AWS Quantum Technologies Blog

Category: Quantum Technologies

Accelerating the Quantum Toolkit for Python (QuTiP) with cuQuantum on AWS

Simulating quantum systems using classical computers remains a computational challenge. In fact, the resources required for these simulations scale exponentially with the size of the system being simulated. This fact is at the core of the motivation to build a quantum computer. Quantum computers have the potential to surpass even the most powerful supercomputers when […]

Introducing Local Device Emulator for Verbatim Circuits on Amazon Braket

Today, we’re excited to announce the launch of local device emulator on Amazon Braket. This feature enables developers to emulate their verbatim circuits based on device calibration data. Local device emulation helps accelerate the development cycle by providing early feedback on circuit compatibility and expected behavior in the presence of noise. In this post, we […]

Amazon Braket introduces program sets enabling customers to run quantum programs up to 24x faster

When running quantum computing workloads with multiple circuits, such as simulating molecular systems, building classical shadows, and training quantum machine learning models, submitting each circuit individually creates substantial delays between executions due to task setup and processing time. This overhead adds hours to the total run time of the experiments and makes the results sensitive […]

Introducing the Amazon Braket Learning Plan and Digital Badge

Introducing the Amazon Braket Learning Plan and Digital Badge

Available today, quantum computing developers, educators, and enthusiasts can learn the foundations of quantum computing on Amazon Web Services (AWS) with the Amazon Braket Digital Learning Plan and earn their own Digital badge – at no additional cost. You earn the badge after completing a series of learning courses and scoring at least 80% on an […]

Amazon Braket launches new 54-qubit superconducting quantum processor from IQM

Amazon Braket enables customers to design and run quantum algorithms on a broad selection of quantum hardware through a unified interface. Today, we expand the hardware available on Braket with the general availability of IQM’s latest quantum processing unit (QPU). The device, named Emerald, is a 54-qubit superconducting QPU providing customers higher fidelity gates and […]

Simulating Fluid Mechanics using Quantum Computing on Amazon Braket with Haiqu and Quanscient

by Dmitri Iouchtchenko, Michael Brett, Ljubomir Budinski, Maciej Koch-Janusz, Mykola Maksymenko, Ossi Niemimäki, Valtteri Lahtinen, Vidyasagar Ananthan, and Vladyslav Bohun on in Amazon Braket, Quantum Technologies Permalink Share

Numerical simulations of complex fluid dynamics, electromagnetics and thermomechanical problems are crucial in the automotive and aerospace industries for designing and optimizing components like airplane wings, yet modeling continuum physics on high-resolution grids pushes classical computing to its limits. Quantum computing offers a potential path to overcome these barriers by encoding large simulation grids with […]

Design quantum integrated circuits with open-source software DeviceLayout.jl from AWS

Today, we are introducing DeviceLayout.jl, a software package for computer-aided design (CAD) of quantum integrated circuits. At the AWS Center for Quantum Computing (CQC), we use DeviceLayout.jl to design superconducting quantum devices on our path to building a fault-tolerant quantum computer—devices we’ve used for experiments like “Hardware-efficient error correction using concatenated bosonic qubits” (our “Ocelot” […]

Experiment with dynamic circuits on IQM Garnet with Amazon Braket

Experiment with dynamic circuits on IQM Garnet with Amazon Braket

Customers use Amazon Braket to design and run quantum algorithms to explore applications of quantum computing. As the complexity of research workloads matures, it is important to have access to innovative capabilities. In this blog post, we announce an expansion of Amazon Braket’s experimental capabilities by adding support for dynamic circuits on the IQM Garnet […]

Modeling a nitrogen-vacancy center with NVIDIA CUDA-Q Dynamics: University of Washington Capstone Project

Modeling a nitrogen-vacancy center with NVIDIA CUDA-Q Dynamics: University of Washington Capstone Project

Defects in crystals are leading qubit candidates for quantum information networks. The ability to efficiently model defects, like nitrogen-vacancy (NV) centers, and their environment would deepen our understanding of these complex quantum systems and accelerate the use of these defects in scalable quantum networks. To study complex quantum systems like these and advance basic research […]