Sign up to save your podcasts
Or
Share Quantum Scoop: Qiskit's Glow-Up, Hybrid AI's 2025 Takeover, and Microsoft's Reality Check!
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Quantum Scoop: Qiskit's Glow-Up, Hybrid AI's 2025 Takeover, and Microsoft's Reality Check!
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 scoop on quantum development tools and SDK updates. Let's dive right in!
I've been exploring the recent advancements in Qiskit, IBM's quantum software development kit. The Qiskit SDK v1.0 release has been a game-changer, introducing a stable API and consolidated features that make it easier for developers to work with quantum hardware. The new V2 primitives, such as the estimator and sampler, allow for vectorized inputs, making it simpler to sweep over parameter value sets and observables. I've been experimenting with these primitives, and I must say, they're a huge improvement.
For instance, the `GenericBackendV2` class in the `qiskit.providers.fake_provider` module enables you to configure and build custom `BackendV2` instances locally. This feature is incredibly useful for testing and debugging quantum circuits. I've been using it to simulate complex quantum algorithms, and it's been a huge time-saver.
I also came across the Qiskit addons, which are modular tools that allow researchers to insert the latest quantum computing techniques into their workflows. The multiproduct formulas (MPF) addon, for example, aims to reduce the Trotter error of time evolution circuits. The approximate quantum compilation (AQC-Tensor) addon uses tensor networks to classically approximate time evolution circuits before using quantum methods to push beyond classical limits.
Speaking of quantum error correction, I've been exploring the Quantum Inspire platform, which provides a range of code examples and Jupyter notebooks for learning quantum algorithms. The repetition code example, which encodes one logical qubit using three physical qubits, is a great starting point for understanding quantum error correction.
As I delved deeper into the world of quantum computing, I stumbled upon an article by Yuval Boger, Chief Commercial Officer at QuEra Computing, and Jan Goetz, Co-CEO and Co-founder of IQM Quantum Computers. They predict that 2025 will be the year of hybrid quantum-AI systems, which will impact fields like optimization, drug discovery, and climate modeling. They also emphasize the importance of quantum error correction and the development of scalable error-correcting codes.
Lastly, I came across an article by Krysta Svore, Technical Fellow at Microsoft, who highlights the challenges of building reliable quantum systems. She emphasizes the need for higher qubit counts and lower noise levels to reach practical, real-world readiness.
That's all for now, folks As a quantum enthusiast, I'm excited to see the advancements in quantum development tools and SDK updates. Stay tuned for more updates, and happy coding!
Outro music plays...
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 scoop on quantum development tools and SDK updates. Let's dive right in!
I've been exploring the recent advancements in Qiskit, IBM's quantum software development kit. The Qiskit SDK v1.0 release has been a game-changer, introducing a stable API and consolidated features that make it easier for developers to work with quantum hardware. The new V2 primitives, such as the estimator and sampler, allow for vectorized inputs, making it simpler to sweep over parameter value sets and observables. I've been experimenting with these primitives, and I must say, they're a huge improvement.
For instance, the `GenericBackendV2` class in the `qiskit.providers.fake_provider` module enables you to configure and build custom `BackendV2` instances locally. This feature is incredibly useful for testing and debugging quantum circuits. I've been using it to simulate complex quantum algorithms, and it's been a huge time-saver.
I also came across the Qiskit addons, which are modular tools that allow researchers to insert the latest quantum computing techniques into their workflows. The multiproduct formulas (MPF) addon, for example, aims to reduce the Trotter error of time evolution circuits. The approximate quantum compilation (AQC-Tensor) addon uses tensor networks to classically approximate time evolution circuits before using quantum methods to push beyond classical limits.
Speaking of quantum error correction, I've been exploring the Quantum Inspire platform, which provides a range of code examples and Jupyter notebooks for learning quantum algorithms. The repetition code example, which encodes one logical qubit using three physical qubits, is a great starting point for understanding quantum error correction.
As I delved deeper into the world of quantum computing, I stumbled upon an article by Yuval Boger, Chief Commercial Officer at QuEra Computing, and Jan Goetz, Co-CEO and Co-founder of IQM Quantum Computers. They predict that 2025 will be the year of hybrid quantum-AI systems, which will impact fields like optimization, drug discovery, and climate modeling. They also emphasize the importance of quantum error correction and the development of scalable error-correcting codes.
Lastly, I came across an article by Krysta Svore, Technical Fellow at Microsoft, who highlights the challenges of building reliable quantum systems. She emphasizes the need for higher qubit counts and lower noise levels to reach practical, real-world readiness.
That's all for now, folks As a quantum enthusiast, I'm excited to see the advancements in quantum development tools and SDK updates. Stay tuned for more updates, and happy coding!
Outro music plays...
For more http://www.quietplease.ai
Get the best deals https://amzn.to/3ODvOta
View all episodes
By Quiet. PleaseThis is your Quantum Dev Digest podcast.
Hey there, fellow quantum enthusiasts I'm Leo, your Learning Enhanced Operator, here to bring you the latest scoop on quantum development tools and SDK updates. Let's dive right in!
I've been exploring the recent advancements in Qiskit, IBM's quantum software development kit. The Qiskit SDK v1.0 release has been a game-changer, introducing a stable API and consolidated features that make it easier for developers to work with quantum hardware. The new V2 primitives, such as the estimator and sampler, allow for vectorized inputs, making it simpler to sweep over parameter value sets and observables. I've been experimenting with these primitives, and I must say, they're a huge improvement.
For instance, the `GenericBackendV2` class in the `qiskit.providers.fake_provider` module enables you to configure and build custom `BackendV2` instances locally. This feature is incredibly useful for testing and debugging quantum circuits. I've been using it to simulate complex quantum algorithms, and it's been a huge time-saver.
I also came across the Qiskit addons, which are modular tools that allow researchers to insert the latest quantum computing techniques into their workflows. The multiproduct formulas (MPF) addon, for example, aims to reduce the Trotter error of time evolution circuits. The approximate quantum compilation (AQC-Tensor) addon uses tensor networks to classically approximate time evolution circuits before using quantum methods to push beyond classical limits.
Speaking of quantum error correction, I've been exploring the Quantum Inspire platform, which provides a range of code examples and Jupyter notebooks for learning quantum algorithms. The repetition code example, which encodes one logical qubit using three physical qubits, is a great starting point for understanding quantum error correction.
As I delved deeper into the world of quantum computing, I stumbled upon an article by Yuval Boger, Chief Commercial Officer at QuEra Computing, and Jan Goetz, Co-CEO and Co-founder of IQM Quantum Computers. They predict that 2025 will be the year of hybrid quantum-AI systems, which will impact fields like optimization, drug discovery, and climate modeling. They also emphasize the importance of quantum error correction and the development of scalable error-correcting codes.
Lastly, I came across an article by Krysta Svore, Technical Fellow at Microsoft, who highlights the challenges of building reliable quantum systems. She emphasizes the need for higher qubit counts and lower noise levels to reach practical, real-world readiness.
That's all for now, folks As a quantum enthusiast, I'm excited to see the advancements in quantum development tools and SDK updates. Stay tuned for more updates, and happy coding!
Outro music plays...
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 scoop on quantum development tools and SDK updates. Let's dive right in!
I've been exploring the recent advancements in Qiskit, IBM's quantum software development kit. The Qiskit SDK v1.0 release has been a game-changer, introducing a stable API and consolidated features that make it easier for developers to work with quantum hardware. The new V2 primitives, such as the estimator and sampler, allow for vectorized inputs, making it simpler to sweep over parameter value sets and observables. I've been experimenting with these primitives, and I must say, they're a huge improvement.
For instance, the `GenericBackendV2` class in the `qiskit.providers.fake_provider` module enables you to configure and build custom `BackendV2` instances locally. This feature is incredibly useful for testing and debugging quantum circuits. I've been using it to simulate complex quantum algorithms, and it's been a huge time-saver.
I also came across the Qiskit addons, which are modular tools that allow researchers to insert the latest quantum computing techniques into their workflows. The multiproduct formulas (MPF) addon, for example, aims to reduce the Trotter error of time evolution circuits. The approximate quantum compilation (AQC-Tensor) addon uses tensor networks to classically approximate time evolution circuits before using quantum methods to push beyond classical limits.
Speaking of quantum error correction, I've been exploring the Quantum Inspire platform, which provides a range of code examples and Jupyter notebooks for learning quantum algorithms. The repetition code example, which encodes one logical qubit using three physical qubits, is a great starting point for understanding quantum error correction.
As I delved deeper into the world of quantum computing, I stumbled upon an article by Yuval Boger, Chief Commercial Officer at QuEra Computing, and Jan Goetz, Co-CEO and Co-founder of IQM Quantum Computers. They predict that 2025 will be the year of hybrid quantum-AI systems, which will impact fields like optimization, drug discovery, and climate modeling. They also emphasize the importance of quantum error correction and the development of scalable error-correcting codes.
Lastly, I came across an article by Krysta Svore, Technical Fellow at Microsoft, who highlights the challenges of building reliable quantum systems. She emphasizes the need for higher qubit counts and lower noise levels to reach practical, real-world readiness.
That's all for now, folks As a quantum enthusiast, I'm excited to see the advancements in quantum development tools and SDK updates. Stay tuned for more updates, and happy coding!
Outro music plays...
For more http://www.quietplease.ai
Get the best deals https://amzn.to/3ODvOta