Metadata-Version: 2.4
Name: limbo-quantum
Version: 0.1.0
Summary: The developer SDK for multi-chip quantum computing.
Project-URL: Homepage, https://limbosys.dev
Project-URL: Documentation, https://github.com/Jminding/limbo-quantum#readme
Project-URL: Repository, https://github.com/Jminding/limbo-quantum
Project-URL: Issues, https://github.com/Jminding/limbo-quantum/issues
Project-URL: Changelog, https://github.com/Jminding/limbo-quantum/blob/main/CHANGELOG.md
Author-email: Jaymin Ding <jd1152@princeton.edu>
License:                                  Apache License
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License-File: LICENSE
Keywords: cirq,compiler,distributed-quantum,multi-chip,openqasm,qaoa,qiskit,quantum,vqe
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
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Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Description-Content-Type: text/markdown

# limbo-quantum

The developer SDK for multi-chip quantum computing.

`limbo-quantum` is the client-side companion to the **Limbo** compiler — a
quantum compilation stack designed around the assumption that the
next-generation device isn't one giant chip but a network of chips
stitched together by photonic interconnects. The SDK gives you a single
Python entry point that ingests circuits from Qiskit, Cirq, or OpenQASM
3, runs a preflight capacity audit + hotspot scan, transpiles for your
target device, and submits to whichever backend you've configured.

The local simulator path runs fully in-process. There's also an
optional hosted backend at [limbosys.dev](https://limbosys.dev) that
runs the multi-chip-aware partitioner + cross-chip scheduler described
in the paper — bring your own API key, the SDK doesn't require it.

---

## Install

```bash
pip install limbo-quantum
```

The SDK itself is pure-Python with minimal hard dependencies. Install
whichever circuit framework matches the format you'll feed in:

```bash
pip install qiskit qiskit-aer    # for QuantumCircuit + local simulation
pip install cirq-core            # for Cirq Circuit ingestion
pip install openqasm3            # for OpenQASM 3 source ingestion
```

---

## Quickstart

```python
from qiskit import QuantumCircuit
from limbo_quantum import DistributedCompilerSDK, LocalSimulationProvider

# 1. A familiar Qiskit circuit.
qc = QuantumCircuit(4)
qc.h(0)
for i in range(3):
    qc.cx(0, i + 1)
qc.measure_all()

# 2. Configure the SDK with a local simulator backend. Everything from
#    here on runs on your laptop — no cloud account required.
sdk = DistributedCompilerSDK(
    provider=LocalSimulationProvider(),
    num_qpus=2,
    max_capacity_per_qpu=4,
)

# 3. One call → ingest → lint → compile → simulate → counts.
result = sdk.compile(qc, shots=1024)
print(result.counts)
print(f"compiled to {len(result.compiled_qasm)} sub-circuit(s)")
```

That's the whole workflow. To run on real hardware, swap
`LocalSimulationProvider()` for a `CloudProductionProvider` (see
[Cloud backend](#cloud-backend-optional)) or a custom `QuantumProvider`
subclass that targets your own infrastructure.

---

## What's in the box

| Component | What it does |
|---|---|
| `DistributedCompilerSDK` | One-call entry point. Ingest → lint → optional pre-optimize → compile → execute. |
| `CircuitParser` | Multi-framework ingestion: Qiskit / Cirq / OpenQASM 3 → standardized JSON IR. |
| `LocalStaticLinter` | Offline capacity audit + interaction-hotspot detection. Catches broken layouts before any network round-trip. |
| `ParametricRuntime` + `TopologicalTemplate` | Compile-once-bind-many runtime for variational workloads (VQE / QAOA). Parameters bind locally; the compile step only re-runs when the circuit's structural signature changes. |
| `LocalSimulationProvider` | In-process Aer simulator. Zero credentials, zero network. |
| `CloudProductionProvider` | HTTP client for the hosted Limbo backend at limbosys.dev. Optional. |
| `QuantumProvider` | Abstract base. Subclass it to point the SDK at your own infrastructure. |
| `TelemetryLogger` | Auto-detects Jupyter vs. terminal, prints per-step timings + payload sizes. Drop-in optional. |

---

## Features in depth

### Multi-framework ingestion

```python
from limbo_quantum import CircuitParser

# All three return the same IR shape.
ir = CircuitParser.from_qiskit(my_qiskit_circuit)
ir = CircuitParser.from_cirq(my_cirq_circuit)
ir = CircuitParser.from_openqasm3(my_qasm3_source_string)
```

The IR is plain JSON (no Python-specific types) so it round-trips
cleanly through caches, message queues, and HTTP payloads. The schema
descriptor is exported as `limbo_quantum.IRSchema`.

### Multi-chip capacity audit

The capacity linter is the SDK's most distinctive feature for
multi-chip targets. Qiskit's transpiler checks coupling maps during
compilation; Limbo's linter checks *layout feasibility* up front, with
a structured exception you can catch.

```python
from limbo_quantum import (
    CircuitParser, LocalStaticLinter, TopologyCapacityError,
)

ir = CircuitParser.from_qiskit(big_circuit)
linter = LocalStaticLinter(ir, num_qpus=4, max_capacity_per_qpu=20)
try:
    linter.capacity_audit()
except TopologyCapacityError as exc:
    print(f"layout infeasible: requested {exc.num_qubits}q, "
          f"available {exc.target_capacity}, "
          f"need per-QPU >= {exc.min_qubits_per_qpu}")
```

### Hotspot dashboard

Identifies "hub" qubits whose interaction degree dominates the circuit
— the ones that will burn cross-chip traffic when partitioned. By
default any qubit holding more than 30% of total degree is flagged.

```python
linter = LocalStaticLinter(ir)
report = linter.hotspot_detection(threshold_ratio=0.30)
# When verbose=True (the default), prints a formatted ASCII table to
# stdout. The structured report is also returned for CI integration.
```

### Parametric template caching (variational amortization)

For VQE / QAOA workflows, `ParametricRuntime` compiles the template
once and binds parameters locally on each subsequent call:

```python
from qiskit import QuantumCircuit
from qiskit.circuit import Parameter
from limbo_quantum import ParametricRuntime, InProcessCompilerClient

theta = Parameter("theta")
qc = QuantumCircuit(2)
qc.ry(theta, 0)
qc.cx(0, 1)
qc.measure_all()

runtime = ParametricRuntime(qc, client=InProcessCompilerClient())

for theta_val in [0.0, 0.1, 0.2, 0.3]:
    result = runtime.run({theta: theta_val}, shots=512)
    print(theta_val, result.counts)
```

The first `.run()` does the heavy compile. Every subsequent call binds
into the cached template and skips re-compilation. The cache key is a
SHA-256 of the circuit's *structural* signature, so different parameter
values share a cache entry but a different gate sequence forces a
re-compile.

### Cost-preview mode

Compile without executing — useful when you want to see the metrics
(number of partitions, cross-chip operations, output gate count)
before spending shots:

```python
result = sdk.compile(qc, execute=False)
print(result.compiled_qasm)
print(result.lint_report)
```

### Custom backends

The `QuantumProvider` abstract base has three methods to implement:

```python
from limbo_quantum import QuantumProvider, Job, JobResult

class MyBackend(QuantumProvider):
    name = "mine"

    def submit(self, payload, *, shots=1024, **options) -> Job:
        ...

    def _fetch_status(self, job_id: str) -> JobResult:
        ...

    def list_devices(self, credentials=None):
        ...

sdk = DistributedCompilerSDK(provider=MyBackend(), num_qpus=1, max_capacity_per_qpu=32)
```

That's enough to swap in your own infrastructure — AWS Braket, IBM
Runtime directly, a self-hosted Aer cluster, anything.

### Telemetry

```python
from limbo_quantum import (
    DistributedCompilerSDK, LocalSimulationProvider, TelemetryLogger,
)

sdk = DistributedCompilerSDK(
    provider=LocalSimulationProvider(),
    num_qpus=2,
    max_capacity_per_qpu=4,
    telemetry=TelemetryLogger(),         # auto-detects terminal vs. Jupyter
)
```

Per-step timings, payload sizes, and lint outcomes print to the host
as a clean timeline.

### Auto-provider

```python
from limbo_quantum import auto_provider, DistributedCompilerSDK

# CloudProductionProvider if LIMBO_API_KEY is set in the env, else local.
sdk = DistributedCompilerSDK(
    provider=auto_provider(),
    num_qpus=2,
    max_capacity_per_qpu=8,
)
```

---

## Execution backends

`limbo-quantum` does not lock you into any single execution path. The
SDK is structured around a narrow `QuantumProvider` interface
([provider.py](./provider.py)) with three implementations shipped in
the box and a documented extension point for everything else. Pick the
one that matches your workflow:

### Path A — Local simulator (no account, no network)

The default. Runs Qiskit Aer in-process.

```python
from limbo_quantum import DistributedCompilerSDK, LocalSimulationProvider

sdk = DistributedCompilerSDK(
    provider=LocalSimulationProvider(),
    num_qpus=2, max_capacity_per_qpu=4,
)
```

### Path B — Hosted Limbo backend (one account, multiple vendors)

`CloudProductionProvider` is an HTTP client for the hosted Limbo
service at [limbosys.dev](https://limbosys.dev). The hosted service
holds your IBM / AWS Braket / Azure Quantum credentials encrypted
server-side and runs the multi-chip-aware partitioner + cross-chip
scheduler. You manage one API key; the service brokers the rest.

```python
from limbo_quantum import DistributedCompilerSDK, CloudProductionProvider

sdk = DistributedCompilerSDK(
    provider=CloudProductionProvider(
        api_key="lmb_live_...",                  # from your dashboard
        server_url="https://api.limbosys.dev",
    ),
    num_qpus=4, max_capacity_per_qpu=20,
)
```

### Path C — Direct to a vendor (IBM / AWS / Azure)

If you already have vendor quotas and want to keep them, the SDK
ships first-class wrappers for the three major clouds. Install the
right extra and pass an instance to the SDK — no subclassing required.

**IBM Quantum** (`pip install 'limbo-quantum[ibm]'`):

```python
from limbo_quantum import DistributedCompilerSDK, IBMProvider

sdk = DistributedCompilerSDK(
    provider=IBMProvider(
        token="...",                             # from quantum.ibm.com
        backend_name="ibm_brisbane",
    ),
    num_qpus=1, max_capacity_per_qpu=127,        # single-chip target
)
result = sdk.compile(qc, shots=1024)
```

**AWS Braket** (`pip install 'limbo-quantum[aws]'`):

```python
from limbo_quantum import DistributedCompilerSDK, AWSProvider

sdk = DistributedCompilerSDK(
    provider=AWSProvider(
        device_arn="arn:aws:braket:::device/quantum-simulator/amazon/sv1",
        # s3_folder=("my-results-bucket", "limbo/"),  # optional
    ),
    num_qpus=1, max_capacity_per_qpu=34,
)
```

AWS credentials are picked up from the standard boto3 chain
(`AWS_ACCESS_KEY_ID` / `AWS_SECRET_ACCESS_KEY` env vars,
`~/.aws/credentials`, or an attached IAM role).

**Azure Quantum** (`pip install 'limbo-quantum[azure]'`):

```python
from limbo_quantum import DistributedCompilerSDK, AzureProvider

sdk = DistributedCompilerSDK(
    provider=AzureProvider(
        subscription_id="...",
        resource_group="quantum-rg",
        workspace_name="my-workspace",
        location="eastus",
        target_name="ionq.qpu",
    ),
    num_qpus=1, max_capacity_per_qpu=23,
)
```

Azure authentication uses `DefaultAzureCredential` from
`azure-identity` — i.e. `az login` on a laptop, a managed identity in
production, or service-principal env vars.

All three vendor providers handle the QASM → vendor-circuit
translation, job submission, status polling, and result decoding for
you. They lazy-import their vendor SDK so `import limbo_quantum` stays
fast even if you've only installed one of the three extras.

### Path D — Bring your own backend

If you're targeting something else (Quantinuum, IonQ direct, Rigetti,
a self-hosted Aer cluster, a private FPGA, anything), subclass
`QuantumProvider` with three methods:

```python
from limbo_quantum import QuantumProvider, Job, JobResult

class MyBackend(QuantumProvider):
    name = "mine"

    def submit(self, payload, *, shots=1024, **options) -> Job:
        """Send the compiled QASM. Return a Job wrapping the backend's
        native job ID."""
        ...

    def _fetch_status(self, job_id: str) -> JobResult:
        """Poll. Return a JobResult with status in
        {queued, running, completed, failed}; on completion include
        the measurement counts."""
        ...

    def list_devices(self) -> list[dict]:
        """Optional device catalog for auto-routing. Can be []."""
        ...
```

The shipped IBM / AWS / Azure providers in [vendors.py](./vendors.py)
are full reference implementations of this pattern — copy one as a
starting point.

### Which path to choose

| Scenario | Use |
|---|---|
| Prototyping, CI, anything Aer can run | **Path A** (`LocalSimulationProvider`) |
| One bill, one credential, multi-chip-aware compile server-side | **Path B** (`CloudProductionProvider`) |
| You already have IBM / AWS / Azure quotas | **Path C** (`IBMProvider` / `AWSProvider` / `AzureProvider`) |
| Anything else | **Path D** (subclass `QuantumProvider`) |

All four paths get you the same client API — only the `provider=`
argument changes.

---

## Correctness scope

Limbo is a faithful transport for circuits that fit on your chosen
target device. If your circuit fits on the device (i.e.
`circuit_qubits <= device_qubits`), the SDK ingests, optionally
pre-optimizes (Qiskit passes only), transpiles against the device's
coupling map, and submits via the provider's official SDK. The result
is semantically identical to what direct Qiskit + the vendor's SDK
would produce.

The **multi-chip routing path** (when your circuit doesn't fit on a
single physical device and the SDK partitions across multiple QPUs) is
research-grade. There is no commercial cloud QPU today that exposes a
multi-chip interconnect, so the cross-chip operations the SDK produces
can only be validated on a simulator. **Don't ship a `num_qpus > 1`
configuration to a single-chip cloud backend and expect correct
results** — the cross-chip entanglement won't be implemented.

---

## Error model

| Exception | When |
|---|---|
| `ImportError` | Framework SDK isn't installed (`from_qiskit` with no `qiskit`, etc.) |
| `TypeError`   | Wrong object type passed in (e.g. a Cirq circuit to `from_qiskit`) |
| `ValueError`  | Malformed IR, bad QASM source, invalid configuration |
| `TopologyCapacityError` | Capacity audit rejected the layout |
| `TimeoutError` | Cloud job didn't finish within the configured polling window |

All errors are raised locally before any network call, except
`TimeoutError` (raised after polling).

---

## Citing

If you use Limbo's multi-chip compilation in published work, please
cite the paper (forthcoming, IEEE).

---

## License

Apache 2.0. See [LICENSE](../LICENSE).

---

## Status

Public release planned for Fall 2026. Pre-1.0 versions on PyPI should
be considered beta — APIs may change in minor versions until then.
