AWS Quantum Technologies Blog

Tag: quantum research

Classiq and AWS Power Quantum-Classical Chemistry Innovation in Singapore with Hatch

Introduction In biochemical processes development and analysis, binding energy, the energy released when a small molecule docks into a protein’s active site, determines how strongly a compound, such as a ligand, interacts with its protein target. Early-stage computational prediction of this quantity helps research teams prioritize candidates before committing to resource-intensive laboratory testing. Conventional methods […]

Realizing the fermionic Laughlin state on a trapped-ion quantum processor

This post was contributed by Lingnan Shen, Mao Lin, Cedric Yen-Yu Lin, Di Xiao, and Ting Cao Quantum computing is beginning to simulate forms of matter that are difficult to study in the lab or compute classically. In this work, researchers from University of Washington and Amazon Braket demonstrate one such milestone. They realized a […]

Quantum-Guided Cluster Algorithms for Combinatorial Optimization

This post was contributed by Peter Eder, Aron Kerschbaumer, Jernej Rudi Finžgar, Raimel Medina, Martin Schuetz, Helmut Katzgraber, Sarah Braun, and Christian Mendl. Optimizing complex systems is a major challenge in science and industry. Many tasks – such as scheduling, routing, portfolio selection, or network design – can be written as combinatorial optimization problems that […]

Amazon Braket Introduces Spending Limits for Quantum Processing Units

This post was contributed by Dimitar Trenev, Vivek Dwivedi, and Zia Mohammad Customers from research institutions, educational organizations, and development teams asked us for a better way to control quantum processing unit (QPU) cost while exploring quantum computing applications. With this launch, Amazon Braket now supports spending limits, which enables you to set maximum spending […]

Exact simulation of Quantum Enhanced Signature Kernels for financial data streams prediction using Amazon Braket

This post was contributed by Ernesto Palidda and Marco Paini from Rigetti Computing, Cristopher Salvi and Antoine Jacquier from Imperial College London, Samuel Crew from National Tsing Hua University and Shaun Geaney from Standard Chartered along with Sebastian Stern and Michael Brett from Amazon Web Services (AWS). Empirical evidence suggests that well-chosen quantum feature maps […]

Leveraging near-term quantum hardware for simulating high-dimensional dynamics

This post was contributed by Jiaqi Leng, Joseph Li, Xiaodi Wu Scientists and engineers face numerous computational challenges in fields like fluid dynamics [1], modeling heat and sound propagation [2], and aircraft design [3]. Simulating partial differential equations (PDEs) in high dimensions offers a powerful approach to addressing these challenges. However, solving these high-dimensional differential […]

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 […]

Quantum-amenable pruning of large language models and large vision models using block coordinate descent

Quantum-amenable pruning of large language models and large vision models using block coordinate descent

Fidelity and AWS have teamed up to put their brains together and create a Combinatorial Brain Surgeon (iCBS) to operate on AI models. This innovative pruning algorithm is just what the doctor ordered for your large-scale AI needs. Check out our latest blog for the full diagnosis.

← Older posts