Amazon Braket
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| Amazon Braket | |
|---|---|
| Name | Amazon Braket |
| Developer | Amazon Web Services |
| Released | 2019 |
| Operating system | Cross-platform |
| License | Proprietary |
Amazon Braket is a cloud-based quantum computing service that provides access to quantum hardware, simulators, and development tools. It integrates with Amazon Web Services, supports devices from multiple vendors, and targets researchers, developers, and enterprises working in quantum algorithms, quantum chemistry, and quantum optimization. Its ecosystem connects to classical cloud services such as AWS Lambda, AWS CloudFormation, Amazon S3, Amazon EC2, and AWS Identity and Access Management.
Overview
Amazon Braket was announced by Amazon Web Services during a period of heightened interest following developments at institutions such as IBM, Google, Microsoft, Rigetti Computing, and D-Wave Systems. It aims to lower barriers to entry for entities including NASA, Oak Ridge National Laboratory, Brookhaven National Laboratory, Los Alamos National Laboratory, and Argonne National Laboratory. The service situates itself amid competitive offerings like IBM Quantum Experience, Google Quantum AI, Microsoft Azure Quantum, and vendor ecosystems from IonQ, Honeywell Quantum Solutions, and Xanadu. Amazon Braket interoperates with research outputs from groups at MIT, Caltech, University of Oxford, University of Waterloo, and Harvard University.
Architecture and Components
The platform architecture couples quantum task orchestration with classical cloud infrastructure such as Amazon EC2, AWS Lambda, Amazon S3, AWS CloudWatch, and AWS Key Management Service. Core components include a managed task queue inspired by distributed systems research from Google, Facebook, and Netflix, a job submission API patterned after practices at Apache Software Foundation projects, and a software development kit influenced by toolchains from Qiskit contributors at IBM Research and Cirq teams at Google Research. The control stack handles device-specific calibrations similar to procedures at IonQ, Rigetti, and D-Wave Systems, and integrates telemetry and monitoring principles used at Datadog and Splunk. Security and identity management follow patterns established by AWS Identity and Access Management and compliance frameworks referenced by ISO, NIST, and SOC 2 auditors.
Supported Quantum Hardware and Simulators
Amazon Braket offers access to gate-based devices and quantum annealers from vendors such as IonQ, Rigetti Computing, Quantinuum, D-Wave Systems, and PsiQuantum-adjacent vendors. It provides pulse- and gate-level execution support analogous to offerings from IBM, Google Quantum AI, and Microsoft Quantum teams. Simulators include state-vector, density-matrix, and tensor-network backends influenced by open-source projects like Qiskit, Cirq, ProjectQ, and numerical libraries from NumPy and SciPy. High-performance simulator deployments leverage instances like Amazon EC2 GPU and HPC nodes comparable to configurations used at Lawrence Berkeley National Laboratory, Argonne National Laboratory, and Sandia National Laboratories.
Programming Model and SDK
The programming model centers on an SDK that supports circuit construction, hybrid workflows, and task batching, drawing conceptual parallels with Qiskit from IBM, Cirq from Google Research, Forest from Rigetti, and PennyLane from Xanadu. The SDK interfaces with orchestration services such as AWS Step Functions and data stores like Amazon S3, while enabling algorithm implementations referenced in literature from Peter Shor, Lov Grover, John Preskill, Aram Harrow, Alexei Kitaev, and Seth Lloyd. Users implement quantum-classical loops used in variational algorithms developed at MIT, Caltech, University of Cambridge, and ETH Zurich. Integration patterns follow best practices advocated by IEEE, ACM, and publications in Nature, Science, and Physical Review Letters.
Use Cases and Applications
Targeted applications include quantum chemistry simulations for molecules studied at Harvard University, Stanford University, and University of California, Berkeley; optimization use cases in logistics and finance examined at McKinsey & Company, Goldman Sachs, and J.P. Morgan; machine learning workflows researched at DeepMind, OpenAI, and Google Brain; and material science investigations by teams at IBM Research, Bell Labs, and Toyota Research Institute. Experimental deployments explore algorithms such as Shor's algorithm, Grover's algorithm, Quantum Approximate Optimization Algorithm, and Variational Quantum Eigensolver reported in articles in Nature Physics, Physical Review X, and proceedings of NeurIPS and QIP conferences.
Pricing and Availability
Pricing models combine per-task execution fees, hardware access charges, and classical compute costs tied to Amazon EC2 and storage fees tied to Amazon S3, following commercial models comparable to IBM Quantum and Microsoft Azure. Availability spans multiple AWS Regions with compliance considerations relevant to standards enforced by NIST and auditors from Deloitte and Ernst & Young. Enterprise uptake and academic collaborations echo procurement patterns observed at Lawrence Livermore National Laboratory, CERN, and European Organization for Nuclear Research.