QClojure Braket

QClojure Braket contains a backend to run QClojure quantum algorithms with Amazon Braket.

It provides access to the simulators and to quantum hardware (QPUs) supported by Amazon Braket.

Disclaimers:

• experimental/alpha
  • the braket backend implementation is yet untested against AWS
• Using Amazon Braket costs money even for using simulators
  • use the simulators provided by QClojure for experimentation first

⚠️ Important: AWS Braket requires an S3 bucket to store quantum task results. See S3 SETUP for configuration details.

Usage

The qclojure-braket provides an Amazon Braket backend for QClojure algorithms. To use it in your code, you need both the qclojure and the qclojure-braket dependency in your project definition.

Prerequisites

1. AWS Account and Credentials: Set up AWS credentials via environment variables, AWS CLI, or IAM roles
2. S3 Bucket: Create an S3 bucket for storing Braket task results (see S3 SETUP)

(require '[org.soulspace.qclojure.application.algorithm.bernstein-vazirani :as bv])
(require '[org.soulspace.qclojure.adapter.backend.braket :as braket])

; Define the Amazon Braket simulation backend.
;
; IMPORTANT: You must provide an S3 bucket for storing quantum task results.
; AWS Braket requires this for all quantum task executions.
;
; Ensure you have:
; 1. AWS credentials configured (via environment variables, CLI, or IAM roles)
; 2. An S3 bucket created with appropriate permissions
; 3. The AWS region matches your bucket's region
(def braket-backend
  (braket/create-braket-simulator {:s3-bucket "my-braket-results-bucket"
                                   :s3-key-prefix "simulations/"
                                   :region "us-east-1"}))

; Run Bernstein-Vazirani algorithm on Amazon Braket
; with a specific secret string [1 0 1 0] and 1000 shots.
; The result will be a map with the counts of the measured results.
(bv/bernstein-vazirani-algorithm braket-backend [1 0 1 0] {:shots 1000})

Advanced Features

The enhanced Braket backend includes:

• Hardware Optimization: Automatic circuit optimization for device topology and gate constraints
• Error Mitigation: Zero-noise extrapolation, readout error correction, and symmetry verification
• Multi-Provider Support: Optimized for Rigetti, IonQ, IQM, OQC, and QuEra devices
• Cost Estimation: Real-time pricing estimates for QPU usage
• Batch Processing: Efficient execution of multiple circuits

Documentation

📚 Documentation - Setup guides, tutorials, and device information

Quick Links

• 🚀 Getting Started - Overview and setup sequence
• 🔑 AWS Credentials - Authentication setup
• 🪣 S3 Setup - Required storage configuration
• 🖥️ Braket Devices - Available quantum hardware
• 📖 Tutorial - Step-by-step learning guide
• ⚙️ Terraform Setup - Infrastructure as code

See examples/enhanced_braket_usage.clj for code examples.

Copyright

© 2025 Ludger Solbach

License

Eclipse Public License 1.0 (EPL1.0)