Metadata-Version: 2.1
Name: pktron
Version: 6.0.0
Summary: PKTron v6.0.0 — HPC Quantum Computing Framework
Home-page: https://github.com/cetqac/pktron
Author: CETQAC
Author-email: CETQAC <cetqac@pktron.io>
License: MIT
Project-URL: Homepage, https://github.com/cetqac/pktron
Project-URL: Bug Tracker, https://github.com/cetqac/pktron/issues
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.20
Requires-Dist: scipy>=1.7
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: build>=0.10; extra == "dev"
Requires-Dist: twine>=4.0; extra == "dev"
Provides-Extra: gpu
Requires-Dist: cupy-cuda12x>=11.0; extra == "gpu"

# PkTron Quantum HPC & SDK Simulator

### Top # 1 in Asia and South and Top 5 Globally (Based on Features, Modules and Breath)

### ✨ Downloads more than 10K

[![Python ≥3.8](https://img.shields.io/badge/python-3.8%2B-blue.svg)](https://www.python.org/)
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[![HPC Ready](https://img.shields.io/badge/HPC-Ready-green.svg)]()
[![SDK Ready](https://img.shields.io/badge/SDK-Ready-blue.svg)]()
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---

## What is PkTron?

**PkTron v6.0.0** is a unified Quantum HPC + SDK Simulator framework — one of the
most feature-complete quantum computing platforms available anywhere in the world,
with **over 10,000 downloads** on PyPI.

A single `pip install pktron` gives you:

- **13 simulator backends** (statevector, density matrix, MPS, Clifford, MERA, PEPS, tensor network, multi-GPU, distributed, pulse, trajectory, superoperator, matchgate, fermionic-Gaussian)
- **50+ quantum algorithms** (Grover, Shor, QPE, QFT, HHL, VQE, QAOA, Simon, Deutsch–Jozsa, quantum walks, QSVT, amplitude amplification, quantum counting, adiabatic, Metropolis, SDP, NAS, GRAPE)
- **Full HPC subsystem** with compiled C kernels (AVX-512/AVX2/SSE/OpenMP), GPU runtime, MPI distributed runtime, circuit cache, and tensor-network kernels
- **Complete quantum SDK** — circuit construction, transpilation, serialization (QPY, QASM2/3, Quil, IonQ, Braket), interop (Qiskit, Cirq, PennyLane)
- **Quantum chemistry stack** — UCCSD, ADAPT-VQE, molecular Hamiltonians, fermionic mappers (JW, Parity, Bravyi–Kitaev)
- **6 error-correction codes** (Steane, Surface, Bacon–Shor, Color, Repetition, Heavy-Hex) with MWPM / PyMatching decoders
- **7+ error-mitigation methods** (ZNE, PEC, CDR, M3, dynamical decoupling, twirling, symmetry verification, probabilistic error amplification, virtual distillation)
- **10+ quantum ML algorithms** (QNN, QSVM, QGAN, QCNN, QBM, QRL, transfer learning, federated learning, barren-plateau-free QNN, kernel trainer, meta-learner)
- **Quantum cryptography** — BB84, E91, B92, TF-QKD, MDI-QKD, DI-QKD, post-quantum primitives, blind quantum computing, quantum money, digital signatures
- **Finance & Defense industry modules** — portfolio optimization, option pricing, VaR/ES, anomaly detection, VRP, mission scheduling, swarm optimization, cryptanalysis
- **Full interoperability** with Qiskit, Cirq, PennyLane, OpenQASM 2/3, Quil, IonQ, Braket

> Developed and maintained by **CETQAC** (Centre of Excellence for Technology Quantum and AI Canada/Pakistan).

---

## What's new in v6.0.0

| Class | What it does |
|---|---|
| **`pk.Pauli`** | Symplectic-representation Pauli class with full algebra: `@` composition, `^` tensor product, `commutes()`, `to_matrix()`, `adjoint()`. Verified: `XY = iZ`, `[X,X]=0`, `{X,Z}=0`. |
| **`pk.E91Protocol`** | Ekert91 entanglement-based QKD with CHSH inequality test. Clean channel CHSH ≈ 2.77, with eavesdropper drops below 2 → intrusion detected. |
| **`pk.M3MeasurementMitigation`** | Matrix-free / subspace readout error mitigation. Solves only over observed bitstrings — scales to many qubits where direct inversion fails. |
| **`SparsePauliOp` at top level** | Was buried in `pktron.quantum_info.operators`, now `pk.SparsePauliOp`. |
| **`DMRGSolver` under `tensor_networks`** | Was in `pktron.dmrg`, now also `pktron.tensor_networks.DMRGSolver`. |
| **MPS SVD bug fix** | `MPSSimulator._apply_2q_nn` `self._fast_svd` → `np.linalg.svd`. Entangling circuits run correctly on MPS now. |

---

## Installation

```bash
pip install pktron
```

```bash
# Optional: GPU / HPC extras
pip install pktron[gpu]
```

---

## Quick-Start

### Bell state
```python
import pktron as pk

qc = pk.QuantumCircuit(2)
qc.h(0); qc.cx(0, 1)
result = pk.StatevectorSimulator().run(qc, shots=1024)
print(result["counts"])           # {'00': ~512, '11': ~512}
```

### Or use the top-level `execute()` helper
```python
qc = pk.QuantumCircuit(2); qc.h(0); qc.cx(0, 1)
print(pk.execute(qc, shots=1024)["counts"])
```

### Grover's Search (4 qubits, 2 marked states)
```python
g = pk.GroverSearch(n_qubits=4, marked_states=[5, 10])
r = g.run()
print(f"Found: {r['found']}  probability: {r['success_prob']:.3f}")
```

### VQE on H₂ — matches FCI exactly
```python
H = pk.QuantumChemistry.h2_hamiltonian(distance=0.735)
r = pk.VQE(H).run(ansatz_depth=2, max_iter=200)
print(f"Ground energy: {r['energy']:.6f} Ha")
# Ground energy: -1.872798 Ha  (FCI = -1.872798 Ha)
```

### Pauli algebra (v6.0.0)
```python
import pktron as pk

p = pk.Pauli("XYZ")
print(p, p.num_qubits, p.to_matrix().shape)   # XYZ 3 (8, 8)

# Physics is correct
xy = pk.Pauli("X") @ pk.Pauli("Y")
print(xy)                                      # iZ

assert pk.Pauli("X").commutes(pk.Pauli("X"))      # True
assert pk.Pauli("X").anticommutes(pk.Pauli("Z"))  # True
```

### E91 entanglement-based QKD (v6.0.0)
```python
import pktron as pk

clean = pk.E91Protocol.run(n_pairs=2048, eavesdrop=False, visibility=0.98, seed=42)
print(f"Clean CHSH={clean['chsh_s']:.2f}  secure={clean['secure']}  qber={clean['qber']:.3f}")
# Clean CHSH=2.77  secure=True  qber=0.020   (Tsirelson bound = 2√2 ≈ 2.83)

eve = pk.E91Protocol.run(n_pairs=2048, eavesdrop=True, visibility=0.98, seed=42)
print(f"Eavesdrop CHSH={eve['chsh_s']:.2f}  detected={eve['eavesdropping_detected']}")
# Eavesdrop CHSH=1.39  detected=True
```

### M3 measurement mitigation (v6.0.0)
```python
import pktron as pk

noisy_counts = {"000": 8500, "111": 9000, "001": 850, "110": 850,
                "010": 350, "101": 350, "011": 50, "100": 50}

m3 = pk.M3MeasurementMitigation(n_qubits=3).calibrate(p1_given_0=0.03, p0_given_1=0.04)
mitigated = m3.mitigate(noisy_counts)
print(sorted(mitigated.items(), key=lambda x: -x[1])[:3])
```

### Surface-code error scaling (sub-threshold)
```python
for d in [3, 5, 7]:
    scd = pk.SurfaceCodeDistance(distance=d)
    r = scd.logical_error_rate(noise_rate=0.001)
    print(f"d={d}: P_L = {r['logical_x_rate']:.2e}")
# d=3: P_L = 3.00e-04
# d=5: P_L = 3.00e-05
# d=7: P_L = 3.00e-06
```

---

# Complete Feature Reference

This is **everything** that ships in `pip install pktron`. All 150 classes, 26 functions, and 39 submodules are listed here so you know exactly what's available.

## Core Simulators (13 backends)

| Simulator | Description |
|---|---|
| `StatevectorSimulator` | Exact full-statevector (≤28 qubits) |
| `UnitarySimulator` | Returns the full unitary matrix |
| `DensityMatrixSimulator` | Mixed states + Kraus channels |
| `MPSSimulator` | Matrix Product State (20–100 qubits) |
| `CliffordSimulator` | Stabilizer tableau (millions of qubits) |
| `ExtendedStabilizerSimulator` | Clifford + T gates |
| `SuperOpSimulator` | Superoperator representation |
| `PulseLevelSimulator` | Time-domain Lindblad master equation |
| `QuantumTrajectorySimulator` | Monte Carlo wave-function trajectories |
| `PEPSSimulator` | Projected Entangled Pair States (2D) |
| `MERASimulator` | Multi-scale Entanglement Renormalization |
| `TensorNetworkSimulator` | General tensor network simulation |
| `MultiGPUSimulator` | Distributed GPU statevector |

Specialty simulators (in submodules):
- `MatchgateSimulator` (`pktron.matchgate_sim`) — efficient matchgate / free-fermion circuits
- `FermionicGaussianSimulator` (`pktron.fermionic_gaussian`) — Gaussian-state fermionic simulator
- `AdaptiveMPSSimulator` (`pktron.advanced`) — entanglement-adaptive MPS

## Circuit Construction

**Core classes:**
- `QuantumCircuit` — primary circuit object
- `Gate` — individual gate with name, qubits, params, matrix
- `Parameter`, `ParameterVector` — symbolic parameters

**Single-qubit gates:** H, X, Y, Z, S, T, SDG, TDG, SX, SXDG, I/ID, RX, RY, RZ, P/Phase, U3, U2, U1
**Two-qubit gates:** CX/CNOT, CY, CZ, SWAP, ISWAP, DCX, ECR, CRX, CRY, CRZ, RXX, RYY, RZZ, CP, CU, CH, CT
**Three-qubit gates:** CCX/Toffoli, CSWAP/Fredkin

**Top-level gate factories:** `RX()`, `RY()`, `RZ()`, `U3()`

**Circuit Operations:**
- `.compose()`, `.tensor()`, `.inverse()`, `.repeat()`, `.control()`, `.power()`
- `.assign_parameters()`, `.bind_parameters()`
- `.gate_count()`, `.analysis()`, `.depth()`, `.to_dag()`
- `.barrier()`, `.measure()`, `.save_statevector()`
- `.draw(mode='unicode'/'text'/'mpl', fold=80)`

**Control-flow instructions:** `IfInstruction`, `ForLoopInstruction`, `WhileLoopInstruction`, `SaveInstruction`

## Compilation & Transpilation

**Pass manager (`pktron.core`):**
- `PassManager`, `TranspilerPass`
- `GateCancellationPass` — cancels X•X → I, etc.
- `TCountOptimizationPass` — T•T → S, S•S → Z
- `SABRERoutingPass`, `SABRERouter` — routing for connectivity constraints
- `LocalNoisePass` — inject depolarizing noise per gate
- `RelaxationNoisePass` — T1/T2 relaxation noise

**Compiler IR:**
- `QuantumIR` (`pktron.compiler.ir`)

**Noise-aware compilation:**
- `NoiseAwareCompiler` (`pktron.noise_aware_compile`)

**KAK / Euler decomposition (`pktron.decompose`):**
- `euler_zyz()` — ZYZ Euler angles for any SU(2)
- `decompose_1q_to_basis()` — ZYZ, IBM (RZ+SX), U3
- `kak_decomposition()` — Cartan decomposition of SU(4)
- `decompose_2q_to_cnot()` — arbitrary 2-qubit → CNOT + 1q gates

## Serialization

- `save_circuit()`, `load_circuit()` — QPY binary format
- `circuit_to_qasm2()`, `circuit_from_qasm2()` — OpenQASM 2.0
- `circuit_to_qasm3()` — OpenQASM 3.0
- `circuit_to_dict()`, `circuit_from_dict()` — JSON dict
- `OpenQASM3` (`pktron.advanced`)
- `QuilExporter` — Rigetti Quil 2.x with DEFGATE
- `IonQExporter` — IonQ JSON with schema validation
- `BraketExporter` — Amazon Braket IR (OpenQASM + JAQCD)

## Interoperability

- `InteropConverter` (`pktron.interop`) — unified converter class
- `QiskitImporter` — all Qiskit standard gates + to_matrix() fallback
- `CirqImporter` — Cirq Circuit with moment structure
- `PennyLaneImporter` — QNode / QuantumTape import

## Quantum Primitives (Qiskit-compatible)

- `Sampler`, `SamplerJob`, `SamplerResult`
- `Estimator`, `EstimatorJob`, `EstimatorResult`
- Session-based batching

## Quantum Algorithms (50+ classes)

**Search & Optimization:**
- `GroverSearch` — diagonal-matrix oracle, correct for any n
- `AmplitudeAmplification` — generalized Grover
- `QuantumCounting` — counts marked states via QPE
- `QAOA` / `qaoa_max_cut()` — variational combinatorial optimization
- `QuantumAnnealing`, `QuantumAnnealing2` — quantum annealing

**Factoring & Number Theory:**
- `Shor` — QPE-based period finding + classical post-processing

**Function Problems:**
- `DeutschJozsa` — correct constant oracle (all_zero_prob = 1.0)
- `SimonsAlgorithm` — quantum circuit + GF(2) classical recovery

**Phase & Transform:**
- `QuantumFourierTransform` — QFT unitary and circuit
- `QuantumPhaseEstimation` — controlled-U chain + IQFT

**Linear Algebra:**
- `HHLAlgorithm` — Harrow–Hassidim–Lloyd linear systems

**Walks & Dynamics:**
- `QuantumWalk`, `QuantumWalkSearch`

**Advanced algorithms (`pktron.advanced_algorithms`):**
- `QuantumMetropolis` — quantum Metropolis sampling
- `LCUFramework` — linear combination of unitaries
- `QuantumSDP` — quantum semidefinite programming
- `AdiabticQuantumOptimizer` — adiabatic optimization
- `QuantumPhaseKickback` — phase-kickback primitives

**New algorithms (`pktron.new_algorithms`):**
- `QuantumWalkSearch` — quantum walk-based search
- `VariationalQuantumEigensolver2` — extended VQE
- `QuantumOptimalControl` — GRAPE: L-BFGS-B with analytical gradient
- `QuantumAnnealing2` — enhanced annealing
- `QuantumNeuralArchitectureSearch` — NAS for quantum circuits
- `QuantumErrorLearning` — process tomography + GST

## Variational Algorithms (VQE family)

- `VQE` — hardware-efficient ansatz, parameter-shift BFGS
- `VariationalQuantumEigensolver2` — extended VQE
- `QAOA` — QAOA with p layers

**Chemistry-specific VQE (`pktron.advanced`):**
- `UCCSDSolver` — UCCSD-VQE, reaches FCI on H₂
- `ADAPTVQESolver` — skew-Hermitian pool, multi-layer, monotone

## Quantum Chemistry

- `QuantumChemistry` — hamiltonian builders, mappers, transformers
- `QuantumChemistry.h2_hamiltonian()` — exact STO-3G H₂
- Molecular library: H₂, N₂, CH₄, CO₂, NH₃, C₂H₄
- Mappers: ParityMapper, BravyiKitaev (Fenwick tree), Jordan–Wigner
- Transformers: ActiveSpaceTransformer, FreezeCoreTransformer, Z2Symmetries
- Initial states: HartreeFockInitialPoint, Molecule

## Quantum Machine Learning

**Core QML (`pktron.core`):**
- `QuantumNeuralNetwork` — parametric QNN, analytic gradients
- `QSVM` — Quantum Support Vector Machine
- `QuantumGAN` — Quantum Generative Adversarial Network
- `QuantumAutoencoder` — circuit-based autoencoder
- `QuantumCNN` — Quantum Convolutional Neural Network
- `QuantumBoltzmannMachine` — QBM with FD gradient
- `QuantumFederatedLearning` — federated quantum learning
- `QuantumReinforcementLearning` — RL with quantum policy
- `QuantumTransferLearning` — transfer-learning circuits

**Advanced QML (`pktron.advanced_qml`):**
- `BarrenPlateauFreeQNN` — barren-plateau-resistant QNN
- `QuantumKernelTrainer` — quantum kernel learning
- `QuantumMetaLearner` — quantum meta-learning
- `ShotFrugalOptimizer` — shot-efficient variational optimizer

**Barren plateau analysis (`pktron.barren_plateau`):**
- `BarrenPlateauAnalyzer` — trainability diagnostics

**Gradient methods (`pktron.gradients`):**
- `ParameterShiftGradient` — exact parameter-shift rule, Hessian

**Optimizers (`pktron.advanced`):**
- `JAXOptimizer` — autodiff via JAX backend

## Noise Models

**Channels (`pktron.noise_models`):**
- `NoiseChannel` — base class
- `DepolarizingNoise`, `AmplitudeDamping`, `PhaseDamping`
- `CrosstalkNoiseModel`, `ThermalNoiseModel`

**In `pktron.core`:**
- `NoiseModel` — composable noise model
- `PauliError` — weighted Pauli channel
- `PauliLindbladError` — Lindblad form Pauli noise

**Noise application:**
- `LocalNoisePass` — per-gate noise injection
- `RelaxationNoisePass` — T1/T2 relaxation

## Error Correction (6 codes)

- `Steane7QEC` — [[7,1,3]] Steane code: encode, syndrome, correct
- `SurfaceCode` — arbitrary odd distance d, stabilizers, logical ops
- `SurfaceCodeDistance` — monotone logical error rate formula
- `BaconShorCode` — [[9,1,3]] Bacon–Shor code
- `ColorCode` — triangular 2D color code
- `RepetitionCode` — bit-flip and phase-flip codes
- `HeavyHexCode` — IBM heavy-hex layout
- `FaultTolerantCircuit` — syndrome extraction between logical gates

**Decoders:**
- `BlossomVDecoder` — pure-Python MWPM decoder
- `PyMatchingDecoder` — PyMatching wrapper (optional dependency)
- `ThresholdEstimator` — Monte Carlo threshold estimation
- `DecoderComparison` — greedy vs MWPM benchmark

**Logical operations:** `logical_x()`, `logical_z()`, `logical_h()`, `logical_cnot()`

## Error Mitigation

**Core mitigation (`pktron.core`):**
- `ZeroNoiseExtrapolation` — Richardson extrapolation, poly, exp
- `ProbabilisticErrorCancellation` — quasi-probability
- `ReadoutErrorMitigation` — matrix inversion (full)
- `CliffordDataRegression` — CDR
- `DynamicalDecoupling` — XY4 and other sequences

**Advanced mitigation (`pktron.advanced_mitigation`):**
- `SymmetryVerification` — post-selection on conserved quantities
- `ProbabilisticErrorAmplification` — PEA
- `PauliNoiseLearner` — learn the noise model from data

**Specialized mitigation:**
- `M3MeasurementMitigation` (`pktron.m3_mitigation`) — **NEW in v6.0.0**, matrix-free / subspace
- `VirtualDistillation` (`pktron.advanced`) — virtual distillation purification

**ZNE folding strategies:** `fold_global`, `fold_gates_from_left`, `fold_gates_at_random`
**Extrapolators:** `RichardsonExtrapolation`, `ExponentialExtrapolation`, `PolyExpExtrapolation`
**Twirling:** `PauliTwirlingPass`, `CliffordTwirlingPass`

## Quantum Benchmarking

- `QuantumBenchmarking` — unified: QV, RB, CLOPS, XEB, gate fidelity
- `StandardRB` — randomized benchmarking, EPC, decay fit
- `InterleavedRB` — per-gate EPC via interleaved Clifford
- `MirrorRB` — mirror circuits, polarization
- `XEB` — cross-entropy benchmarking, per-cycle fidelity
- `CLOPS` — circuit-layer operations per second
- `StateTomography` — MLE-projected density matrix reconstruction
- `ProcessTomography` — χ-matrix via Choi isomorphism
- `GateTomography (GST)` — linear + iterative MLE
- `LayerFidelityEstimator` — simultaneous RB
- `LayerFidelityBenchmark`

## Pauli Framework

**Top-level (v6.0.0):**
- `pk.Pauli` — **NEW**, symplectic representation, full Pauli algebra
- `pk.SparsePauliOp` — sparse weighted sum: `+`, `-`, `*`, `@`, `**`, adjoint, simplify, chop

**Module (`pktron.pauli`):**
- `Pauli` — same as top-level, full arithmetic
- `PauliTerm`, `PauliSum` — sparse Pauli algebra
- `pauli_basis(n)`, `pauli_basis_labels(n)` — all 4^n Paulis
- `commutes()`, `commutator()`, `anti_commutator()`
- `qubit_wise_commuting_groups()` — QWC partitioning
- `general_commuting_groups()` — full commutativity graph coloring
- `PauliGrouper` — measurement-reduction orchestrator with basis-rotation circuits

**Sparse Hamiltonians (`pktron.sparse`):**
- `SparseHamiltonian` — CSR-format sparse H
- `ising_hamiltonian()`, `heisenberg_hamiltonian()`, `transverse_ising()`
- `from_dense()` — matrix → sparse Pauli decomposition
- `expectation_pauli_string()` — O(dim•n) without full matrix

## Pulse-Level Simulation

- **Channels:** `DriveChannel`, `ControlChannel`, `MeasureChannel`, `AcquireChannel`
- **Pulse shapes:** `Waveform`, `Gaussian`, `GaussianSquare`, `Drag`, `Constant`
- **Instructions:** `Play`, `Delay`, `Acquire`, `ShiftPhase`, `SetFrequency`
- **Schedules:** `Schedule` (`<<`, `|` operators), `ScheduleBlock` (4 alignment modes)
- **Simulator:** `PulseLevelSimulator` — Lindblad RK4 master equation
- **Calibration:** `InstructionScheduleMap`, `attach_calibration`, `build_standard_inst_map`
- **Pulse gates:** `DRAGPulse` (calibrated X gate), `CrossResonancePulse`

## Quantum Cryptography

**Core protocols:**
- `BB84Protocol` — QKD with realistic QBER floor (0.5%), eavesdrop detection
- `E91Protocol` — **NEW in v6.0.0**, entanglement-based with CHSH security test
- `PostQuantumCrypto` — post-quantum cryptographic primitives

**Advanced crypto (`pktron.advanced_crypto`):**
- `BlindQuantumComputing` — blind quantum computing protocols
- `QuantumDigitalSignature` — quantum digital signatures
- `QuantumMoney` — quantum money schemes
- `QuantumSecretSharing` — quantum secret-sharing

**QKD pipeline (`pktron.qkd_pipeline`):**
- `QKDPipeline` — BB84, E91, B92, TF-QKD, MDI-QKD, DIQKD

## Finance Module (`pktron.finance`)

- `QuantumAmplitudeEstimation` — QAE via Grover operator + QPE
- `QuantumPortfolioOptimizer` — Markowitz → Ising → QAOA
- `QuantumOptionPricer` — European/Asian options via QAE
- `QuantumCreditRisk` — VaR/ES via amplitude estimation
- `QuantumMonteCarlo` — QMC integration engine
- `QuantumAnomalyDetection` — quantum kernel variational classifier

## Defense Module (`pktron.defense`)

- `QuantumVRP` — vehicle routing via QUBO → QAOA
- `QuantumGameTheory` — Nash equilibrium via variational circuits
- `QuantumMissionScheduler` — RCPSP scheduling via QAOA
- `QuantumSwarmOptimizer` — multi-agent QAOA coordination
- `QuantumTargetDetection` — ZZ-feature map + variational classifier
- `QuantumCryptanalysis` — enhanced period finding + SVP + Grover key search

## HPC Subsystem

**C kernel (`pktron.kernels`):**
- `sv_kernels.c` — AVX-512/AVX2/SSE/OpenMP statevector kernels
- `apply_1q_gate`, `apply_h/x/y/z/s/t`, `apply_rz/ry`
- `apply_2q_gate`, `apply_cx/cz/swap`
- `fuse_1q_chain` — multi-gate fusion
- `compute_probs`, `sample_measurements`
- `expectation_diag`, `expectation_dense`, `expectation_csr` (sparse)
- `normalize_sv`
- `KernelSet` — Python wrapper with NumPy fallback

**Top-level C-backend functions (`pktron.c_backend`):**
- `apply_1q_gate_c()`, `apply_2q_gate_c()`
- `compute_probs_c()`, `sample_c()`
- `expectation_diag_c()`, `expectation_dense_c()`
- `c_backend_info()` — returns AVX/OpenMP/thread info

**Scheduler (`pktron.scheduler`):**
- `build_schedule()`, `Schedule`, `OpNode`
- Gate normalization, 1-qubit fusion pass, Clifford detection

**Runtime (`pktron.runtime`):**
- `StatevectorRuntime` — routes through kernel/scheduler + MPS auto-routing
- GPU → CuPy path, CPU → C kernel, Clifford → tableau
- `TaskGraphScheduler`, `AsyncExecutor`

**Sparse ops (`pktron.sparse`):**
- `PauliTerm`, `SparseHamiltonian`
- `ising_hamiltonian()`, `heisenberg_hamiltonian()`, `from_dense()`
- `expectation_pauli_string()` — O(dim•n) per term

**Circuit cache (`pktron.cache`):**
- `CircuitCache` — 2-level LRU (memory) + shelve (disk)
- SHA-256 circuit key, thread-safe, hit-rate tracking

**GPU backend (`pktron.gpu`):**
- `GPUBackend` — CuPy, CUDA RawKernels, memory pool
- Kernels: `apply_h_gpu`, `apply_1q_gpu`, `apply_cx_gpu`
- Falls back to CuPy `tensordot` then CPU

**Multi-GPU (`pktron.multi_gpu_engine`):**
- `MultiGPUSimulator` — distributed GPU statevector
- `GPUScheduler` — device placement and load balancing

**Distributed (`pktron.distributed`):**
- `DistributedRuntime` — MPI rank partitioning via mpi4py
- Local-qubit gates (no communication), global-qubit gates (`MPI_Sendrecv`)

**Benchmarks (`pktron.benchmarks`):**
- `bench_gate_scaling`, `bench_gate_types`, `bench_fusion_speedup`
- `bench_expectation`, `bench_sampling`, `bench_full_circuit`
- `bench_openmp_scaling`, `bench_correctness`
- `run_all(quick=True/False)`

## Hardware & Backend Infrastructure

**Hardware backend (`pktron.core`):**
- `HardwareBackend` — physical/mock device with noise
- `SABRERouter` — SABRE routing for connectivity constraints

**Modular backend registry (`pktron.modular_backends`):**
- `BackendRegistry`, `BackendPlugin`, `BackendCapabilities`
- `BackendLifecycleManager`
- `find_best_backend()` — auto-selects by circuit properties

**Calibration & drift:**
- `CalibrationData`, `QubitCalibration` (`pktron.hardware_calibration`)
- `DriftEngine`, `CalibrationDriftSimulator` (`pktron.drift_simulator`)
- `GateScheduler`, `GateSequence`, `TimingInfo` (`pktron.gate_scheduler`)

**Hardware reporting (`pktron.hardware_report`):**
- `HardwareExecutionReport`

**Dynamic circuits (`pktron.dynamic_circuits`):**
- `DynamicCircuit` — circuits with classical feedback
- `MidCircuitMeasurement`
- `ConditionalGate`

**Virtual devices (`pktron.virtual_devices`):**
- `VirtualDevice` — mock backend with realistic topology and noise

## Tensor Networks (`pktron.tensor_networks`)

- `MPSNetwork` — MPS state with per-site tensors
- `DMRGSolver` — DMRG ground state for Ising/Heisenberg (also at `pktron.dmrg`)
- `AdaptiveMPSSimulator` — entanglement-adaptive MPS

## QSVT (Quantum Singular Value Transformation, `pktron.qsvt`)

- `QSVT` — QSP angle finder (Adam gradient descent), QSVT circuits
- `QSPAngleFinder` — Chebyshev–Gauss nodes optimization
- `QSPCircuit` — assembles full U_φ circuit
- `LinearCombinationBlockEncoding`, `SparseAccessBlockEncoding`
- Methods: `hamiltonian_simulation`, `ground_state_projector`, `apply_function`, `linear_system_solve`

## Circuit Visualization (`pktron.circuit_drawing`)

- `CircuitDrawer` — text (ASCII), unicode box-drawing, matplotlib
- Modes: `'text'`, `'unicode'`, `'mpl'`
- `QuantumCircuit.draw()` — `.draw(mode='unicode', fold=80)`
- `QuantumCircuit.__repr__()` — auto-draws in notebooks

## Circuit Debugger (`pktron.circuit_debugger`)

- `QuantumCircuitDebugger` — step-through circuit execution with intermediate states

## Infrastructure & Tooling

**Config (`pktron.config`):**
- `PKTronConfig`, `get_config()`, `set_config()`, `reset_config()`

**Validation (`pktron.validation`):**
- `QuantumStateValidator`, `validate_input()`
- `PhysicsValidator` — checks norm, Hermiticity, PSD, Bloch vector

**Profiling (`pktron.profiling`):**
- `PerformanceMonitor`, `get_profiler()`

**Top-level helpers:**
- `pk.execute(qc, shots=...)` — one-liner circuit execution
- `pk.backend_info()` — returns version + compiled-backend status

**Benchmark registry:**
- `BenchmarkRegistry` — classification tags (exact/approximate/heuristic/mock)
- `run_seeded(42)`, `report()`

---

## Complete Submodule Index

Every importable subpackage that ships with PkTron:

```
pktron                          # top level (~190 public symbols re-exported here)
pktron.core                     # 89 classes/functions — simulators, algorithms, gates
pktron.advanced                 # UCCSDSolver, ADAPTVQESolver, AdaptiveMPSSimulator,
                                #   JAXOptimizer, OpenQASM3, SurfaceCodeDistance,
                                #   VirtualDistillation
pktron.advanced_algorithms      # QuantumMetropolis, LCUFramework, QuantumSDP,
                                #   AdiabticQuantumOptimizer, QuantumPhaseKickback
pktron.advanced_crypto          # BlindQuantumComputing, QuantumDigitalSignature,
                                #   QuantumMoney, QuantumSecretSharing
pktron.advanced_mitigation      # PauliNoiseLearner, ProbabilisticErrorAmplification,
                                #   SymmetryVerification
pktron.advanced_qml             # BarrenPlateauFreeQNN, QuantumKernelTrainer,
                                #   QuantumMetaLearner, ShotFrugalOptimizer
pktron.barren_plateau           # BarrenPlateauAnalyzer
pktron.benchmarks               # HPC benchmark suite
pktron.c_backend                # C-extension Python bindings
pktron.cache                    # CircuitCache (LRU + shelve)
pktron.circuit_debugger         # QuantumCircuitDebugger
pktron.circuit_drawing          # CircuitDrawer
pktron.compiler                 # QuantumIR (compiler IR)
pktron.config                   # PKTronConfig
pktron.decompose                # KAK / Euler decomposition
pktron.defense                  # 6 defense classes
pktron.distributed              # MPI distributed runtime
pktron.dmrg                     # DMRGSolver
pktron.drift_simulator          # CalibrationDriftSimulator, DriftEngine
pktron.dynamic_circuits         # DynamicCircuit, MidCircuitMeasurement
pktron.e91_protocol             # E91Protocol  (NEW in v6.0.0)
pktron.fermionic_gaussian       # FermionicGaussianSimulator
pktron.finance                  # 6 finance classes
pktron.gate_scheduler           # GateScheduler, GateSequence
pktron.gpu                      # GPUBackend (CuPy)
pktron.gradients                # ParameterShiftGradient
pktron.hardware_calibration     # CalibrationData, QubitCalibration
pktron.hardware_report          # HardwareExecutionReport
pktron.interop                  # InteropConverter (Qiskit / Cirq / PennyLane)
pktron.kernels                  # C statevector kernels (.so packaged in wheel)
pktron.m3_mitigation            # M3MeasurementMitigation  (NEW in v6.0.0)
pktron.matchgate_sim            # MatchgateSimulator
pktron.modular_backends         # BackendRegistry, BackendPlugin
pktron.multi_gpu_engine         # MultiGPUSimulator, GPUScheduler
pktron.new_algorithms           # 6 new algorithms (GRAPE, NAS, QErrorLearning, …)
pktron.noise_aware_compile      # NoiseAwareCompiler
pktron.noise_models             # 6 noise channels
pktron.pauli                    # Pauli (NEW), PauliTerm, PauliSum
pktron.profiling                # PerformanceMonitor
pktron.qkd_pipeline             # QKDPipeline
pktron.qsvt                     # QSVT
pktron.quantum_info             # SparsePauliOp (also at top level in v6.0.0)
pktron.runtime                  # StatevectorRuntime
pktron.scheduler                # Op-graph scheduler
pktron.sparse                   # SparseHamiltonian, Ising/Heisenberg builders
pktron.tensor_networks          # MPSNetwork, DMRGSolver (NEW re-export), AdaptiveMPSSimulator
pktron.validation               # QuantumStateValidator, PhysicsValidator
pktron.virtual_devices          # VirtualDevice
```

---

## Why PkTron Ranks Top in Asia and Globally Top 5

| Feature | PkTron v6.0.0 | Typical alternatives |
|---|---|---|
| Simulator backends | **13** (incl. matchgate, fermionic-gaussian, pulse-level) | 3–5 |
| Native gate set | **23+ gates** | 10–15 |
| Quantum algorithms | **50+** | 10–20 |
| Variational/Chemistry VQE family | **9** (VQE, UCCSD, ADAPT, kUpCCGSD, PUCCD, SUCCD, qEOM, SSVQE, EvolvedOp) | 1–2 |
| QML algorithms | **13** (incl. BarrenPlateauFreeQNN, MetaLearner, KernelTrainer) | 2–4 |
| QEC codes | **6** (Steane, Surface, Bacon-Shor, Color, Repetition, Heavy-Hex) | 1–2 |
| Error mitigation methods | **9+** (ZNE, PEC, CDR, M3 NEW, DD, twirling, PEA, VD, SymVer) | 1–2 |
| QKD protocols | **6** (BB84, E91 NEW, B92, TF, MDI, DI) | 1 |
| Finance industry module | **✅** 6 classes | ✗ |
| Defense industry module | **✅** 6 classes | ✗ |
| HPC C kernel (AVX-512/OMP) | **✅** | ✗ |
| GPU backend (CuPy) | **✅** | Optional/paid |
| MPI distributed runtime | **✅** | Rare |
| Tensor networks (MPS/PEPS/MERA/DMRG) | **✅** | Limited |
| Interop targets | **5** (Qiskit, Cirq, PennyLane, QASM3, Quil) | 1–2 |
| Open source | **✅ MIT** | Often proprietary |
| PyPI downloads | **10K+** | Varies |

---

## License

MIT License — Copyright © 2024–2026 CETQAC
