FSharp.Azure.Quantum 1.2.1

FSharp.Azure.Quantum

Quantum-First F# Library - Solve combinatorial optimization problems using quantum algorithms (QAOA) with automatic cloud/local backend selection.

✨ Status: Production Ready

Architecture: 100% Quantum-Only - Classical algorithms removed per design philosophy

Current Features:

• ✅ Multiple Backends: LocalBackend (simulation), Azure Quantum (IonQ, Rigetti)
• ✅ OpenQASM 2.0: Import/export compatibility with IBM Qiskit, Amazon Braket, Google Cirq
• ✅ QAOA Implementation: Quantum Approximate Optimization Algorithm with parameter optimization & warm-start
• ✅ 6 Quantum Optimization Builders: Graph Coloring, MaxCut, Knapsack, TSP, Portfolio, Network Flow
• ✅ VQE Implementation: Variational Quantum Eigensolver for molecular ground state energies (quantum chemistry)
• ✅ Error Mitigation: ZNE (30-50% error reduction), PEC (2-3x accuracy), REM (50-90% readout correction)
• ✅ F# Computation Expressions: Idiomatic, type-safe problem specification
• ✅ C# Interop: Fluent API extensions for C# developers
• ✅ Circuit Building: Low-level quantum circuit construction and optimization possible

---

📖 Table of Contents

1. Quick Start - Start here! Get running in 5 minutes
2. Problem Builders - High-level APIs for 6 optimization problems
3. HybridSolver - Automatic classical/quantum routing
4. Architecture - How the library is organized
5. Error Mitigation - Reduce quantum noise by 30-90%
6. C# Interop - Using from C#
7. Backend Selection - Local vs Cloud quantum execution
8. Educational Algorithms - Grover, QFT, Amplitude Amplification (for learning)

---

🚀 Quick Start

Installation

dotnet add package FSharp.Azure.Quantum

F# Computation Expressions

open FSharp.Azure.Quantum

// Graph Coloring: Register Allocation
let problem = graphColoring {
    node "R1" conflictsWith ["R2"; "R3"]
    node "R2" conflictsWith ["R1"; "R4"]
    node "R3" conflictsWith ["R1"; "R4"]
    node "R4" conflictsWith ["R2"; "R3"]
    colors ["EAX"; "EBX"; "ECX"; "EDX"]
}

// Solve using quantum optimization (QAOA)
match GraphColoring.solve problem 4 None with
| Ok solution ->
    printfn "Colors used: %d" solution.ColorsUsed
    solution.Assignments 
    |> Map.iter (fun node color -> printfn "%s → %s" node color)
| Error msg -> 
    printfn "Error: %s" msg

C# Fluent API

using FSharp.Azure.Quantum;
using static FSharp.Azure.Quantum.CSharpBuilders;

// MaxCut: Circuit Partitioning
var vertices = new[] { "A", "B", "C", "D" };
var edges = new[] {
    (source: "A", target: "B", weight: 1.0),
    (source: "B", target: "C", weight: 2.0),
    (source: "C", target: "D", weight: 1.0),
    (source: "D", target: "A", weight: 1.0)
};

var problem = MaxCutProblem(vertices, edges);
var result = MaxCut.solve(problem, null);

if (result.IsOk) {
    var solution = result.ResultValue;
    Console.WriteLine($"Cut Value: {solution.CutValue}");
    Console.WriteLine($"Partition S: {string.Join(", ", solution.PartitionS)}");
    Console.WriteLine($"Partition T: {string.Join(", ", solution.PartitionT)}");
}

What happens:

1. Computation expression builds graph coloring problem
2. GraphColoring.solve calls QuantumGraphColoringSolver internally
3. QAOA quantum algorithm encodes problem as QUBO
4. LocalBackend simulates quantum circuit (≤16 qubits)
5. Returns color assignments with validation

---

🎯 Problem Builders

1. Graph Coloring

Use Case: Register allocation, frequency assignment, exam scheduling

open FSharp.Azure.Quantum

let problem = graphColoring {
    node "Task1" conflictsWith ["Task2"; "Task3"]
    node "Task2" conflictsWith ["Task1"; "Task4"]
    node "Task3" conflictsWith ["Task1"; "Task4"]
    node "Task4" conflictsWith ["Task2"; "Task3"]
    colors ["Slot A"; "Slot B"; "Slot C"]
    objective MinimizeColors
}

match GraphColoring.solve problem 3 None with
| Ok solution ->
    printfn "Valid coloring: %b" solution.IsValid
    printfn "Colors used: %d/%d" solution.ColorsUsed 3
    printfn "Conflicts: %d" solution.ConflictCount
| Error msg -> printfn "Error: %s" msg

2. MaxCut

Use Case: Circuit design, community detection, load balancing

let vertices = ["A"; "B"; "C"; "D"]
let edges = [
    ("A", "B", 1.0)
    ("B", "C", 2.0)
    ("C", "D", 1.0)
    ("D", "A", 1.0)
]

let problem = MaxCut.createProblem vertices edges

match MaxCut.solve problem None with
| Ok solution ->
    printfn "Partition S: %A" solution.PartitionS
    printfn "Partition T: %A" solution.PartitionT
    printfn "Cut value: %.2f" solution.CutValue
| Error msg -> printfn "Error: %s" msg

3. Knapsack (0/1)

Use Case: Resource allocation, portfolio selection, cargo loading

let items = [
    ("laptop", 3.0, 1000.0)   // (id, weight, value)
    ("phone", 0.5, 500.0)
    ("tablet", 1.5, 700.0)
    ("monitor", 2.0, 600.0)
]

let problem = Knapsack.createProblem items 5.0  // capacity = 5.0

match Knapsack.solve problem None with
| Ok solution ->
    printfn "Total value: $%.2f" solution.TotalValue
    printfn "Total weight: %.2f/%.2f" solution.TotalWeight problem.Capacity
    printfn "Items: %A" (solution.SelectedItems |> List.map (fun i -> i.Id))
| Error msg -> printfn "Error: %s" msg

4. Traveling Salesperson Problem (TSP)

Use Case: Route optimization, delivery planning, logistics

let cities = [
    ("Seattle", 0.0, 0.0)
    ("Portland", 1.0, 0.5)
    ("San Francisco", 2.0, 1.5)
    ("Los Angeles", 3.0, 3.0)
]

let problem = TSP.createProblem cities

match TSP.solve problem None with
| Ok tour ->
    printfn "Optimal route: %s" (String.concat " → " tour.Cities)
    printfn "Total distance: %.2f" tour.TotalDistance
| Error msg -> printfn "Error: %s" msg

5. Portfolio Optimization

Use Case: Investment allocation, asset selection, risk management

let assets = [
    ("AAPL", 0.12, 0.15, 150.0)  // (symbol, return, risk, price)
    ("GOOGL", 0.10, 0.12, 2800.0)
    ("MSFT", 0.11, 0.14, 350.0)
]

let problem = Portfolio.createProblem assets 10000.0  // budget

match Portfolio.solve problem None with
| Ok allocation ->
    printfn "Portfolio value: $%.2f" allocation.TotalValue
    printfn "Expected return: %.2f%%" (allocation.ExpectedReturn * 100.0)
    printfn "Risk: %.2f" allocation.Risk
    
    allocation.Allocations 
    |> List.iter (fun (symbol, shares, value) ->
        printfn "  %s: %.2f shares ($%.2f)" symbol shares value)
| Error msg -> printfn "Error: %s" msg

6. Network Flow

Use Case: Supply chain optimization, logistics, distribution planning

let nodes = [
    NetworkFlow.SourceNode("Factory", 100)
    NetworkFlow.IntermediateNode("Warehouse", 80)
    NetworkFlow.SinkNode("Store1", 40)
    NetworkFlow.SinkNode("Store2", 60)
]

let routes = [
    NetworkFlow.Route("Factory", "Warehouse", 5.0)
    NetworkFlow.Route("Warehouse", "Store1", 3.0)
    NetworkFlow.Route("Warehouse", "Store2", 4.0)
]

let problem = { NetworkFlow.Nodes = nodes; Routes = routes }

match NetworkFlow.solve problem None with
| Ok flow ->
    printfn "Total cost: $%.2f" flow.TotalCost
    printfn "Fill rate: %.1f%%" (flow.FillRate * 100.0)
| Error msg -> printfn "Error: %s" msg

---

🤖 HybridSolver - Automatic Classical/Quantum Routing

Smart solver that automatically chooses between classical and quantum execution based on problem analysis.

The HybridSolver provides a unified API that:

• ✅ Analyzes problem size, structure, and complexity
• ✅ Estimates quantum advantage potential
• ✅ Routes to classical solver (fast, free) OR quantum backend (scalable, expensive)
• ✅ Provides reasoning for solver selection
• ✅ Optionally compares both methods for validation

Decision Framework:

• Small problems (< 50 variables) → Classical solver (milliseconds, $0)
• Large problems (> 100 variables) → Quantum solver (seconds-minutes, ~$10-100)
• Automatic cost guards and recommendations

Supported Problems

The HybridSolver supports all 5 main optimization problems:

open FSharp.Azure.Quantum.Classical.HybridSolver

// TSP with automatic routing
let distances = array2D [[0.0; 10.0; 15.0]; 
                          [10.0; 0.0; 20.0]; 
                          [15.0; 20.0; 0.0]]

match solveTsp distances None None None with
| Ok solution ->
    printfn "Method used: %A" solution.Method           // Classical or Quantum
    printfn "Reasoning: %s" solution.Reasoning          // Why this method?
    printfn "Time: %.2f ms" solution.ElapsedMs
    printfn "Route: %A" solution.Result.Route
    printfn "Distance: %.2f" solution.Result.TotalDistance
| Error msg -> printfn "Error: %s" msg

// MaxCut with quantum backend config
let vertices = ["A"; "B"; "C"; "D"]
let edges = [("A", "B", 1.0); ("B", "C", 2.0); ("C", "D", 1.0)]
let problem = MaxCut.createProblem vertices edges

let quantumConfig = {
    Backend = IonQ "ionq.simulator"
    WorkspaceId = "your-workspace-id"
    Location = "eastus"
    ResourceGroup = "quantum-rg"
    SubscriptionId = "sub-id"
    MaxCostUSD = Some 50.0          // Cost guard
    EnableComparison = true         // Compare with classical
}

match solveMaxCut problem (Some quantumConfig) None None with
| Ok solution ->
    printfn "Method: %A" solution.Method
    printfn "Cut Value: %.2f" solution.Result.CutValue
    match solution.Recommendation with
    | Some rec -> printfn "Advisor: %s" rec.Reasoning
    | None -> ()
| Error msg -> printfn "Error: %s" msg

// Knapsack
match solveKnapsack knapsackProblem None None None with
| Ok solution ->
    printfn "Total Value: %.2f" solution.Result.TotalValue
    printfn "Items: %A" solution.Result.SelectedItems
| Error msg -> printfn "Error: %s" msg

// Graph Coloring
match solveGraphColoring graphProblem 3 None None None with
| Ok solution ->
    printfn "Colors Used: %d/3" solution.Result.ColorsUsed
    printfn "Valid: %b" solution.Result.IsValid
| Error msg -> printfn "Error: %s" msg

// Portfolio Optimization
match solvePortfolio portfolioProblem None None None with
| Ok solution ->
    printfn "Portfolio Value: $%.2f" solution.Result.TotalValue
    printfn "Expected Return: %.2f%%" (solution.Result.ExpectedReturn * 100.0)
| Error msg -> printfn "Error: %s" msg

Features

• ✅ Unified API: Single function call for any problem size
• ✅ Smart Routing: Automatic classical/quantum decision
• ✅ Cost Guards: MaxCostUSD prevents runaway quantum costs
• ✅ Validation Mode: EnableComparison = true runs both methods
• ✅ Transparent Reasoning: Explains why each method was chosen
• ✅ Quantum Advisor: Provides recommendations on quantum readiness

When to Use HybridSolver vs Direct Builders

Use HybridSolver when:

• Problem size varies (sometimes small, sometimes large)
• You want automatic cost optimization
• You need validation/comparison between classical and quantum
• You're prototyping and unsure which approach is better

Use Direct Builders when:

• You always want quantum (for research/learning)
• Problem size is consistently in quantum range (10-16 qubits)
• You need fine-grained control over backend configuration
• You're integrating with specific QAOA parameter tuning

Location: src/FSharp.Azure.Quantum/Solvers/Hybrid/HybridSolver.fs
Status: Production-ready - Recommended for production deployments

---

🏗️ Architecture

3-Layer Quantum-Only Architecture

graph TB
    subgraph "Layer 1: High-Level Builders"
        GC["GraphColoring Builder<br/>graphColoring { }"]
        MC["MaxCut Builder<br/>MaxCut.createProblem"]
        KS["Knapsack Builder<br/>Knapsack.createProblem"]
        TS["TSP Builder<br/>TSP.createProblem"]
        PO["Portfolio Builder<br/>Portfolio.createProblem"]
        NF["NetworkFlow Builder<br/>NetworkFlow module"]
    end
    
    subgraph "Layer 2: Quantum Solvers"
        QGC["QuantumGraphColoringSolver<br/>(QAOA)"]
        QMC["QuantumMaxCutSolver<br/>(QAOA)"]
        QKS["QuantumKnapsackSolver<br/>(QAOA)"]
        QTS["QuantumTspSolver<br/>(QAOA)"]
        QPO["QuantumPortfolioSolver<br/>(QAOA)"]
        QNF["QuantumNetworkFlowSolver<br/>(QAOA)"]
    end
    
    subgraph "Layer 3: Quantum Backends"
        LOCAL["LocalBackend<br/>(≤16 qubits)"]
        IONQ["IonQBackend<br/>(Azure Quantum)"]
        RIGETTI["RigettiBackend<br/>(Azure Quantum)"]
    end
    
    GC --> QGC
    MC --> QMC
    KS --> QKS
    TS --> QTS
    PO --> QPO
    NF --> QNF
    
    QGC --> LOCAL
    QMC --> LOCAL
    QKS --> LOCAL
    QTS --> LOCAL
    QPO --> LOCAL
    QNF --> LOCAL
    
    QGC -.-> IONQ
    QMC -.-> IONQ
    QKS -.-> IONQ
    QTS -.-> IONQ
    QPO -.-> IONQ
    QNF -.-> IONQ
    
    QGC -.-> RIGETTI
    QMC -.-> RIGETTI
    QKS -.-> RIGETTI
    QTS -.-> RIGETTI
    QPO -.-> RIGETTI
    QNF -.-> RIGETTI
    
    style GC fill:#90EE90
    style MC fill:#90EE90
    style KS fill:#90EE90
    style TS fill:#90EE90
    style PO fill:#90EE90
    style NF fill:#90EE90
    style QGC fill:#FFA500
    style QMC fill:#FFA500
    style QKS fill:#FFA500
    style QTS fill:#FFA500
    style QPO fill:#FFA500
    style QNF fill:#FFA500
    style LOCAL fill:#4169E1
    style IONQ fill:#4169E1
    style RIGETTI fill:#4169E1

Layer Responsibilities

Layer 1: High-Level Builders 🟢

Who uses it: End users (F# and C# developers)
Purpose: Business domain APIs with problem-specific validation

Features:

• ✅ F# computation expressions (graphColoring { })
• ✅ C# fluent APIs (CSharpBuilders.MaxCutProblem())
• ✅ Type-safe problem specification
• ✅ Domain-specific validation
• ✅ Automatic backend creation (defaults to LocalBackend)

Example:

// F# computation expression
let problem = graphColoring {
    node "R1" conflictsWith ["R2"]
    colors ["Red"; "Blue"]
}

// Delegates to Layer 2
GraphColoring.solve problem 2 None

Layer 2: Quantum Solvers 🟠

Who uses it: High-level builders (internal delegation)
Purpose: QAOA implementations for specific problem types

Features:

• ✅ Problem → QUBO encoding
• ✅ QAOA circuit construction
• ✅ Variational parameter optimization (Nelder-Mead)
• ✅ Solution decoding and validation
• ✅ Backend-agnostic (accepts IQuantumBackend)

Example:

// Called internally by GraphColoring.solve
QuantumGraphColoringSolver.solve 
    backend          // IQuantumBackend
    problem          // GraphColoringProblem
    quantumConfig    // QAOA parameters

Layer 3: Quantum Backends 🔵

Who uses it: Quantum solvers
Purpose: Quantum circuit execution

Backend Types:

Backend	Qubits	Speed	Cost	Use Case
LocalBackend	≤16	Fast (ms)	Free	Development, testing, small problems
IonQBackend	29+ (sim), 11 (QPU)	Moderate (seconds)	Paid	Production, large problems
RigettiBackend	40+ (sim), 80 (QPU)	Moderate (seconds)	Paid	Production, large problems

Example:

// Local simulation (default)
let backend = BackendAbstraction.createLocalBackend()

// Azure Quantum (cloud)
let backend = BackendAbstraction.createIonQBackend(
    connectionString,
    "ionq.simulator"
)

LocalBackend Internal Architecture

How LocalBackend simulates quantum circuits:

graph TB
    subgraph "LocalBackend (≤16 qubits)"
        INIT["StateVector.init<br/>|0⟩⊗n"]
        
        subgraph "Gate Operations"
            H["Hadamard (H)<br/>Superposition"]
            CNOT["CNOT<br/>Entanglement"]
            RX["RX(θ)<br/>X-axis rotation"]
            RY["RY(θ)<br/>Y-axis rotation"]
            RZ["RZ(θ)<br/>Z-axis rotation"]
        end
        
        subgraph "State Evolution"
            STATE["Complex State Vector<br/>2^n amplitudes"]
            MATRIX["Matrix Multiplication<br/>Gate × State"]
        end
        
        subgraph "Measurement"
            PROB["Compute Probabilities<br/>|amplitude|²"]
            SAMPLE["Sample Bitstrings<br/>(shots times)"]
            COUNT["Aggregate Counts<br/>{bitstring → frequency}"]
        end
        
        INIT --> H
        H --> STATE
        CNOT --> STATE
        RX --> STATE
        RY --> STATE
        RZ --> STATE
        
        STATE --> MATRIX
        MATRIX --> STATE
        
        STATE --> PROB
        PROB --> SAMPLE
        SAMPLE --> COUNT
    end
    
    subgraph "QAOA Circuit Example"
        Q0["Qubit 0: |0⟩"]
        Q1["Qubit 1: |0⟩"]
        
        H0["H"]
        H1["H"]
        
        COST["Cost Layer<br/>RZ(γ)"]
        MIX["Mixer Layer<br/>RX(β)"]
        
        MEAS["Measure<br/>→ '01'"]
        
        Q0 --> H0
        Q1 --> H1
        H0 --> COST
        H1 --> COST
        COST --> MIX
        MIX --> MEAS
    end
    
    COUNT --> MEAS
    
    style INIT fill:#90EE90
    style H fill:#FFD700
    style CNOT fill:#FFD700
    style RX fill:#FFD700
    style RY fill:#FFD700
    style RZ fill:#FFD700
    style STATE fill:#87CEEB
    style MATRIX fill:#87CEEB
    style PROB fill:#FFA07A
    style SAMPLE fill:#FFA07A
    style COUNT fill:#FFA07A
    style Q0 fill:#E6E6FA
    style Q1 fill:#E6E6FA
    style MEAS fill:#98FB98

Key Components:

1. StateVector Module 🟢

   • Stores quantum state as complex amplitude array
   • Size: 2^n complex numbers (n = number of qubits)
   • Example: 3 qubits = 8 amplitudes

2. Gate Module 🟡

   • Matrix representations of quantum gates
   • Applied via tensor products and matrix multiplication
   • Gates: H, CNOT, RX, RY, RZ, SWAP, CZ, etc.

3. Measurement Module 🟠

   • Computes probabilities from amplitudes: P(x) = |amplitude(x)|²
   • Samples bitstrings according to probability distribution
   • Returns histogram: {bitstring → count}

4. QAOA Integration 🟣

   • Cost layer: Problem-specific rotations (RZ gates)
   • Mixer layer: Standard X-rotations (RX gates)
   • Repeat for multiple QAOA layers (p-layers)

Performance:

• 1-6 qubits: Instant (< 10ms)
• 7-10 qubits: Fast (< 100ms)
• 11-14 qubits: Moderate (< 1s)
• 15-16 qubits: Slow (< 10s)
• 17+ qubits: ❌ Exceeds limit (exponential memory: 2^n)

---

💻 C# Interop

C# Fluent API

All problem builders have C#-friendly extensions:

using FSharp.Azure.Quantum;
using static FSharp.Azure.Quantum.CSharpBuilders;

// MaxCut
var vertices = new[] { "A", "B", "C", "D" };
var edges = new[] {
    (source: "A", target: "B", weight: 1.0),
    (source: "B", target: "C", weight: 2.0)
};
var problem = MaxCutProblem(vertices, edges);
var result = MaxCut.solve(problem, null);

// Knapsack
var items = new[] {
    (id: "laptop", weight: 3.0, value: 1000.0),
    (id: "phone", weight: 0.5, value: 500.0)
};
var problem = KnapsackProblem(items, capacity: 5.0);
var result = Knapsack.solve(problem, null);

// TSP
var cities = new[] {
    (name: "Seattle", x: 0.0, y: 0.0),
    (name: "Portland", x: 1.0, y: 0.5)
};
var problem = TspProblem(cities);
var result = TSP.solve(problem, null);

// Portfolio
var assets = new[] {
    (symbol: "AAPL", expectedReturn: 0.12, risk: 0.15, price: 150.0),
    (symbol: "MSFT", expectedReturn: 0.10, risk: 0.12, price: 300.0)
};
var problem = PortfolioProblem(assets, budget: 10000.0);
var result = Portfolio.solve(problem, null);

See: C# Usage Guide for complete examples

---

🔌 Backend Selection

Automatic Local Simulation (Default)

// No backend parameter = automatic LocalBackend creation
match GraphColoring.solve problem 3 None with
| Ok solution -> (* ... *)

What happens:

1. Builder creates LocalBackend() automatically
2. Simulates quantum circuit using state vectors
3. ≤16 qubits supported (larger problems fail with error)

Explicit Cloud Backend

// Create Azure Quantum backend
let backend = BackendAbstraction.createIonQBackend(
    connectionString = "YOUR_CONNECTION_STRING",
    targetId = "ionq.simulator"  // or "ionq.qpu" for hardware
)

// Pass to solver
match GraphColoring.solve problem 3 (Some backend) with
| Ok solution -> 
    printfn "Backend used: %s" solution.BackendName

Backend Comparison

// Small problem: Use local simulation
let smallProblem = MaxCut.createProblem ["A"; "B"; "C"] [("A","B",1.0)]
let result1 = MaxCut.solve smallProblem None  // Fast, free

// Large problem: Use cloud backend
let largeProblem = MaxCut.createProblem 
    [for i in 1..20 -> sprintf "V%d" i]
    [for i in 1..19 -> (sprintf "V%d" i, sprintf "V%d" (i+1), 1.0)]

let backend = BackendAbstraction.createIonQBackend(conn, "ionq.simulator")
let result2 = MaxCut.solve largeProblem (Some backend)  // Scalable, paid

---

🔄 OpenQASM 2.0 Support

Import and export quantum circuits to IBM Qiskit, Cirq, and other OpenQASM-compatible platforms.

Why OpenQASM?

OpenQASM (Open Quantum Assembly Language) is the industry-standard text format for quantum circuits:

• ✅ IBM Qiskit - Primary format (6.7k GitHub stars)
• ✅ Amazon Braket - Native support
• ✅ Google Cirq - Import/export compatibility
• ✅ Interoperability - Share circuits between platforms

Export Circuits to OpenQASM

F# API:

open FSharp.Azure.Quantum
open FSharp.Azure.Quantum.CircuitBuilder

// Build circuit using F# circuit builder
let circuit = 
    CircuitBuilder.empty 2
    |> CircuitBuilder.addGate (H 0)
    |> CircuitBuilder.addGate (CNOT (0, 1))
    |> CircuitBuilder.addGate (RZ (0, System.Math.PI / 4.0))

// Export to OpenQASM 2.0 string
let qasmCode = OpenQasm.export circuit
printfn "%s" qasmCode

// Export to .qasm file
OpenQasm.exportToFile circuit "bell_state.qasm"

Output (bell_state.qasm):

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
h q[0];
cx q[0],q[1];
rz(0.7853981634) q[0];

Import Circuits from OpenQASM

F# API:

open FSharp.Azure.Quantum
open System.IO

// Parse OpenQASM string
let qasmCode = """
OPENQASM 2.0;
include "qelib1.inc";
qreg q[3];
h q[0];
cx q[0],q[1];
cx q[1],q[2];
"""

match OpenQasmImport.parse qasmCode with
| Ok circuit ->
    printfn "Loaded %d-qubit circuit with %d gates" 
        circuit.QubitCount circuit.Gates.Length
    // Use circuit with LocalBackend or export to another format
| Error msg -> 
    printfn "Parse error: %s" msg

// Import from file
match OpenQasmImport.parseFromFile "grover.qasm" with
| Ok circuit -> (* use circuit *)
| Error msg -> printfn "Error: %s" msg

C# API

using FSharp.Azure.Quantum;
using FSharp.Azure.Quantum.CircuitBuilder;

// Export circuit to OpenQASM
var circuit = CircuitBuilder.empty(2)
    .AddGate(Gate.NewH(0))
    .AddGate(Gate.NewCNOT(0, 1));

var qasmCode = OpenQasm.export(circuit);
File.WriteAllText("circuit.qasm", qasmCode);

// Import from OpenQASM
var qasmInput = File.ReadAllText("qiskit_circuit.qasm");
var result = OpenQasmImport.parse(qasmInput);

if (result.IsOk) {
    var imported = result.ResultValue;
    Console.WriteLine($"Loaded {imported.QubitCount}-qubit circuit");
}

Supported Gates

All standard OpenQASM 2.0 gates supported:

Category	Gates
Pauli	X, Y, Z, H
Phase	S, S†, T, T†
Rotation	RX(θ), RY(θ), RZ(θ)
Two-qubit	CNOT (CX), CZ, SWAP
Three-qubit	CCX (Toffoli)

Workflow: Qiskit → F# → IonQ

Full interoperability workflow:

// 1. Load circuit from Qiskit
let qiskitCircuit = OpenQasmImport.parseFromFile "qiskit_algorithm.qasm"

match qiskitCircuit with
| Ok circuit ->
    // 2. Run on LocalBackend for testing
    let localBackend = BackendAbstraction.createLocalBackend()
    let testResult = LocalSimulator.QaoaSimulator.simulate circuit 1000
    
    printfn "Local test: %d samples" testResult.Shots
    
    // 3. Transpile for IonQ hardware
    let transpiled = GateTranspiler.transpileForBackend "ionq.qpu" circuit
    
    // 4. Execute on IonQ
    let ionqBackend = BackendAbstraction.createIonQBackend(
        connectionString,
        "ionq.qpu"
    )
    
    // 5. Export results back to Qiskit format
    OpenQasm.exportToFile transpiled "results_ionq.qasm"
| Error msg -> 
    printfn "Import failed: %s" msg

Round-Trip Compatibility

Circuits are preserved through export/import:

// Original circuit
let original = { QubitCount = 3; Gates = [H 0; CNOT (0, 1); RZ (1, 1.5708)] }

// Export → Import → Compare
let qasm = OpenQasm.export original
let imported = OpenQasmImport.parse qasm

match imported with
| Ok circuit ->
    assert (circuit.QubitCount = original.QubitCount)
    assert (circuit.Gates.Length = original.Gates.Length)
    printfn "✅ Round-trip successful"
| Error msg -> 
    printfn "❌ Round-trip failed: %s" msg

Use Cases

1. Share algorithms - Export F# quantum algorithms to IBM Qiskit community
2. Import research - Load published Qiskit papers/benchmarks into F# for analysis
3. Multi-provider - Develop in F#, run on IBM Quantum, Amazon Braket, IonQ
4. Education - Students learn quantum with type-safe F#, export to standard format
5. Validation - Cross-check results between F# LocalBackend and IBM simulators

See: tests/OpenQasmIntegrationTests.fs for comprehensive examples

---

🛡️ Error Mitigation

Reduce quantum noise and improve result accuracy by 30-90% with production-ready error mitigation techniques.

Why Error Mitigation?

Quantum computers are noisy (NISQ era). Error mitigation improves results without requiring error-corrected qubits:

• Gate errors - Imperfect quantum gates introduce noise (~0.1-1% per gate)
• Decoherence - Qubits lose quantum information over time
• Readout errors - Measurement outcomes have ~1-5% error rate

Error mitigation achieves near-ideal results on noisy hardware - critical for real-world quantum advantage.

---

Available Techniques

1️⃣ Zero-Noise Extrapolation (ZNE)

Richardson extrapolation to estimate error-free result.

How it works:

1. Run circuit at different noise levels (1.0x, 1.5x, 2.0x)
2. Fit polynomial to noise vs. result
3. Extrapolate to zero noise (λ=0)

Performance:

• ✅ 30-50% error reduction
• ✅ 3x cost overhead (3 noise scaling levels)
• ✅ Works on any backend (IonQ, Rigetti, Local)

F# Example:

open FSharp.Azure.Quantum.ErrorMitigation

// Configure ZNE
let zneConfig = {
    NoiseScalings = [
        ZeroNoiseExtrapolation.IdentityInsertion 0.0    // baseline (1.0x)
        ZeroNoiseExtrapolation.IdentityInsertion 0.5    // 1.5x noise
        ZeroNoiseExtrapolation.IdentityInsertion 1.0    // 2.0x noise
    ]
    PolynomialDegree = 2
    MinSamples = 1000
}

// Apply ZNE to circuit expectation value
let circuit = // ... your QAOA circuit
let observable = // ... Pauli operator to measure

async {
    let! result = ZeroNoiseExtrapolation.mitigate circuit observable zneConfig backend
    
    match result with
    | Ok zneResult ->
        printfn "Zero-noise value: %f" zneResult.ZeroNoiseValue
        printfn "R² goodness of fit: %f" zneResult.GoodnessOfFit
        printfn "Measured values:"
        zneResult.MeasuredValues 
        |> List.iter (fun (noise, value) -> printfn "  λ=%.1f: %f" noise value)
    | Error msg -> 
        printfn "ZNE failed: %s" msg
}

When to use:

• Medium-depth circuits (20-50 gates)
• Cost-constrained (3x affordable)
• Need 30-50% error reduction

---

2️⃣ Probabilistic Error Cancellation (PEC)

Quasi-probability decomposition with importance sampling.

How it works:

1. Decompose noisy gates into sum of ideal gates with quasi-probabilities (some negative!)
2. Sample circuits from quasi-probability distribution
3. Reweight samples to cancel noise

Performance:

• ✅ 2-3x accuracy improvement
• ⚠️ 10-100x cost overhead (Monte Carlo sampling)
• ✅ Powerful for high-accuracy requirements

F# Example:

open FSharp.Azure.Quantum.ErrorMitigation

// Configure PEC with noise model
let pecConfig = {
    NoiseModel = {
        SingleQubitDepolarizing = 0.001  // 0.1% per single-qubit gate
        TwoQubitDepolarizing = 0.01      // 1% per two-qubit gate
        ReadoutError = 0.02              // 2% readout error
    }
    Samples = 1000
    Seed = Some 42
}

// Apply PEC to circuit
async {
    let! result = ProbabilisticErrorCancellation.mitigate circuit observable pecConfig backend
    
    match result with
    | Ok pecResult ->
        printfn "Corrected expectation: %f" pecResult.CorrectedExpectation
        printfn "Uncorrected (noisy): %f" pecResult.UncorrectedExpectation
        printfn "Error reduction: %.1f%%" (pecResult.ErrorReduction * 100.0)
        printfn "Overhead: %.1fx" pecResult.Overhead
    | Error msg -> 
        printfn "PEC failed: %s" msg
}

When to use:

• High-accuracy requirements (research, benchmarking)
• Budget available for 10-100x overhead
• Need 2-3x accuracy improvement

---

3️⃣ Readout Error Mitigation (REM)

Confusion matrix calibration with matrix inversion.

How it works:

1. Calibration phase - Prepare all basis states (|00⟩, |01⟩, |10⟩, |11⟩), measure confusion matrix
2. Correction phase - Invert matrix, multiply by measured histogram
3. Result - Corrected histogram with confidence intervals

Performance:

• ✅ 50-90% readout error reduction
• ✅ ~0x runtime overhead (one-time calibration, then free!)
• ✅ Works on all backends

F# Example:

open FSharp.Azure.Quantum.ErrorMitigation

// Step 1: Calibrate (one-time cost per backend)
let remConfig = 
    ReadoutErrorMitigation.defaultConfig
    |> ReadoutErrorMitigation.withCalibrationShots 10000
    |> ReadoutErrorMitigation.withConfidenceLevel 0.95

async {
    // Calibrate confusion matrix (run once, cache result)
    let! calibrationResult = ReadoutErrorMitigation.calibrate remConfig backend
    
    match calibrationResult with
    | Ok calibMatrix ->
        printfn "Calibration complete:"
        printfn "  Qubits: %d" calibMatrix.Qubits
        printfn "  Shots: %d" calibMatrix.CalibrationShots
        printfn "  Backend: %s" calibMatrix.Backend
        
        // Step 2: Correct measurement histogram (zero overhead!)
        let noisyHistogram = // ... your measurement results
        let correctedResult = ReadoutErrorMitigation.correctHistogram noisyHistogram calibMatrix remConfig
        
        match correctedResult with
        | Ok corrected ->
            printfn "\nCorrected histogram:"
            corrected.Histogram 
            |> Map.iter (fun state prob -> printfn "  |%s⟩: %.4f" state prob)
            
            printfn "\nConfidence intervals (95%%):"
            corrected.ConfidenceIntervals
            |> Map.iter (fun state (lower, upper) -> 
                printfn "  |%s⟩: [%.4f, %.4f]" state lower upper)
        | Error msg -> 
            printfn "Correction failed: %s" msg
    | Error msg -> 
        printfn "Calibration failed: %s" msg
}

When to use:

• Shallow circuits (readout errors dominate)
• Cost-constrained (free after calibration)
• All quantum applications (always beneficial)

---

4️⃣ Automatic Strategy Selection

Let the library choose the best technique for your circuit.

F# Example:

open FSharp.Azure.Quantum.ErrorMitigation

// Define selection criteria
let criteria = {
    CircuitDepth = 25
    QubitCount = 6
    Backend = Types.Backend.IonQBackend
    MaxCostUSD = Some 10.0
    RequiredAccuracy = Some 0.95
}

// Get recommended strategy
let recommendation = ErrorMitigationStrategy.selectStrategy criteria

printfn "Recommended: %s" (
    match recommendation.Primary with
    | ZeroNoiseExtrapolation _ -> "Zero-Noise Extrapolation (ZNE)"
    | ProbabilisticErrorCancellation _ -> "Probabilistic Error Cancellation (PEC)"
    | ReadoutErrorMitigation _ -> "Readout Error Mitigation (REM)"
    | Combined _ -> "Combined Techniques"
)
printfn "Reasoning: %s" recommendation.Reasoning
printfn "Estimated cost multiplier: %.1fx" recommendation.EstimatedCostMultiplier
printfn "Estimated accuracy: %.1f%%" (recommendation.EstimatedAccuracy * 100.0)

// Apply recommended strategy
let noisyHistogram = // ... your measurement results
let mitigatedResult = ErrorMitigationStrategy.applyStrategy noisyHistogram recommendation.Primary

match mitigatedResult with
| Ok result ->
    printfn "\nMitigation successful:"
    printfn "  Technique: %A" result.AppliedTechnique
    printfn "  Used fallback: %b" result.UsedFallback
    printfn "  Actual cost: %.1fx" result.ActualCostMultiplier
    result.Histogram |> Map.iter (fun k v -> printfn "    %s: %f" k v)
| Error msg ->
    printfn "Mitigation failed: %s" msg

Strategy selection logic:

• Shallow (depth < 20): Readout errors dominate → REM
• Medium (20-50): Gate errors significant → ZNE
• Deep (> 50): High gate errors → PEC or Combined (ZNE + REM)
• High accuracy: PEC (if budget allows)
• Cost-constrained: REM (free) or ZNE (3x)

---

Decision Matrix

Circuit Type	Best Technique	Error Reduction	Cost	Why
Shallow (< 20 gates)	REM	50-90%	~0x	Readout dominates
Medium (20-50 gates)	ZNE	30-50%	3x	Balanced gate/readout
Deep (> 50 gates)	PEC or ZNE+REM	40-70%	10-100x	High gate errors
Cost-constrained	REM	50-90%	~0x	Free after calibration
High accuracy	PEC	2-3x	10-100x	Research/benchmarking

---

Real-World Example: MaxCut with ZNE

open FSharp.Azure.Quantum
open FSharp.Azure.Quantum.ErrorMitigation

// Define MaxCut problem
let vertices = ["A"; "B"; "C"; "D"]
let edges = [
    ("A", "B", 1.0)
    ("B", "C", 2.0)
    ("C", "D", 1.0)
    ("D", "A", 1.0)
]

let problem = MaxCut.problem vertices edges

// Solve with ZNE error mitigation
let zneConfig = {
    NoiseScalings = [
        ZeroNoiseExtrapolation.IdentityInsertion 0.0
        ZeroNoiseExtrapolation.IdentityInsertion 0.5
        ZeroNoiseExtrapolation.IdentityInsertion 1.0
    ]
    PolynomialDegree = 2
    MinSamples = 1000
}

async {
    // Standard solve (noisy)
    let! noisyResult = MaxCut.solve problem None
    
    // Solve with ZNE (error-mitigated)
    let! mitigatedResult = MaxCut.solveWithErrorMitigation problem (Some zneConfig) None
    
    match noisyResult, mitigatedResult with
    | Ok noisy, Ok mitigated ->
        printfn "Noisy cut value: %.2f" noisy.CutValue
        printfn "ZNE-mitigated cut value: %.2f" mitigated.CutValue
        printfn "Improvement: %.1f%%" ((mitigated.CutValue - noisy.CutValue) / noisy.CutValue * 100.0)
    | _ -> 
        printfn "Error occurred"
}

Expected improvement: 30-50% better cut value on noisy hardware.

---

Testing & Validation

Error mitigation is production-ready with comprehensive testing:

• ✅ 534 test cases across all modules
• ✅ ZNE: 111 tests (Richardson extrapolation, noise scaling, goodness-of-fit)
• ✅ PEC: 222 tests (quasi-probability, Monte Carlo, integration)
• ✅ REM: 161 tests (calibration, matrix inversion, confidence intervals)
• ✅ Strategy: 40 tests (selection logic, cost estimation, fallbacks)

See: tests/FSharp.Azure.Quantum.Tests/*ErrorMitigation*.fs

---

Further Reading

• ZNE Paper: Digital zero-noise extrapolation for quantum error mitigation (arXiv:2005.10921)
• PEC Paper: Probabilistic error cancellation with sparse Pauli-Lindblad models (arXiv:2201.09866)
• REM Paper: Practical characterization of quantum devices without tomography (arXiv:2004.11281)
• Tutorial: Mitiq - Quantum Error Mitigation

---

🧪 QAOA Algorithm Internals

How Quantum Optimization Works

QAOA (Quantum Approximate Optimization Algorithm):

1. QUBO Encoding: Convert problem → Quadratic Unconstrained Binary Optimization

   Graph Coloring → Binary variables for node-color assignments
   MaxCut → Binary variables for partition membership
   

2. Circuit Construction: Build parameterized quantum circuit

   |0⟩^n → H^⊗n → [Cost Layer (γ)] → [Mixer Layer (β)] → Measure
   

3. Parameter Optimization: Find optimal (γ, β) using Nelder-Mead

   for iteration in 1..maxIterations do
       let cost = evaluateCost(gamma, beta)
       optimizer.Update(cost)
   

4. Solution Extraction: Decode measurement results → problem solution

   Bitstring "0101" → [R1→Red, R2→Blue, R3→Red, R4→Blue]
   

QAOA Configuration

// Custom QAOA parameters
let quantumConfig : QuantumGraphColoringSolver.QuantumGraphColoringConfig = {
    OptimizationShots = 100        // Shots per optimization step
    FinalShots = 1000              // Shots for final measurement
    EnableOptimization = true      // Enable parameter optimization
    InitialParameters = (0.5, 0.5) // Starting (gamma, beta)
}

// Use custom config
let backend = BackendAbstraction.createLocalBackend()
match QuantumGraphColoringSolver.solve backend problem quantumConfig with
| Ok result -> (* ... *)

---

📚 Documentation

• Getting Started Guide - Installation and first examples
• C# Usage Guide - Complete C# interop guide
• API Reference - Complete API documentation
• Architecture Overview - Deep dive into library design
• Backend Switching Guide - Local vs Cloud backends
• FAQ - Common questions and troubleshooting

---

📊 Problem Size Guidelines

Problem Type	Small (LocalBackend)	Medium	Large (Cloud Required)
Graph Coloring	≤16 nodes	16-25 nodes	25+ nodes
MaxCut	≤16 vertices	16-25 vertices	25+ vertices
Knapsack	≤16 items	16-25 items	25+ items
TSP	≤7 cities	7-10 cities	10+ cities
Portfolio	≤16 assets	16-25 assets	25+ assets
Network Flow	≤12 nodes	12-20 nodes	20+ nodes

Note: LocalBackend limited to 16 qubits. Larger problems require Azure Quantum backends.

---

🎯 Design Philosophy

Rule 1: Quantum-Only Library

FSharp.Azure.Quantum is a quantum-first library - NO classical algorithms.

Why?

• ✅ Clear identity: Purpose-built for quantum optimization
• ✅ No architectural confusion: Pure quantum algorithm library
• ✅ Complements classical libraries: Use together with classical solvers when needed
• ✅ Educational value: Learn quantum algorithms without classical fallbacks

What this means:

// ✅ QUANTUM: QAOA-based optimization
GraphColoring.solve problem 3 None

// ❌ NO CLASSICAL FALLBACK: If quantum fails, returns Error
// Users should use dedicated classical libraries for that use case

Clean API Layers

1. High-Level Builders: Business domain APIs (register allocation, portfolio optimization)
2. Quantum Solvers: QAOA implementations (algorithm experts)
3. Quantum Backends: Circuit execution (hardware abstraction)

No leaky abstractions - Each layer has clear responsibilities.

---

📚 Educational Algorithms

Note: The following algorithms are provided for quantum computing education and research. They are NOT optimization solvers and should not be used for production optimization tasks. For production optimization, use the Problem Builders or HybridSolver above.

---

In addition to the production-ready optimization solvers above, the library includes foundational quantum algorithms for education and research:

Grover's Search Algorithm

Quantum search algorithm for finding elements in unsorted databases.

open FSharp.Azure.Quantum.GroverSearch

// Search for item satisfying predicate
let searchConfig = {
    MaxIterations = Some 10
    SuccessThreshold = 0.9
    OptimizeIterations = true
    Shots = 1000
    RandomSeed = Some 42
}

// Search 8-item space for items where f(x) = true
let predicate x = x = 3 || x = 5  // Looking for indices 3 or 5

match Search.searchWithPredicate 3 predicate searchConfig with
| Ok result ->
    printfn "Found solutions: %A" result.Solutions
    printfn "Success probability: %.2f%%" (result.SuccessProbability * 100.0)
    printfn "Iterations: %d" result.IterationsApplied
| Error msg -> 
    printfn "Search failed: %s" msg

Features:

• ✅ Automatic optimal iteration calculation
• ✅ Amplitude amplification for multiple solutions
• ✅ Direct LocalSimulator integration (no IBackend)
• ✅ Educational/research tool (not production optimizer)

Location: src/FSharp.Azure.Quantum/Algorithms/
Status: Experimental - Research and education purposes

Note: Grover's algorithm is a standalone quantum search primitive, separate from the QAOA-based optimization builders. It does not use the IBackend abstraction and is optimized for specific search problems rather than general combinatorial optimization.

---

Amplitude Amplification

Generalization of Grover's algorithm for custom initial states.

Amplitude amplification extends Grover's algorithm to work with arbitrary initial state preparations (not just uniform superposition). This enables quantum speedups for problems beyond simple database search.

Key Insight: Grover's algorithm is a special case where:

• Initial state = uniform superposition H^⊗n|0⟩
• Reflection operator = Grover diffusion operator

Amplitude amplification allows:

• Custom initial state preparation A|0⟩
• Reflection about A|0⟩ (automatically generated)

Local Simulation Example:

open FSharp.Azure.Quantum.GroverSearch.AmplitudeAmplification

// Custom state preparation (W-state: equal superposition of single-excitation states)
let wState = wStatePreparation 3  // (|100⟩ + |010⟩ + |001⟩)/√3

// Configure amplitude amplification
let config = {
    NumQubits = 3
    StatePreparation = wState
    Oracle = myOracle
    ReflectionOperator = None  // Auto-generate from state preparation
    Iterations = 5
}

// Execute amplitude amplification
match execute config with
| Ok result ->
    printfn "Success probability: %.2f%%" (result.SuccessProbability * 100.0)
    printfn "Iterations applied: %d" result.IterationsApplied
| Error msg ->
    printfn "Amplification failed: %s" msg

Backend Execution (NEW - Cloud/Hardware Support):

open FSharp.Azure.Quantum.GroverSearch.AmplitudeAmplificationAdapter
open FSharp.Azure.Quantum.Core.BackendAbstraction

// Execute amplitude amplification on quantum hardware backend
let backend = createIonQBackend(connectionString, "ionq.simulator")

// Define oracle (marks solution states)
let oracle = StateOracle(fun state -> state = 3 || state = 5)

// Configure amplitude amplification
let config = {
    NumQubits = 3
    StatePreparation = UniformSuperposition  // or BasisState(n), PartialSuperposition(states)
    Oracle = oracle
    Iterations = 2  // Optimal iterations for 2 solutions in 8-element space
}

match executeWithBackend config backend 1000 with
| Ok measurementCounts ->
    printfn "Amplitude amplification executed on quantum hardware"
    measurementCounts 
    |> Map.iter (fun state count -> printfn "  |%d⟩: %d counts" state count)
| Error msg ->
    printfn "Backend execution failed: %s" msg

// Convenience functions
executeUniformAmplification 3 oracle 2 backend 1000      // Uniform initial state
executeBasisStateAmplification 3 5 oracle 2 backend 1000 // Start from |5⟩

Features:

• ✅ Custom state preparation (W-states, partial superpositions, arbitrary states)
• ✅ Cloud backend execution (IonQ, Rigetti, LocalBackend)
• ✅ Automatic reflection operator generation (circuit-based A†)
• ✅ Grover equivalence verification (shows Grover as special case)
• ✅ Optimal iteration calculation for arbitrary initial success probability
• ✅ Measurement-based results (histogram of basis states)

Backend Limitations:

• ⚠️ Backend execution returns measurement statistics only (not full amplitudes)
• ⚠️ Quantum phases are lost during measurement (fundamental limitation)
• ✅ Suitable for algorithms that measure amplification results
• ✅ For amplitude/phase analysis, use local simulation

Use Cases:

• Quantum walk algorithms with non-uniform initial distributions
• Fixed-point search (where initial state biases toward solutions)
• Quantum sampling with amplification
• Fixed-amplitude search (building block for quantum counting)

Location:

• Local simulation: Algorithms/AmplitudeAmplification.fs
• Backend adapter: Algorithms/AmplitudeAmplificationAdapter.fs

Status: Production-ready - Full backend support (as of Nov 2025)

---

Quantum Fourier Transform (QFT)

Quantum analog of the discrete Fourier transform - foundational building block for many quantum algorithms.

The QFT transforms computational basis states into frequency basis with exponential speedup over classical FFT:

• Classical FFT: O(n·2^n) operations
• Quantum QFT: O(n²) quantum gates

Mathematical Transform:

QFT: |j⟩ → (1/√N) Σₖ e^(2πijk/N) |k⟩

Local Simulation Example:

open FSharp.Azure.Quantum.GroverSearch.QuantumFourierTransform

// Initialize state |5⟩ (computational basis)
let state = StateVector.init 3  // 3 qubits
// ... prepare |5⟩ = |101⟩

// Apply QFT (local simulation)
match execute config state with
| Ok result ->
    printfn "QFT applied successfully"
    printfn "Gate count: %d" result.GateCount
    printfn "Final state transformed to frequency basis"
| Error msg ->
    printfn "QFT failed: %s" msg

Backend Execution (NEW - Cloud/Hardware Support):

open FSharp.Azure.Quantum.GroverSearch.QFTBackendAdapter
open FSharp.Azure.Quantum.Core.BackendAbstraction

// Execute QFT on quantum hardware backend
let backend = createIonQBackend(connectionString, "ionq.simulator")
let config = { NumQubits = 5; ApplySwaps = true; Inverse = false }

match executeQFTWithBackend config backend 1000 None with
| Ok measurementCounts ->
    printfn "QFT executed on quantum hardware"
    measurementCounts 
    |> Map.iter (fun state count -> printfn "  |%d⟩: %d counts" state count)
| Error msg ->
    printfn "Backend execution failed: %s" msg

// Convenience functions
executeStandardQFT 5 backend 1000      // Standard QFT with swaps
executeInverseQFT 5 backend 1000       // Inverse QFT (QFT†)
executeQFTOnState 5 7 backend 1000     // QFT on specific input state |7⟩

Features:

• ✅ O(n²) gate complexity (exponential speedup over classical)
• ✅ Cloud backend execution (IonQ, Rigetti, LocalBackend)
• ✅ Controlled phase gates (CP) with correct decomposition
• ✅ Bit-reversal SWAP gates (optional for QPE)
• ✅ Inverse QFT (QFT†) for result decoding
• ✅ Angle validation (prevents NaN/infinity)
• ✅ Measurement-based results (histogram of basis states)

Backend Limitations:

• ⚠️ Backend execution returns measurement statistics only (not full state vector)
• ⚠️ Amplitudes and phases are lost during measurement (fundamental quantum limitation)
• ✅ Suitable for algorithms that measure QFT output (Shor's, Phase Estimation)
• ✅ For amplitude/phase analysis, use local simulation

Use Cases:

• Shor's Algorithm: Integer factorization (period finding step)
• Quantum Phase Estimation: Eigenvalue estimation for VQE improvements
• Period Finding: Hidden subgroup problems
• Quantum Signal Processing: Frequency domain analysis

Performance:

• 3 qubits: 12 gates (3 H + 6 CPhase + 1 SWAP)
• 5 qubits: 27 gates (5 H + 20 CPhase + 2 SWAP)
• 10 qubits: 105 gates (10 H + 90 CPhase + 5 SWAP)

Location:

• Local simulation: Algorithms/QuantumFourierTransform.fs
• Backend adapter: Algorithms/QFTBackendAdapter.fs

Status: Production-ready - Foundational algorithm with full backend support (as of Nov 2025)

---

Library Scope & Focus

Primary Focus: QAOA-Based Combinatorial Optimization

This library is designed for NISQ-era practical quantum advantage in optimization:

• ✅ 6 optimization problem builders (Graph Coloring, MaxCut, TSP, Knapsack, Portfolio, Network Flow)
• ✅ QAOA implementation with automatic parameter tuning
• ✅ Error mitigation for noisy hardware (ZNE, PEC, REM)
• ✅ Production-ready solvers with cloud backend integration

Secondary Focus: Quantum Algorithm Education & Research

The Algorithms/ directory contains foundational quantum algorithms for learning:

• ✅ Grover's Search (quantum search, O(√N) speedup)
• ✅ Amplitude Amplification (generalization of Grover)
• ✅ Quantum Fourier Transform (O(n²) vs O(n·2^n) classical FFT)

Why F# for Quantum?

• Type-safe quantum circuit construction
• Functional programming matches quantum mathematics
• Interop with .NET ecosystem (C#, Azure, ML.NET)
• Higher level abstraction than Python (Qiskit) and Q#

---

🤝 Contributing

Contributions welcome!

Development principles:

• Maintain quantum-only architecture (no classical algorithms)
• Follow F# coding conventions
• Provide C# interop for new builders
• Include comprehensive tests
• Document QAOA encodings for new problem types

---

📄 License

Unlicense - Public domain. Use freely for any purpose.

---

📞 Support

• Documentation: docs/
• Issues: GitHub Issues
• Examples: examples/

---

Status: Production Ready - Quantum-only architecture, 6 problem builders, full QAOA implementation