Developers — qgate

Quick Start

Installation

          Terminal
          pip
        
          pip install qgate

Requires Python 3.9+. Compatible with Qiskit 0.45+ and standard IBM Quantum backends. See the GitHub README for full dependency details.

Resources

Developer Resources

GitHub Repository

Source code, issues, and contribution guidelines for qgate-trajectory-filter.

PyPI Package

Install the latest release, view version history, and check dependency requirements.

Documentation

Full API documentation and tutorials. Coming soon — see the GitHub README for current usage guidance.

Coming soon

Public Package Scope

What's Included

✓ Trajectory filtering for measurement outcomes
✓ Qiskit-compatible integration
✓ Hardware-aware filtering parameters
✓ Standard IBM Quantum backend support
✓ Level-2 (binary) measurement data processing

Not in Public Release

What's Not Included

— Calibration-driven observable reconstruction
— Real-time telemetry-driven control
— Level-1 / Level-0 data processing
— FPGA deployment tooling
— Patent-pending control algorithms

Advanced capabilities are part of the broader qgate architecture and are not available in the open-source package.

Example

Quick Usage Example

Basic trajectory filtering integration with a Qiskit workflow.

            Python
            example
          

            # Example: Apply trajectory filtering to a Qiskit circuit result
from qiskit import QuantumCircuit
from qiskit_ibm_runtime import QiskitRuntimeService, Sampler

# Import qgate trajectory filter
from qgate import TrajectoryFilter

# Build a circuit
qc = QuantumCircuit(5)
qc.h(0)
for i in range(4):
    qc.cx(i, i + 1)
qc.measure_all()

# Execute on hardware
service = QiskitRuntimeService()
backend = service.backend("ibm_brisbane")
sampler = Sampler(backend)
result = sampler.run(qc, shots=4096).result()

# Apply trajectory filtering
tf = TrajectoryFilter(backend=backend)
filtered = tf.filter(result)

print(filtered.expectation_values)
          

This is a simplified placeholder example. See the GitHub repository for actual API documentation and usage patterns.

Start with Trajectory Filtering