EMRG

Error Mitigation Recipe Generator -- Automatic quantum error mitigation for NISQ circuits.

EMRG analyzes your quantum circuit and generates ready-to-run, explained Mitiq-powered error mitigation code. No manual tuning required.

Status: v0.1.0 -- MVP. Actively developed, grant-funded roadmap ahead.

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Why EMRG?

Noise limits every computation on today's hardware. Error mitigation techniques like Zero-Noise Extrapolation (ZNE) can boost fidelity 2--10x, but configuring them manually is tedious:

• Which extrapolation factory? Linear, Richardson, Polynomial?
• What scale factors for your circuit depth?
• How do you balance overhead vs. accuracy?

EMRG handles this automatically. Give it a circuit, get back optimized mitigation code with clear explanations of why each choice was made.

How It Works

Quantum Circuit --> [Analyze] --> [Heuristic Engine] --> [Code Generator] --> Mitigated Code

1. Parse & Validate -- Load a Qiskit QuantumCircuit or QASM file
2. Extract Features -- Depth, gate counts, multi-qubit gate density, estimated noise factor
3. Apply Heuristics -- Rule-based decision tree selects the best mitigation recipe
4. Generate Code -- Output runnable Python with Mitiq imports, factory config, and inline rationale

Heuristic Rules (v0.1)

Circuit Profile	Factory	Scale Factors	Rationale
Depth < 20, low multi-qubit gates	LinearFactory	[1.0, 1.5, 2.0]	Conservative for shallow circuits
Depth 20--50	RichardsonFactory	[1.0, 1.5, 2.0, 2.5]	Better extrapolation for moderate noise
Depth > 50 or high noise	PolyFactory (deg 2--3)	[1.0, 1.5, 2.0, 2.5, 3.0]	Handles non-linear noise scaling

Quick Start

Installation

pip install emrg

Or from source:

git clone https://github.com/FedorShind/EMRG.git
cd EMRG
pip install -e ".[dev]"

CLI Usage

# Generate mitigation recipe from a QASM file
emrg generate docs/examples/bell_state.qasm

# With verbose explanation
emrg generate docs/examples/bell_state.qasm --explain

# Save to file
emrg generate circuit.qasm -o mitigated.py

# Analyze circuit features
emrg analyze docs/examples/simple_vqe.qasm

# JSON output (for scripting)
emrg analyze circuit.qasm --json

Python API

from qiskit import QuantumCircuit
from emrg import generate_recipe

# Create a circuit
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])

# Generate mitigation recipe (one-liner)
result = generate_recipe(qc)
print(result)             # Ready-to-run Python script
print(result.rationale)   # Why these parameters were chosen
print(result.features)    # Circuit analysis details

# With verbose explanations
result = generate_recipe(qc, explain=True)

Example Output

# =============================================================
# EMRG v0.1.0 -- Error Mitigation Recipe
# Circuit: 2 qubits, depth 3, 1 multi-qubit gates
# Noise estimate: 0.011 (low)
# =============================================================
#
# Recommendation: LinearFactory + fold_global
#
# =============================================================

from mitiq.zne import execute_with_zne
from mitiq.zne.inference import LinearFactory
from mitiq.zne.scaling import fold_global

factory = LinearFactory(scale_factors=[1.0, 1.5, 2.0])

def execute(circuit):
    """Execute a circuit and return an expectation value (float)."""
    # Replace with your actual backend
    raise NotImplementedError("Replace this with your executor.")

mitigated_value = execute_with_zne(
    circuit,
    execute,
    factory=factory,
    scale_noise=fold_global,
)

print(f"Mitigated expectation value: {mitigated_value}")

Project Structure

EMRG/
├── src/emrg/
│   ├── __init__.py      # Public API and generate_recipe()
│   ├── _version.py      # Single source of truth for version
│   ├── analyzer.py      # Circuit feature extraction
│   ├── heuristics.py    # Rule-based decision engine
│   ├── codegen.py       # Template-based code generation
│   ├── cli.py           # Click CLI interface
│   └── py.typed         # PEP 561 type marker
├── tests/               # 144 pytest tests, 98% coverage
├── docs/examples/       # Example circuits (Python + QASM)
└── pyproject.toml       # Package configuration

Benchmarks

Real measurements from EMRG v0.1.0, collected automatically by benchmarks/run_benchmark.py.

Environment: Python 3.12, Windows 11 | Qiskit 2.3.0, Mitiq 0.48.1

Tool Performance

EMRG relies on pure Qiskit introspection (no simulation), so generate_recipe() completes in sub-millisecond time even for large circuits. Median of 100 runs:

Circuit	Qubits	Depth	Gates	Multi-Q	Factory	Time	Memory
Bell state	2	3	2	1	LinearFactory	0.033 ms	3.8 KB
GHZ-5	5	6	5	4	LinearFactory	0.047 ms	3.8 KB
GHZ-10	10	11	10	9	LinearFactory	0.069 ms	3.8 KB
Random 10q, 3 layers	10	7	45	15	LinearFactory	0.159 ms	4.1 KB
VQE 10q, 4 layers	10	20	76	36	PolyFactory	0.234 ms	3.9 KB
Random 20q, 6 layers	20	13	180	60	PolyFactory	0.478 ms	6.2 KB
Random 30q, 10 layers	30	21	450	150	PolyFactory	1.10 ms	7.9 KB
Random 50q, 15 layers	50	31	1125	375	PolyFactory	2.59 ms	11.1 KB

A 50-qubit, 1125-gate circuit is analyzed and produces a full mitigation recipe in under 3 ms with ~11 KB memory overhead.

ZNE Fidelity

To validate that EMRG selects effective mitigation parameters, we ran ZNE end-to-end on noisy simulations (Cirq DensityMatrixSimulator with per-gate depolarizing noise) and compared the <Z> expectation value on qubit 0:

Circuit	Qubits	Depth	Noise	Factory	Ideal	Noisy	Mitigated	Error Reduction
X-flip, 2q	2	3	p=0.01	LinearFactory	-1.0000	-0.9761	-1.0003	77x
X-flip, 3q	3	4	p=0.01	LinearFactory	-1.0000	-0.9761	-1.0003	77x
X-flip, 2q	2	3	p=0.05	LinearFactory	-1.0000	-0.8836	-0.9906	12x
X-flip, 3q	3	4	p=0.05	LinearFactory	-1.0000	-0.8836	-0.9906	12x
VQE 4q, 2 layers	4	8	p=0.01	LinearFactory	0.0850	0.0775	0.0794	1.4x
VQE 4q, 4 layers	4	14	p=0.01	LinearFactory	-0.1915	-0.1766	-0.1850	2.3x
VQE 4q, 2 layers	4	8	p=0.05	LinearFactory	0.0850	0.0523	0.0586	1.2x

EMRG-generated ZNE recipes reduce error across all tested circuits, with improvements from 1.2x on high-noise VQE ansatze up to 77x on structured low-noise circuits.

Reproduce

pip install -e ".[dev]" qiskit-aer
python benchmarks/run_benchmark.py

Roadmap

Phase 1 -- MVP (current)

Everything needed to go from circuit to mitigation recipe in one command:

• Project structure and packaging
• Circuit analyzer (feature extraction)
• Heuristic engine (ZNE: Linear + Richardson + Poly)
• Code generator (template-based)
• CLI with generate and analyze commands
• Public Python API (generate_recipe())
• Example circuits (Python + QASM) and documentation
• 144 tests, 98% coverage, zero lint warnings

Phase 2 -- More techniques, better validation

Expand beyond ZNE so EMRG can recommend the right technique, not just the right ZNE settings:

• Probabilistic Error Cancellation (PEC) support
• Layerwise Richardson integration
• --preview mode (noisy simulation + fidelity plots)
• Real hardware benchmarks (IBM Quantum devices)
• Expanded tutorials (VQE, QAOA, random circuits)

Phase 3 -- Multi-framework and community

Make EMRG useful regardless of which framework you use:

• Cirq and PennyLane input support
• Configurable heuristics file
• Web/Colab interface

Tech Stack

• Python 3.10+
• Qiskit >= 1.0 -- Circuit representation and introspection
• Mitiq >= 0.48 -- Error mitigation primitives
• Click >= 8.0 -- CLI framework

Contributing

EMRG is open source and contributions are welcome. If you have ideas, find bugs, or want to add support for new mitigation techniques, open an issue or PR. BTC address: bc1qaxdd227afjp3ym5t7jn2atmacjqx3auh9urwfu

License

MIT -- Free for academic and commercial use.

Acknowledgments

Built on Mitiq by Unitary Foundation. Inspired by the need to make quantum error mitigation accessible to everyone working with NISQ hardware.

Side note

If you're stuck or don't understand something, I strongly advise you to ask AI for help. Cheers!