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rqm-braket

Amazon Braket lowering and execution bridge for the Resonant Quantum Mechanics (RQM) ecosystem.

rqm-braket receives compiler-optimized circuit representations from rqm-compiler and translates them into Amazon Braket circuit and task objects, then executes them on:

  • the Amazon Braket Local Simulator
  • AWS Braket quantum devices

This package is a backend adapter / execution bridge, not a compiler, not a math engine, and not the owner of the public circuit schema. The canonical external circuit IR lives in rqm-circuits; optimization logic lives in rqm-compiler. rqm-braket is the final, AWS-facing step in that pipeline.

Better Coordinates for Better Measurement

This project uses quaternions because they preserve more of what physical systems are doing: phase, rotation, orientation, polarization, and coherence. Standard complex-number methods are powerful, but they can flatten these relationships too early. Quaternionic coordinates keep them together as one structured object, giving software a richer view of the measured system.

For RQM Technologies, better coordinates mean better measurement: more informative diagnostics, cleaner transformations, and more precise control across quantum, wave, sensing, imaging, and communications workflows.

Architecture Overview

The RQM software stack is intentionally layered:

              rqm-docs
                  |
  -------------------------------------------
  |               |                         |
rqm-core      rqm-circuits             rqm-notebooks
                  |
            rqm-compiler
                  |
        ----------------------
        |                    |
    rqm-qiskit          rqm-braket
        |                    |
        └────────────────────┘
                  |
            rqm-optimize  (optional)
                  |
              rqm-api
                  |
             RQM Studio

Layer responsibilities

Layer Responsibility
rqm-core Canonical math (quaternion, spinor, Bloch, SU(2))
rqm-circuits Canonical external / public circuit IR — the shared schema for Studio, API, and inter-service communication
rqm-compiler Parse, optimize, and rewrite circuits in an internal model; produce backend-ready instruction sequences
rqm-braket Lower compiler output into Amazon Braket objects; execute on local simulator or AWS devices
rqm-qiskit Lower compiler output into Qiskit objects; execute on IBM / Qiskit devices
rqm-optimize Optional backend-adjacent optimization / compression (post-compiler, pre-execution)
rqm-api REST API layer exposing backends to RQM Studio
rqm-notebooks Examples, demos, tutorials

Input boundary

The typical data flow from an external caller through to execution is:

RQM Studio / API caller
        │ (rqm-circuits payload)
        ▼
   rqm-circuits  ←  public/external circuit schema lives here
        │ (parsed & validated)
        ▼
   rqm-compiler  ←  optimization, rewriting, instruction lowering
        │ (compiler-internal circuit or descriptor list)
        ▼
   rqm-braket    ←  Braket lowering & execution (this package)
        │ (Braket Circuit + task)
        ▼
  Amazon Braket / AWS

External callers (RQM Studio, rqm-api) originate from rqm-circuits payloads. rqm-compiler validates and optimizes those payloads. rqm-braket only sees the compiler-produced output — it does not parse or own the public wire format.

If rqm-braket exposes helper functions that accept compiler Circuit objects or descriptor lists directly (e.g. run_descriptors, to_backend_circuit), those helpers assume upstream parsing and validation have already happened.

What This Package Does

rqm-braket provides five core capabilities:

1. Translation

Convert compiled programs into Braket Circuit objects.

compiled_program → Braket Circuit

Handled by:

BraketTranslator
compile_to_braket_circuit(...)

2. Synchronous Execution

Run circuits on:

  • Local simulator (offline-safe)
  • AWS Braket devices (synchronous — blocks until complete)
Circuit → execution → BraketResult

Handled by:

run_local(...)
run_device(...)
BraketBackend

3. Asynchronous Execution

Submit jobs without blocking and poll for results later:

Circuit → submit → task_arn → poll status → retrieve result

Handled by:

run_device_async(...)   → task ARN
get_task_status(arn)    → "QUEUED" / "RUNNING" / "COMPLETED" / ...
get_task_result(arn)    → BraketResult

4. Device Discovery

List available AWS Braket devices:

from rqm_braket import list_devices

simulators = list_devices(device_types=["SIMULATOR"])
qpu_devices = list_devices(device_types=["QPU"])
all_devices = list_devices()

Returns JSON-serializable dicts with deviceArn, deviceName, deviceType, status, and providerName.

5. Descriptor-first Execution

Execute directly from canonical descriptors (JSON output of rqm_compiler.Circuit.to_descriptors()):

from rqm_braket import run_descriptors

descriptors = [
    {"gate": "h", "targets": [0], "controls": [], "params": {}},
    {"gate": "cx", "targets": [1], "controls": [0], "params": {}},
]
result = run_descriptors(descriptors, shots=200)
print(result.counts)

This is the primary API entry point for rqm-api / RQM Studio.

Note: In production, descriptor lists originate from rqm-circuits payloads that have been parsed and optimized by rqm-compiler upstream. rqm-braket receives the compiler-produced output and does not validate the public wire format itself.

6. Result Wrapping

Normalize Braket outputs into a simple interface:

result.counts
result.probabilities
result.shots
result.most_likely_bitstring()
result.to_dict()                                    # base fields
result.to_dict(include_probabilities=True)          # + probabilities
result.to_dict(include_task_id=True)                # + task ARN
result.to_dict(include_status=True)                 # + task status

7. Convenience Bridges (rqm-core delegation)

rqm-braket exposes thin bridge functions for users who want to prepare quantum states without going through the compiler first.

These bridges are not the primary API.
For production use, prefer the compiler-first path.

rqm-compiler → compiled_program → backend.run(...)

Bridges are intended for students, quick experiments, and direct state preparation. All underlying mathematics is handled by rqm-core.

spinor_to_circuit(alpha, beta, target=0)

Prepares the qubit state |ψ⟩ = α|0⟩ + β|1⟩ from the given spinor.

Bloch-sphere math is delegated to rqm_core.state_to_bloch.

import math
from rqm_braket import spinor_to_circuit

s = 1 / math.sqrt(2)
circuit = spinor_to_circuit(s, s)   # prepares |+⟩

bloch_to_circuit(theta, phi, target=0)

Prepares the qubit state parameterized by Bloch-sphere polar angles.

import math
from rqm_braket import bloch_to_circuit

circuit = bloch_to_circuit(math.pi / 2, 0.0)  # prepares |+⟩

Quaternion (re-exported from rqm-core)

The Quaternion class from rqm-core is re-exported for user convenience. All quaternion mathematics lives in rqm-core.

from rqm_braket import Quaternion

q = Quaternion.from_axis_angle("z", math.pi / 2)

What This Package Owns and Does NOT Own

rqm-braket owns

Capability Description
Braket lowering Translation of compiler output into Amazon Braket Circuit / task objects
Backend execution helpers run_local, run_device, run_device_async
AWS / Braket device integration Device discovery, task submission, status polling
Result normalization BraketResult wrapper around Braket task outputs

rqm-braket does NOT own

Concern Owner
Quaternion / SU(2) math rqm-core
Spinor normalization rqm-core
Bloch sphere conversions rqm-core
Canonical external circuit schema rqm-circuits
Optimization pass design rqm-compiler
Internal circuit compilation logic rqm-compiler
API wire format rqm-circuits / rqm-api
Studio payload format rqm-circuits / rqm-api

The rule: rqm-braket may call math and compiler APIs, but never define them.

Installation

pip install rqm-braket

Development install:

pip install -e .

Quick Start (Compiled Program)

from rqm_braket import BraketBackend, RQMGate

program = [
    RQMGate("H", target=0),
    RQMGate("CNOT", control=0, target=1),
]

backend = BraketBackend()

result = backend.run_local(program, shots=1000)

print(result.counts)

Usage Modes

rqm-braket supports multiple entry points depending on your audience and use case.

Mode 1 — Compiler-first (recommended for production)

rqm-circuits → rqm-compiler → compiled_program → backend.run(...)

from rqm_braket import BraketBackend, RQMGate

backend = BraketBackend()
result = backend.run_local([
    RQMGate("H", target=0),
    RQMGate("CNOT", control=0, target=1),
], shots=500)
print(result.counts)

Intended for: researchers, engineers, production workflows.

Note: In a full stack flow, the gate sequence originates as an rqm-circuits payload, is parsed and optimized by rqm-compiler, and the compiler's output is then passed into rqm-braket. Using RQMGate directly (as above) is fine for direct scripting and experiments.

Mode 2 — Descriptor-first (recommended for API layer)

rqm-circuits → rqm-compiler → descriptors (JSON) → run_descriptors(...)

from rqm_braket import run_descriptors

descriptors = [
    {"gate": "h", "targets": [0], "controls": [], "params": {}},
    {"gate": "cx", "targets": [1], "controls": [0], "params": {}},
]
result = run_descriptors(descriptors, shots=200)
print(result.to_dict(include_probabilities=True))

Intended for: the rqm-api layer and RQM Studio integration.

Note: Descriptor lists are the compiler-internal format produced by rqm_compiler.Circuit.to_descriptors(). In Studio / API workflows the original circuit is expressed in rqm-circuits format and is parsed and optimized by rqm-compiler before descriptors reach rqm-braket.

Mode 3 — Asynchronous device execution

run_device_async(...) → task_arn → get_task_status(arn) → get_task_result(arn)

from rqm_braket import run_device_async, get_task_status, get_task_result

task_arn = run_device_async(
    program,
    device_arn="arn:aws:braket:::device/quantum-simulator/amazon/sv1",
    s3_folder=("my-bucket", "results"),
    shots=100,
)

status = get_task_status(task_arn)
print(status)  # "QUEUED", "RUNNING", "COMPLETED", ...

if status == "COMPLETED":
    result = get_task_result(task_arn)
    print(result.counts)

Intended for: long-running QPU jobs where blocking is undesirable.

Mode 4 — Bridge functions (convenient for exploration)

spinor_to_circuit(...)
bloch_to_circuit(...)

import math
from rqm_braket import spinor_to_circuit, bloch_to_circuit, run_local

# From a spinor
s = 1 / math.sqrt(2)
circuit = spinor_to_circuit(s, s)
result = run_local(circuit, shots=200)
print(result.counts)

# From Bloch angles
circuit = bloch_to_circuit(math.pi / 2, 0.0)
result = run_local(circuit, shots=200)
print(result.counts)

Intended for: students, tutorials, quick experiments.

All quantum mathematics (Bloch conversion, spinor normalization) is delegated to rqm-core. rqm-braket only maps the results to gates.

Running from RQM Studio

RQM Studio communicates with rqm-api, which calls into rqm-braket. The recommended call pattern is:

  1. Design circuit in RQM Studio UI → expressed as an rqm-circuits payload.
  2. Compile via rqm-compiler → validates, optimizes, and produces descriptors.
  3. Choose device via GET /v1/devices → calls list_devices().
  4. Submit job via POST /v1/run/async → calls run_device_async(...), returns task_arn.
  5. Poll status via GET /v1/tasks/<task_arn>/status → calls get_task_status(task_arn).
  6. Retrieve result via GET /v1/tasks/<task_arn>/result → calls get_task_result(task_arn).
  7. Visualize result in RQM Studio UI.

For synchronous (blocking) local or device runs use POST /v1/run.

rqm-braket only participates from step 4 onward. The public circuit schema and wire format belong to rqm-circuits; rqm-braket receives already- compiled / already-validated data.

Integrating the Blueprint

from flask import Flask
from rqm_braket.api import api_blueprint

app = Flask(__name__)
app.register_blueprint(api_blueprint, url_prefix="/v1")

Device selection

from rqm_braket import list_devices

# List all simulators
simulators = list_devices(device_types=["SIMULATOR"])

# List all QPUs
qpus = list_devices(device_types=["QPU"])

# RQM Studio can display these to the user for device selection

AWS credentials

rqm-braket uses the standard AWS credential chain. Configure via:

  • aws configure (CLI)
  • Environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION)
  • IAM roles (recommended for production)

Never store AWS credentials in code.

S3 result storage

Device execution requires an S3 bucket for Braket to store task results. For large circuits or production deployments, use a dedicated S3 bucket and prefix managed by rqm-api to consolidate result storage across jobs.

result = run_descriptors(
    descriptors,
    backend="device",
    device_arn="arn:aws:braket:us-east-1::device/qpu/ionq/Harmony",
    s3_folder=("your-braket-bucket", "rqm-results"),
    shots=1000,
)

Direct Translation Example

from rqm_braket import compile_to_braket_circuit, RQMGate

program = [
    RQMGate("RX", target=0, angle=1.57),
]

circuit = compile_to_braket_circuit(program)

print(circuit)

Examples

Local simulator

examples/basic_local_simulator.py

Bell state

examples/bell_state_demo.py

Compiled program demo

examples/compiled_program_demo.py

Public API

REST API Blueprint (rqm-api integration)

api_blueprint           # Flask Blueprint — mount in rqm-api Flask application

Mount in your rqm-api application:

from flask import Flask
from rqm_braket.api import api_blueprint

app = Flask(__name__)
app.register_blueprint(api_blueprint, url_prefix="/v1")

Endpoints exposed:

Method Path Description
POST /v1/run Execute circuit synchronously (local or device backend)
POST /v1/run/async Submit circuit to AWS Braket device; returns task_arn
GET /v1/tasks/<task_arn>/status Poll task state (QUEUED, RUNNING, COMPLETED, …)
GET /v1/tasks/<task_arn>/result Retrieve result of completed task
GET /v1/devices List available AWS Braket devices

Install the optional [api] extra to pull in Flask:

pip install rqm-braket[api]

Core backend API

BraketBackend           # unified backend object
BraketTranslator        # compile programs → Braket Circuit
RQMGate                 # typed gate descriptor
compile_to_braket_circuit  # convenience translation
run_local               # execute on local simulator (offline-safe)
run_device              # execute on AWS Braket device (synchronous)
BraketResult            # result wrapper

Async & task management

run_device_async        # submit job → task ARN (non-blocking)
get_task_status         # query task state ("QUEUED" / "RUNNING" / ...)
get_task_result         # retrieve BraketResult for completed task

Device discovery

list_devices            # list available AWS Braket devices

Descriptor-first execution

run_descriptors         # translate descriptors + execute (API-ready)

Error handling

BraketDeviceError       # raised for device/task failures (RuntimeError subclass)

Convenience bridges (rqm-core delegation)

spinor_to_circuit   # spinor (α, β) → Braket Circuit
bloch_to_circuit    # Bloch angles (θ, φ) → Braket Circuit
Quaternion          # re-exported from rqm-core

Execution Modes

Local (offline-safe)

result = run_local(program, shots=100)

No AWS credentials required.

AWS Device (synchronous)

result = run_device(
    program,
    device_arn="arn:aws:braket:...",
    s3_folder=("bucket", "prefix"),
    shots=100
)

Requires standard AWS + Braket configuration.

AWS Device (asynchronous)

task_arn = run_device_async(
    program,
    device_arn="arn:aws:braket:...",
    s3_folder=("bucket", "prefix"),
    shots=100,
)
status = get_task_status(task_arn)     # "QUEUED", "RUNNING", "COMPLETED", ...
result = get_task_result(task_arn)     # BraketResult (blocks until done)

Descriptor-first (API layer)

result = run_descriptors(
    descriptors,
    shots=100,
    backend="local",   # or "device"
)

Development

Run tests:

pytest

All tests are:

  • offline-safe
  • no AWS credentials required
  • include mocked cloud execution

Design Principles

Math Delegation

rqm-braket does not implement canonical quantum mathematics.

All physics and math operations are delegated to rqm-core:

rqm-core      = physics + math
rqm-circuits  = public/external circuit schema
rqm-compiler  = optimization + internal instruction model
rqm-braket    = Braket lowering + execution

The rule: rqm-braket may call math and compiler APIs, but never define them.

Thin Adapter Layer

rqm-braket is intentionally minimal:

  • no duplicated logic
  • no second IR
  • no math reimplementation
  • no redefinition of the public circuit schema

Compiler Boundary

Direct inputs to rqm-braket come from:

rqm-compiler

The full upstream path is:

rqm-circuits  (public schema)
      ↓
rqm-compiler  (optimization / internal IR)
      ↓
rqm-braket    (Braket lowering + execution)

This ensures:

  • backend independence
  • clean separation of concerns
  • extensibility to new platforms
  • rqm-braket never owns or parses the public wire format

Backend Agnostic Design

Because the compiler produces a canonical instruction format:

rqm-compiler → rqm-qiskit
rqm-compiler → rqm-braket
rqm-compiler → future backends

Versioning

Current version: 0.2.0

This release introduces:

  • compiler-based architecture
  • BraketBackend abstraction
  • clean translation/execution separation
  • async execution (run_device_async, get_task_status, get_task_result)
  • device discovery (list_devices)
  • descriptor-first execution (run_descriptors)
  • extended result serialization (BraketResult.to_dict optional extras)
  • BraketDeviceError for friendly error handling
  • backward-compatibility shims (deprecated)

Roadmap

Future improvements may include:

  • parameter binding support via Braket FreeParameter (TODO: propose adding parametric circuit support to rqm-core or rqm-compiler)
  • batched execution
  • hybrid Braket workflows
  • richer result analysis
  • multi-qubit optimization paths
  • S3 result storage managed by rqm-api

License

Apache License 2.0

Copyright (c) RQM Technologies

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