Tag: quantum algorithms

This post was contributed by Mao Lin, Bruno Camino, John Buckeridge and Scott M. Woodley Many advanced materials — from battery electrodes to semiconductor alloys — owe their useful properties to atomic-scale disorder. But predicting how atoms arrange themselves at a given temperature is hard: the number of possible configurations explodes combinatorially and sampling them […]

This post was contributed by Dimitar Trenev, Sebastian Stern, Tyler Takeshita, Cedric Lin, Peter Komar, Pooja Rao, Jerome Gonthier, and Elica Kyoseva. As quantum computing matures toward fault tolerance, a pressing question faces the high-performance computing (HPC) community: why is tightly integrating quantum processors to classical supercomputing infrastructure important? Today, algorithm researchers from Amazon Web […]

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 […]

We recently released v0.11 of the Qiskit-Braket provider, which brings more Qiskit features to Amazon Braket users, improves access to Braket backends through Qiskit, and also enables compilation on the Braket SDK or with OpenQASM Programs. With v0.11, the Qiskit-Braket provider now: Supports flexible compilation features for Braket using common Qiskit transpile functionality through the […]

This post shows how quantum reservoir computing (QRC) can tackle machine learning challenges using Rydberg-atom quantum computers. Readers will learn how QRC works, see its performance on image classification and time series prediction tasks, and understand when it outperforms classical methods—particularly for small datasets in pharmaceutical research. After decades of progress, machine learning (ML) has […]

AWS re:Invent 2025 returns to Las Vegas from December 1 to 5, bringing together builders, researchers, and business leaders who are exploring what is next in advanced computing on AWS. Quantum computing is moving from research labs into early real-world use. With Amazon Braket , AWS gives you managed access to multiple quantum hardware providers, […]

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 […]

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 […]

Given a three-dimensional model of a drug’s potential target (the binding site), what is the orientation of the drug candidate molecule that best “fits” (docks onto) this binding site? Structure-based drug design involves the use of computational tools to answer this very question! Chugai Pharmaceutical Co., Ltd., a leading Japanese pharmaceutical company, specializes in the […]

Introduction In this post, we explore the mathematical foundations and practical implementations of quantum measurement techniques using Amazon Braket. We examine this subject through multiple perspectives: action of gates as basis transformation, projection as inner product, projector formalism, and observable formalism. A key insight that we’ll develop is understanding basis transformations as tools for converting […]