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Quantum Bombshell: IBM Unleashes Qiskit 1.0, Revs Up for 1,000+ Qubit Quantum Domination
- 2024/12/17
- 再生時間： 3 分
- ポッドキャスト
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サマリー
あらすじ・解説
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
Get the best deals https://amzn.to/3ODvOta
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
Get the best deals https://amzn.to/3ODvOta