SynapseX Integration

SynapseX is Softquantus's AI platform that seamlessly integrates with QCOS to provide AI-assisted quantum computing capabilities. This guide covers how to leverage LLM + Quantum hybrid workflows.

Overview

SynapseX + QCOS enables:

Natural Language Circuit Generation - Describe circuits in plain English
AI-Powered Optimization Suggestions - Get recommendations for improving circuits
Automated Error Analysis - Understand and mitigate quantum errors
Result Interpretation - Get human-readable explanations of quantum results
Hybrid Workflows - Combine classical AI with quantum computing

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    SynapseX + QCOS Integration                  │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌──────────────────┐    ┌───────────────────────────────────┐  │
│  │   SynapseX AI    │    │           QCOS Platform           │  │
│  │  ┌────────────┐  │    │  ┌──────────┐  ┌──────────────┐  │  │
│  │  │    LLM     │◄─┼────┼─►│ Circuit  │  │   Quantum    │  │  │
│  │  │  (GPT-4o)  │  │    │  │ Optimizer│  │   Backends   │  │  │
│  │  └────────────┘  │    │  └──────────┘  └──────────────┘  │  │
│  │  ┌────────────┐  │    │  ┌──────────┐  ┌──────────────┐  │  │
│  │  │  Quantum   │  │    │  │   API    │  │     HPC      │  │  │
│  │  │  Advisor   │  │    │  │  Server  │  │  Simulator   │  │  │
│  │  └────────────┘  │    │  └──────────┘  └──────────────┘  │  │
│  └──────────────────┘    └───────────────────────────────────┘  │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Getting Started

Prerequisites

QCOS account (Pro plan or higher)
SynapseX enabled on your account
softqcos-sdk[synapsex] installed

Installation

pip install softqcos-sdk[synapsex]

Enable SynapseX

import softqcos
from softqcos.synapsex import SynapseXClient

# Initialize with SynapseX enabled
client = softqcos.QCOSClient()
synapsex = SynapseXClient(qcos_client=client)

Natural Language Circuit Generation

Generate quantum circuits from natural language descriptions.

generate_circuit()

circuit = synapsex.generate_circuit(
    description: str,
    num_qubits: int = None,
    target_backend: str = None
) -> QuantumCircuit

Examples

# Simple Bell state
circuit = synapsex.generate_circuit(
    "Create a Bell state between two qubits"
)

# GHZ state with specific size
circuit = synapsex.generate_circuit(
    "Create a 5-qubit GHZ state",
    num_qubits=5
)

# Algorithm-specific
circuit = synapsex.generate_circuit(
    "Implement Grover's algorithm to search for state |101⟩ in 3 qubits",
    target_backend="ibm_brisbane"
)

# VQE ansatz
circuit = synapsex.generate_circuit(
    "Create a hardware-efficient ansatz for VQE with 4 qubits and 2 layers"
)

With Parameters

# Parameterized circuit
circuit, params = synapsex.generate_circuit(
    "Create a variational quantum eigensolver ansatz for H2 molecule",
    return_parameters=True
)

print(f"Generated circuit with {len(params)} parameters")

Circuit Optimization Suggestions

Get AI-powered recommendations for circuit improvements.

suggest_optimizations()

suggestions = synapsex.suggest_optimizations(
    circuit: QuantumCircuit,
    target_backend: str = None,
    optimization_goal: str = "depth"  # "depth", "fidelity", "gate_count"
) -> List[Suggestion]

Example

circuit = QuantumCircuit(4, 4)
circuit.h(0)
circuit.cx(0, 1)
circuit.cx(1, 2)
circuit.cx(2, 3)
circuit.barrier()
circuit.ry(0.5, 0)
circuit.ry(0.5, 1)
circuit.measure_all()

suggestions = synapsex.suggest_optimizations(
    circuit,
    target_backend="ibm_brisbane",
    optimization_goal="fidelity"
)

for s in suggestions:
    print(f"Suggestion: {s.title}")
    print(f"Description: {s.description}")
    print(f"Expected improvement: {s.improvement_percent}%")
    print(f"Confidence: {s.confidence}")
    print("---")

Output:

Suggestion: Parallelize single-qubit gates
Description: The RY gates on qubits 0 and 1 can be executed simultaneously
Expected improvement: 15%
Confidence: high
---
Suggestion: Use native gate decomposition
Description: Replace CX chain with optimized ZZ interactions for IBM hardware
Expected improvement: 25%
Confidence: medium
---

Apply Suggestions

# Apply a specific suggestion
optimized = synapsex.apply_suggestion(circuit, suggestions[0])

# Apply all high-confidence suggestions
optimized = synapsex.apply_all_suggestions(
    circuit,
    suggestions,
    min_confidence="high"
)

Error Analysis

Analyze quantum execution errors and get mitigation strategies.

analyze_errors()

analysis = synapsex.analyze_errors(
    circuit: QuantumCircuit,
    result: Result,
    backend: str = None
) -> ErrorAnalysis

Example

# Run circuit
job = client.run(circuit, backend="ibm_brisbane", shots=10000)
result = job.result()

# Analyze errors
analysis = synapsex.analyze_errors(
    circuit,
    result,
    backend="ibm_brisbane"
)

print(f"Estimated fidelity: {analysis.estimated_fidelity}")
print(f"Dominant error: {analysis.dominant_error_type}")
print(f"Explanation: {analysis.explanation}")

for mitigation in analysis.mitigations:
    print(f"Mitigation: {mitigation.strategy}")
    print(f"Expected improvement: {mitigation.improvement}")

Output:

Estimated fidelity: 0.87
Dominant error: readout
Explanation: The circuit shows significant readout errors on qubits 2 and 3,
likely due to state leakage. The CNOT chain creates an entangled state that
is susceptible to T1 decay during the final measurement.

Mitigation: Readout error mitigation
Expected improvement: 5-8%

Mitigation: Dynamical decoupling
Expected improvement: 3-5%

Apply Error Mitigation

# Apply recommended mitigation
mitigated_result = synapsex.apply_error_mitigation(
    circuit,
    result,
    strategy="readout_correction"
)

print(f"Original counts: {result.counts}")
print(f"Mitigated counts: {mitigated_result.counts}")

Result Interpretation

Get human-readable explanations of quantum results.

interpret_results()

interpretation = synapsex.interpret_results(
    circuit: QuantumCircuit,
    result: Result,
    context: str = None
) -> Interpretation

Example

# Bell state measurement
circuit = QuantumCircuit(2, 2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure_all()

job = client.run(circuit, backend="ibm_brisbane", shots=1000)
result = job.result()

interpretation = synapsex.interpret_results(
    circuit,
    result,
    context="I'm trying to create and verify quantum entanglement"
)

print(interpretation.summary)
print("\nDetailed explanation:")
print(interpretation.detailed)

Output:

Summary: The circuit successfully created a Bell state with 95% fidelity.
The results show strong correlation between qubits.

Detailed explanation:
Your circuit creates the Bell state |Φ+⟩ = (|00⟩ + |11⟩)/√2. The measurement
results show:
- |00⟩: 48.5% (expected: 50%)
- |11⟩: 49.2% (expected: 50%)
- |01⟩: 1.2% (error)
- |10⟩: 1.1% (error)

The high correlation (97.7% in {00, 11}) confirms quantum entanglement.
The small percentage in |01⟩ and |10⟩ represents hardware noise.
This is consistent with typical IBM Quantum performance.

Hybrid Quantum-Classical Workflows

Combine SynapseX AI with QCOS quantum execution.

Example: AI-Guided VQE

from softqcos.algorithms import VQE
from softqcos.synapsex import QuantumAdvisor

# Initialize advisor
advisor = QuantumAdvisor(synapsex)

# Define problem
problem = "Find the ground state energy of H2 at bond length 0.74 Å"

# Get AI recommendations
recommendations = advisor.recommend_approach(problem)
print(f"Recommended algorithm: {recommendations.algorithm}")
print(f"Recommended backend: {recommendations.backend}")
print(f"Estimated shots needed: {recommendations.shots}")

# Generate optimized ansatz
ansatz = synapsex.generate_circuit(
    f"Create an optimal VQE ansatz for {problem}",
    target_backend=recommendations.backend
)

# Run VQE
vqe = VQE(
    ansatz=ansatz,
    optimizer="COBYLA",
    backend=recommendations.backend,
    shots=recommendations.shots
)

result = vqe.run(client)

# Interpret results
interpretation = synapsex.interpret_results(
    ansatz,
    result,
    context=problem
)

print(f"\nGround state energy: {result.eigenvalue}")
print(f"\nInterpretation: {interpretation.summary}")

Example: Iterative Circuit Design

# Start with a description
description = "Create a quantum circuit for portfolio optimization with 4 assets"

# Generate initial circuit
circuit = synapsex.generate_circuit(description)

# Iterative refinement loop
for iteration in range(3):
    # Simulate
    result = client.simulate(circuit, shots=10000)

    # Analyze
    analysis = synapsex.analyze_errors(circuit, result, "ibm_brisbane")

    # Get suggestions
    suggestions = synapsex.suggest_optimizations(
        circuit,
        target_backend="ibm_brisbane",
        optimization_goal="fidelity"
    )

    if not suggestions or analysis.estimated_fidelity > 0.95:
        break

    # Apply best suggestion
    circuit = synapsex.apply_suggestion(circuit, suggestions[0])
    print(f"Iteration {iteration + 1}: Fidelity = {analysis.estimated_fidelity}")

print("Final circuit ready for quantum execution")

Chat Interface

Interactive chat for quantum computing assistance.

Example: Jupyter Integration

from softqcos.synapsex import QuantumChat

chat = QuantumChat(synapsex)

# Interactive session
response = chat.ask("What's the best way to implement quantum phase estimation?")
print(response.text)

# With context
response = chat.ask(
    "How can I improve this circuit's fidelity?",
    context={"circuit": my_circuit, "backend": "ibm_brisbane"}
)
print(response.text)

# Generate code
response = chat.ask("Show me code for a 3-qubit Toffoli gate")
print(response.code)

API Reference

SynapseXClient

class SynapseXClient:
    def __init__(self, qcos_client: QCOSClient)

    def generate_circuit(
        self,
        description: str,
        num_qubits: int = None,
        target_backend: str = None,
        return_parameters: bool = False
    ) -> QuantumCircuit

    def suggest_optimizations(
        self,
        circuit: QuantumCircuit,
        target_backend: str = None,
        optimization_goal: str = "depth"
    ) -> List[Suggestion]

    def analyze_errors(
        self,
        circuit: QuantumCircuit,
        result: Result,
        backend: str = None
    ) -> ErrorAnalysis

    def interpret_results(
        self,
        circuit: QuantumCircuit,
        result: Result,
        context: str = None
    ) -> Interpretation

Plan Requirements

Feature	Free	Pro	Hybrid	Enterprise
Circuit Generation	❌	✅ Basic	✅ Full	✅ Full
Optimization Suggestions	❌	10/day	Unlimited	Unlimited
Error Analysis	❌	✅	✅	✅
Result Interpretation	❌	✅	✅	✅
Custom Fine-tuning	❌	❌	❌	✅

Next Steps

📖 QCOS Quickstart - Get started with QCOS
💡 Circuit Optimization - Manual optimization techniques
🔬 Backends - Choose the right quantum hardware

Overview​

Architecture​

Getting Started​

Prerequisites​

Installation​

Enable SynapseX​

Natural Language Circuit Generation​

generate_circuit()​

Examples​

With Parameters​

Circuit Optimization Suggestions​

suggest_optimizations()​

Example​

Apply Suggestions​

Error Analysis​

analyze_errors()​

Example​

Apply Error Mitigation​

Result Interpretation​

interpret_results()​

Example​

Hybrid Quantum-Classical Workflows​

Example: AI-Guided VQE​

Example: Iterative Circuit Design​

Chat Interface​

Example: Jupyter Integration​

API Reference​

SynapseXClient​

Plan Requirements​

Next Steps​

Overview

Architecture

Getting Started

Prerequisites

Installation

Enable SynapseX

Natural Language Circuit Generation

generate_circuit()

Examples

With Parameters

Circuit Optimization Suggestions

suggest_optimizations()

Example

Apply Suggestions

Error Analysis

analyze_errors()

Example

Apply Error Mitigation

Result Interpretation

interpret_results()

Example

Hybrid Quantum-Classical Workflows

Example: AI-Guided VQE

Example: Iterative Circuit Design

Chat Interface

Example: Jupyter Integration

API Reference

SynapseXClient

Plan Requirements

Next Steps