Quickstart¶

Write once. Run on any quantum backend.

Get up and running with the RQM platform in a few minutes.

1. Install¶

Install the execution backend you want to use. Each backend pulls in rqm-core and rqm-compiler automatically.

AWS Braket backend:

pip install rqm-braket

rqm-braket installs the full runtime stack — rqm-core, rqm-compiler, and the Braket execution layer — so this single command is all you need to start running programs.

Qiskit backend:

pip install rqm-qiskit

rqm-qiskit installs the full runtime stack — rqm-core, rqm-compiler, and the Qiskit execution layer — so this single command is all you need to start running programs on Qiskit.

Install both:

pip install rqm-braket rqm-qiskit

Virtual environment recommended

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install rqm-braket

2. Run Your First Program¶

Write a program using RQM's backend-agnostic gate model, then execute it:

from rqm_braket import BraketBackend, RQMGate

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

backend = BraketBackend()
result = backend.run_local(program)

print(result.counts)

Same program, different backend

The same program list can be executed on a Qiskit backend using the same compiler pipeline:

from rqm_qiskit import QiskitBackend

backend = QiskitBackend()
result = backend.run_local(program)
print(result.counts)

Swapping backends requires only changing the import and backend constructor. The program and compiler pipeline are identical.

3. How It Works¶

When you call backend.run_local(program), the platform executes this pipeline:

program (RQMGate list)
    → rqm-compiler  (normalize and lower to IR)
    → rqm-braket    (map IR to Braket circuits)
    → local simulator
    → result.counts

rqm-core provides the underlying math (quaternions, spinors, SU(2)) that the compiler uses during instruction normalization.

4. Work Directly with the Math Layer¶

If you need direct access to the canonical math layer:

from rqm_core.spinor import normalize_spinor
from rqm_core.bloch import state_to_bloch

psi_raw = [1.0, 1.0]
psi = normalize_spinor(psi_raw)
bloch = state_to_bloch(psi)

print("Normalized spinor:", psi)
print("Bloch vector:", bloch)
# Bloch vector: [1.0, 0.0, 0.0]  (|+⟩ state, points along +x)

Next Steps¶

Goal	Where to go
Understand how the layers connect	Concepts
Full ecosystem overview	Ecosystem
Full install options (editable, from GitHub)	Installation
Math API reference	rqm-core API
Qiskit execution reference	rqm-qiskit API