Agentic UI for Qiskit: Prototype a Desktop App that Suggests Circuit Improvements

Hook: Your Qiskit code is private — your assistant should be too

If you are a quantum developer or engineering manager, you know the frustration: best-effort circuit suggestions live in fragmented tools, cloud services leak IP risk, and vendor SDKs offer inconsistent optimizations. Imagine a desktop assistant that listens to your Qiskit session, proposes gate-level improvements, runs local tests (unitary and noisy simulations), and never sends code to the cloud. This guide shows how to prototype an agentic, privacy-first desktop assistant for Qiskit in 2026—leveraging local LLM inference, Qiskit's compiler passes, and reproducible local testing.

Why build an agentic desktop assistant in 2026?

Late 2025 and early 2026 saw two converging trends that make a privacy-first desktop quantum assistant practical:

• Local inference has matured — quantized LLM runtimes (llama.cpp variants, Ollama, ONNX quantized engines) now run well on modern developer workstations and small servers, enabling offline prompting and reasoning.
• Compiler and simulator tooling is stable and modular — Qiskit (and peers like Cirq and PennyLane) expose API hooks and pass managers that can be orchestrated programmatically, letting an assistant suggest and test gate-level changes automatically.

Combined, these trends enable a tool that is both agentic (it performs multi-step tasks and tests) and privacy-first (no code leaves the developer's machine).

High-level architecture

Design the prototype around three components:

1. Session Listener — hooks into the developer's environment (IPython/Jupyter/VS Code) to capture code cells / file edits.
2. Local Reasoner — a rule-based engine combined with a local LLM runtime that generates suggestions and human-readable explanations.
3. Test Runner — uses Qiskit simulators (Aer or Qiskit Runtime local backends) to validate suggestions: gate counts, depth, fidelity, and unit tests.

Diagram (conceptual)

Developer IDE / Jupyter Kernel ↔ Session Listener ↔ Local Reasoner (LLM + rules) ↔ Test Runner (Qiskit Aer) ↔ Developer UI (Electron/Tauri)

Practical: Hooking into a Qiskit session

There are multiple ways to "listen" to a developer's Qiskit activity. For a quick prototype pick one or more of the following:

• IPython events — register callbacks for executed cells in Jupyter / IPython. This is low-friction for notebooks.
• File system watch — monitor .py and .ipynb files with watchdog to detect saved changes.
• Language Server / Extension — integrate with VS Code via an extension for richer context.

IPython pre-run hook (example)

from IPython import get_ipython

def on_pre_run(cell):
    # minimal: capture cell source and send to assistant
    source = cell
    process_cell(source)

ip = get_ipython()
ip.events.register('pre_run_cell', on_pre_run)

This captures each cell before execution. For production, add parsing, rate-limiting, and user consent prompts.

Parsing Qiskit programs: AST & circuit extraction

Once you have a code snippet, extract the Qiskit circuit object(s). Strategies:

• Execute the cell in a sandboxed interpreter and inspect variables (look for instances of qiskit.circuit.QuantumCircuit).
• Parse the source AST to statically find circuit constructions and composition patterns.

Example: after execution, locate circuits in the locals() of the kernel:

def find_circuits(exec_env):
    return [v for v in exec_env.values() if isinstance(v, QuantumCircuit)]

Rule-based gate-level optimization suggestions

Before invoking an LLM, implement deterministic rules that catch common low-hanging fruit. This reduces LLM calls and improves trustworthiness:

• Consecutive single-qubit rotation fusion — merge consecutive RZ/RX/RY on same qubit.
• Identity gate removal — remove gates equivalent to identity (e.g., RZ(0)).
• CX-CX cancellation — cancel adjacent CNOT pairs on same qubit pair.
• Basis gate mapping — suggest rewrites to native basis gates for target hardware (e.g., convert to u3/CX or fSim where appropriate).

Implement these as rewrites on the circuit DAG (Qiskit provides DAGCircuit utilities).

Example: merge consecutive RZ on same qubit (Qiskit)

from qiskit.converters import circuit_to_dag, dag_to_circuit

def merge_consecutive_rz(circ):
    dag = circuit_to_dag(circ)
    for node in list(dag.topological_op_nodes()):
        if node.name == 'rz':
            succ = list(dag.successors(node))
            if succ and succ[0].name == 'rz' and node.qargs == succ[0].qargs:
                angle = node.op.params[0] + succ[0].op.params[0]
                # replace pair with single rz
                dag.apply_operation_back(Instruction('rz', [angle], []), qargs=node.qargs)
                dag.remove_op_node(node)
                dag.remove_op_node(succ[0])
    return dag_to_circuit(dag)

Local LLM + prompt patterns for gate-level suggestions

Use a local, quantized model for natural-language explanations and multi-step reasoning. In 2026 prefer runtimes that support ONNX or native quantized weights (llama.cpp, Ollama, or local GPU with Triton/ONNX). Keep prompts deterministic and combine them with the deterministic rules above.

Prompt structure (concise):

1. Context: circuit metadata (qubit count, depth, gate counts, target basis).
2. Rule output: deterministic suggestions already applied or not.
3. Task: propose 3 gate-level optimizations, explain impact in expected reductions and any tradeoffs.

Privacy-first local inference

Keep all model weights and inference on-device. Use quantized models and explicit user consent for file/system access.

Example tool choices in 2026: llama.cpp (quantized GGML), Ollama, or local Triton/ONNX runtimes. For resource-constrained machines use distilled or small instruction-tuned local models.

Testing suggestions locally: unitary, noisy, and benchmark tests

Every suggestion must be validated locally. The assistant should propose changes, run deterministic tests, and show numerical evidence:

• Unitary equivalence — compare unitaries (or statevectors) up to global phase.
• Noisy benchmarks — run noisy simulator backends with a target device noise model and compare process fidelity.
• Resource metrics — gate counts, CX counts, circuit depth, critical-path latency estimate.

Example: validate with Aer statevector

from qiskit import Aer, transpile
from qiskit.quantum_info import Operator

sim = Aer.get_backend('aer_simulator_statevector')

def unitary_equal(circ1, circ2, atol=1e-8):
    op1 = Operator(circ1)
    op2 = Operator(circ2)
    # compare up to global phase
    diff = op1.data.conj().T @ op2.data
    # normalize so max element is real positive
    ph = diff[0,0] / abs(diff[0,0])
    return np.allclose(op1.data, ph * op2.data, atol=atol)

For larger circuits use fidelity estimation or randomized benchmarking style metrics rather than full unitary extraction.

Integrating Cirq and PennyLane examples

Keep the assistant multi-SDK aware. Use a thin adapter layer that converts between SDK circuit representations where possible. For example:

• Qiskit ↔ OpenQASM gym for interop with Cirq.
• PennyLane circuits can be converted to Qiskit when using qubit-based devices; focus on parameterized gate suggestions instead of proprietary templates.

Offer SDK-agnostic suggestions: fusion, parameter-shared rotations, and gate cancellations translate across frameworks.

Agentic behaviors: multi-step suggestions and testing

An agentic assistant performs chains of actions: detect, propose multiple rewrites, apply one at a time, test, and rollback if tests fail. Implement a simple transactional model:

1. Snapshot current circuit (DAG or source).
2. Apply candidate rewrite A, run unitary/noisy tests.
3. Record metrics. If metrics meet threshold, keep; else rollback and try candidate B.

Present the developer with a ranked list of changes and a reproducible test ledger (inputs, outputs, metrics).

UI: Desktop app patterns (Electron / Tauri)

Prototype a simple desktop UI to surface suggestions and diffs. Key UI elements:

• Live Activity Feed — shows captured cells / files and assistant suggestions.
• Diff Viewer — gate-level diffs and visual circuit diagrams (use Qiskit.Visualization).
• Replay / Apply Buttons — apply a suggestion back into the developer's buffer or save a patched file.
• Privacy Dashboard — status of on-device model, storage location, and permission toggles.

Tech choices: Tauri for small footprint or Electron for rapid prototyping. Back-end processes (Python) communicate via local WebSocket or gRPC.

Security & privacy checklist

• Always ask explicit user consent before recording or scanning files.
• Keep model weights and inference logs local; offer an option to delete logs.
• Restrict file access to project directories; avoid full-disk access by default.
• Open-source the critical parts or provide audits for trust.

Advanced strategies and future-proofing (2026+)

To make your assistant robust and valuable for teams and production workflows consider:

• Hybrid rules + LLM — keep deterministic optimizations primary; use LLM for explanations and edge-case reasoning.
• Model selection policy — automatically choose a smaller local LLM for quick explanations and a larger local model when deep reasoning is required (both offline).
• Continuous benchmarking — store metrics across runs to learn which rewrites consistently improve fidelity on target hardware.
• Compiler pass suggestions — map suggestions to canonical Qiskit passes so they can be integrated into CI and reproducible pipelines.
• Plugin ecosystem — expose extension points so hardware vendors can add native gate templates and noise models.

Sample end-to-end flow (concise)

1. Developer executes cell that builds a QuantumCircuit.
2. IPython hook captures cell and detects circuit objects.
3. Assistant runs rule-based fast passes (fusion, cancellation).
4. Assistant runs a local LLM to propose higher-level changes and risk notes.
5. Test Runner validates unitary equivalence and noisy fidelity with local simulator and target noise model.
6. Assistant presents diffs, metrics, and apply/rollback buttons in the UI.

Concrete code snippets: orchestration example

# simplified orchestration
import subprocess, json

def process_cell(source, exec_env):
    circuits = find_circuits(exec_env)
    suggestions = []
    for circ in circuits:
        # deterministic suggestions
        circ_opt = merge_consecutive_rz(circ)
        if not unitary_equal(circ, circ_opt):
            suggestions.append({'kind':'rz_merge','before':circ,'after':circ_opt})
        # ask local LLM for higher-level ideas (pseudocode)
        prompt = make_prompt(circ, metrics(circ), suggestions)
        llm_out = local_llm_infer(prompt)
        # parse and append
        suggestions += parse_llm_out(llm_out)
    return suggestions

Evaluation metrics & thresholds

Define actionable thresholds to avoid false positives:

• Gate count reduction — require at least 5% reduction or a minimum absolute delta (e.g., 1 CX)
• Fidelity delta — prefer only suggestions that do not reduce fidelity below a configurable threshold on the noisy model
• Depth reduction — show depth changes and estimate latency improvements

Limitations & ethical considerations

Agentic tools can act autonomously; keep these safeguards:

• Require explicit approval before writing or executing transformed code.
• Limit automated file modifications to designated project directories.
• Log suggested changes and keep an easy rollback path.

2026 trends to watch

What's shaping the field this year:

• On-device multiprocessor inference — widespread support for heterogeneous inference across CPU, Apple Neural Engines, and local GPUs.
• Stronger hardware-compiler co-design — hardware APIs will expose more native gates and calibration-aware constraints; assistants that ingest vendor calibration will produce better suggestions.
• Declarative quantum CI — expect teams to shift towards reproducible, test-driven quantum development where assistants contribute guardrails and measurable improvements.

Actionable takeaways

• Prototype quickly with IPython hooks and a rule-first engine to reduce noise and risk.
• Use local quantized LLMs for explanations — keep core transforms deterministic and testable.
• Validate every suggested rewrite with unitary or noisy simulation before applying.
• Provide a clear privacy dashboard and consent model for the assistant.
• Plan for CI integration: map suggestions to Qiskit passes for reproducible application in pipelines.

Next steps — a minimal project checklist

1. Set up a dev environment: Python (3.10+), Qiskit latest stable (2026 release), Aer simulator, and a local LLM runtime (llama.cpp/Ollama).
2. Implement an IPython listener and circuit extraction hook.
3. Build a small rule-engine for gate-level rewrites and event logging.
4. Wire a local LLM to generate explanations and candidate suggestions.
5. Create a UI with apply/rollback and privacy controls (start with Tauri/Electron).
6. Measure and iterate using representative circuits and device noise models.

Closing: Why this matters for teams

For quantum teams the path to meaningful advantage passes through solid developer tooling. A privacy-first, agentic desktop assistant bridges the gap between ad-hoc notebooks and production-ready circuits by offering deterministic, testable, and explainable gate-level improvements — without leaking IP to cloud LLMs. By 2026 this approach is feasible on developer workstations, and teams that embed such assistants into their workflows will reduce iteration time, lower gate costs on noisy devices, and accelerate prototype-to-benchmark cycles.

Call to action

Ready to try a prototype? Clone our starter repo, run the IPython listener, and test it on your Qiskit notebooks. If you want a guided workshop or a tailored integration for your team's hardware, contact the SmartQubit engineering team for a hands-on build and training session.

Related Reading

• DIY artisanal cat treats: how small-batch makers scale safely (lessons from a cocktail startup)
• Case Study: How a Fake Star Wars ‘Leak’ Could Fuel Modding Communities—and Moderation Nightmares
• Fannie & Freddie IPO Legal Roadmap: Regulatory Hurdles Small Lenders Should Watch
• Protecting Your Home Office Tech from Basement Moisture: Lessons from Mac mini M4 Deals
• Affordable Pet Tech Under $100 That Actually Helps Your Cat (Smart Lamp, Cameras, and More)