantum development tools and SDKs.

    Just a few days ago, I was exploring the latest release of Qiskit SDK, version 1.3. This update is a game-changer, folks. The team at IBM has been working tirelessly to improve performance, and it shows. One of the most significant updates is the transition of most transpilation passes to Rust, resulting in a whopping 6x speedup for transpiling tasks. This means that running the full Benchpress suite of performance benchmarks now takes less than an hour, compared to the 6+ hours required for Qiskit SDK v1.2[1].

    But that's not all. The circuit library has undergone a major refactor, clarifying the distinction between circuits defined by their structure and those defined by abstract mathematical operations. This includes new gates support for HighLevelSynthesis plugins, with ancilla support, and the integration of Rustiq, a popular external library, into the core stack. Specifically, the PauliEvolution gate now offers the option to use Rustiq, which is a significant enhancement.

    Moreover, the circuit library now includes new observable classes like SparseObservable, which stores observables as a sum of terms in a memory-efficient way. Additionally, new functions like evolved_operator_ansatz(), hamiltonian_variational_ansatz(), and qaoa_ansatz() have been added to implement variational circuits based on operator evolutions. These are more performant versions of EvolvedOperatorAnsatz and QAOAAnsatz.

    Another notable addition is the RemoveIdentityEquivalent transpiler pass, which removes gates that are equivalent to an identity up to some tolerance. This is a practical tool for optimizing quantum circuits.

    IBM's roadmap for quantum-centric supercomputers is also worth mentioning. By 2025, they plan to introduce the Kookaburra processor, a 1,386-qubit multi-chip processor with a quantum communication link. This will enable the connection of multiple chips into a larger system, paving the way for quantum parallelization[3].

    In the meantime, developers can already access 127-qubit systems for free on the IBM Quantum Platform. This is a fantastic opportunity to explore quantum computing and prepare for the future[4].

    That's all for today, folks. Keep coding, and remember, the quantum future is here. Stay tuned for more updates from the quantum world.

    For more http://www.quietplease.ai


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    3 mins

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