Resource of free step by step video how to guides to get you started with machine learning.
Tuesday, April 14, 2020
Quantum supremacy: Benchmarking the Sycamore processor (QuantumCasts)
Quantum supremacy: Benchmarking the Sycamore processor talk is presented by Research Scientist Kevin Satzinger for the APS March Meeting 2020. The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with 53 programmable superconducting qubits. In our Sycamore processor, each qubit interacts with four neighbors in a rectangular lattice using tunable couplers. A key systems engineering advance of this device is achieving high-fidelity single- and two-qubit operations, not just in isolation but also while performing a realistic computation with simultaneous gate operations across the entire processor. We benchmark the Sycamore processor using cross-entropy benchmarking, a scalable method to evaluate system performance. Our largest system benchmarks feature circuits that are intractable for classical hardware, culminating in the demonstration of quantum supremacy. Furthermore, the fidelities from full-system benchmarks agree with what we predict from individual gate and measurement fidelities, verifying the digital error model and presenting a path forward to quantum error correction. Nature 574, 505-510 (2019) Watch every episode of QuantumCasts here → https://goo.gle/QuantumCasts Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow
Subscribe to:
Post Comments (Atom)
-
Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow) [Collection] In a special live ep...
-
Quick answer: Qwen 3 32B needs about 22.7 GB of VRAM at Q4_K_M with a 4k-token context — a used 24 GB card like the RTX 3090 is the minimu...
-
Inside TensorFlow: Summaries and TensorBoard [Collection] Take an inside look into the TensorFlow team’s own internal training sessions-...
-
Hey everyone This is Ujjwal kapil B.tech 2nd year Ai and Ml In this video we covered one of the most important question "COLLEGE KAB O...
-
Quick answer: DeepSeek V4 Flash is a Mixture-of-Experts model: all 284 billion parameters must be stored, so combined RAM plus VRAM — not ...
-
This video is a crash course on understanding how finetuning on LLM models can be performed uing QLORA,LORA, Quantization using LLama2, Grad...
-
Quick answer: For an 8B model at Q4, local electricity costs roughly $0.05 per million output tokens — against about $0.53 per million ble...
-
Quick answer: For most learners, the best single machine learning book is Hands-On Machine Learning with Scikit-Learn, Keras & Tensor...
-
#minecraft #neuralnetwork #backpropagation I built an analog neural network in vanilla Minecraft without any mods or command blocks. The n...
-
In this video, you'll learn how to use machine learning, computer vision and deep learning to create a football analysis system. This pr...
No comments:
Post a Comment