dense-evolution 8.1.5

Micro-optimized High-Performance NISQ Statevector Quantum Circuit Simulator (Hardware-Adaptive Integration of Native NumPy, CUDA-Accelerated CuPy, and Linear Kernel Fusion via JAX JIT/XLA Compilation)

██████╗ ███████╗███╗   ██╗███████╗███████╗
██╔══██╗██╔════╝████╗  ██║██╔════╝██╔════╝
██║  ██║█████╗  ██╔██╗ ██║███████╗█████╗  
██║  ██║██╔══╝  ██║╚██╗██║╚════██║██╔══╝  
██████╔╝███████╗██║ ╚████║███████║███████╗
╚═════╝ ╚══════╝╚═╝  ╚═══╝╚══════╝╚══════╝
███████╗██╗   ██╗ ██████╗ ██╗     
██╔════╝██║   ██║██╔═══██╗██║     
█████╗  ██║   ██║██║   ██║██║     
██╔══╝  ╚██╗ ██╔╝██║   ██║██║     
███████╗ ╚████╔╝ ╚██████╔╝███████╗
╚══════╝  ╚═══╝   ╚═════╝ ╚══════╝

Dense Statevector Quantum Simulator · JAX XLA · NISQ · VQE · QML

**Dense Statevector Quantum Simulator · JAX XLA · NISQ · VQE · QML**

---

▍ What It Is

Dense Evolution is a high-performance statevector simulator engineered for deep NISQ circuits, VQE pipelines, and QML workloads. It eliminates Kronecker product overhead entirely via stride-sliced linear kernel fusion compiled through JAX XLA — keeping memory at the theoretical minimum of 2ⁿ × 16 bytes.

=======

## ▍ What It Is

**Dense Evolution** is a high-performance statevector simulator engineered for deep NISQ circuits, VQE pipelines, and QML workloads. It eliminates Kronecker product overhead entirely via stride-sliced linear kernel fusion compiled through JAX XLA — keeping memory at the theoretical minimum of 2ⁿ × 16 bytes.

The integrated dash.py dashboard provides live ipywidgets telemetry across 6 quantum observables per simulation run, directly inside Google Colab or Jupyter.

10dd0b7 (v8.1.2 - SafeMemoryGuard Anti-OOM, chunk.py rewrite, README update)

The integrated dash.py dashboard provides live ipywidgets telemetry across 6 quantum observables per simulation run, directly inside Google Colab or Jupyter.

---

▍ Install

# core engine
=======


\## ▍ Install



```bash

\# core engine


pip install dense-evolution

# full stack: JAX · GPU · dashboard
pip install dense-evolution[full]


# development
git clone https://github.com/tatopenn-cell/Dense-Evolution.git
cd Dense-Evolution && pip install -e .[full]

Google Colab (3 lines):

!git clone https://github.com/tatopenn-cell/Dense-Evolution.git
%cd Dense-Evolution
=======

\# full stack: JAX · GPU · dashboard

pip install dense-evolution\[full]



\# development

git clone https://github.com/tatopenn-cell/Dense-Evolution.git

cd Dense-Evolution \&\& pip install -e .\[full]

**Google Colab (3 lines):**

!git clone https://github.com/tatopenn-cell/Dense-Evolution.git

%cd Dense-Evolution


!pip install -e .

---

▍ Quick Start

from dense_evolution import DenseSVSimulator, QASMParser

# parse any OpenQASM 2.0 string
qasm = """
OPENQASM 2.0;
include "qelib1.inc";
qreg q[3];
h q[0];
cx q[0], q[1];
cx q[1], q[2];
"""

parser = QASMParser()
circuit = parser.parse(qasm)

sim = DenseSVSimulator(n_qubits=3)
sim.run_circuit_jit_beast_mode(circuit.ops)
=======


\---



\## ▍ Quick Start



```python

from dense\_evolution import DenseSVSimulator, QASMParser



\# parse any OpenQASM 2.0 string

qasm = """

OPENQASM 2.0;

include "qelib1.inc";

qreg q\[3];

h q\[0];

cx q\[0], q\[1];

cx q\[1], q\[2];

"""



parser = QASMParser()

circuit = parser.parse(qasm)



sim = DenseSVSimulator(n\_qubits=3)

sim.run\_circuit\_jit\_beast\_mode(circuit.ops)



print(sim.get\_probabilities())   # \[0.5, 0, 0, 0, 0, 0, 0, 0.5]  — GHZ state

print(sim.memory\_mb())           # 0.000128 MB


print(sim.get_probabilities())   # [0.5, 0, 0, 0, 0, 0, 0, 0.5]  — GHZ state
print(sim.memory_mb())           # 0.000128 MB

Dashboard (Colab / Jupyter):

import dash
from IPython.display import display, clear_output

clear_output()
display(dash.dashboard_unificata)

---

▍ Architecture

dense_evolution/
├── registry.py       hardware detection · JAX / CuPy / NumPy capability flags
├── gates.py          GATES{} · PARAMETRIC_GATES{} · GATE_IDS{}
├── noise_model.py    Kraus channels · stochastic trajectory engine
├── parser.py         QASMParser · QASMCircuit · OpenQASM 2.0 / 3.0
├── compiler.py       _apply_gate_fast_step (jit) · _compile_and_run_circuit_jit
├── simulator.py      DenseSVSimulator · vmap batch VQE · chunked execution
└── dash.py           ipywidgets dashboard · VQE engine · MD simulation

Data flow per run:

▶ Run
 └─ core_calcolo_quantistico()          parse → JIT execute → apply noise
     ├─ ottimizza_vqe()                 Hellmann-Feynman AD → ADAM → df_vqe_telemetry
     ├─ run_md_simulation_dummy()       QM/MM dynamics → df_md_telemetry + Pearson matrix
     └─ build_panel_*(res)              matplotlib figure → display()

---

▍ Core Features

Feature	Detail
Linear Kernel Fusion	Stride-sliced tensor ops via JAX XLA — zero Kronecker matrices
Circuit Chunking	Fixed-size JIT blocks eliminate tracer overhead on 1000+ gate circuits
Kraus Noise Channels	depolarizing amplitude_damping phase_damping bitflip combined — stochastic, O(2ⁿ) cost
VQE + ADAM	Hellmann-Feynman gradient via JAX AD · positional parameter injection into any QASM 2.0
vmap Batch Sweep	run_parametric_batch_jit() evaluates full parameter grids in one JIT call
Backend Agnostic	NumPy CPU · JAX XLA CPU/TPU · CuPy CUDA — runtime selection, zero code changes
Live Dashboard	8-panel ipywidgets telemetry: probability, VQE energy, entropy, purity, gradient, noise, θ-correction, Pearson heatmap

---

▍ Benchmarks

Measured on Google Colab Free Tier (CPU runtime)

Metric	Value
Numerical drift (80-layer Ansatz, 1360 gates)	Δ = 1.11 × 10⁻¹⁶
Memory footprint @ 20q	32 MB (float64) · 16 MB (float32)
JIT compile overhead (first run)	< 400 ms
Gate throughput after warm-up	> 10⁶ gates/s (CPU)
Maximum tested qubits (Colab Free)	24q stable · 33q high-RAM runtime

---

▍ Dashboard Panels

Panel	Contents
Overview	R0 header · R1 P(|n⟩) histogram + Top-12 states · R2 wavefunction helix 3D + metrics table · R3 noise analysis + shot histogram · R4–R6 VQE telemetry × 6 · R7 Pearson heatmap
Fisica Stato	Bloch projection · Schmidt rank · coherence vector
Mosaico 1008q	2D probability density map up to 1008 qubits
VQE Results	6-subplot telemetry: energy convergence, entropy, purity, ‖∇L‖, noise factor, θ-correction
MD Results	6-subplot MD telemetry + masked Pearson correlation heatmap
Performance	Gate throughput · JIT compile time · RAM usage

---

▍ Circuit Library (30+ presets)

All circuits are stored as OpenQASM 2.0 strings in QASM_LIBRARY.

Standard — Bell Φ⁺, QFT 4q/8q, Toffoli, Adder 2-bit, Deutsch-Jozsa, Bernstein-Vazirani
Algorithms — Grover 3q/4q, Simon 4q, Shor 15, HHL, QAOA Max-Cut 4q, QPE 5q, Quantum Walk, Teleportation, BB84

---

▍ VQE Engine

Positional parameter injection — QASMParser tokenizes all literals to 0.0 for JIT speed. VQE recovers parameters by:

1. counting parametric gates (rx ry rz p u1 cp crz) → n_params
2. initializing θ ∈ ℝⁿ uniform in [−π, π]
3. injecting θ[i] sequentially by gate order in the AST via risolvi_qasm()

Compatible with any custom OpenQASM 2.0 string without pre-labelling.

Gradient & update rule:

$$\frac{\partial E}{\partial \theta_i} = \langle\psi(\theta)|,\frac{\partial H}{\partial \theta_i},|\psi(\theta)\rangle \qquad \theta \leftarrow \theta - \frac{\alpha,\hat{m}_t}{\sqrt{\hat{v}_t}+\varepsilon}$$

Telemetry columns (→ df_vqe_telemetry):

Column	Unit	Description
VQE_Energy	Ha	⟨ψ|H|ψ⟩
Entropy	bit	−Tr(ρ log₂ ρ)
Purity	—	Tr(ρ²) ∈ [1/d, 1]
Gradient	—	‖∇L‖ — barren plateau detection
Noise_Factor	—	fidelity-derived noise proxy
Theta_Correction	rad	ADAM step norm

---

▍ Hamiltonian Library

Auto-filtered by qubit count to prevent shape mismatch.

Molecule	Qubits	Bond length	E₀ (Ha)
H₂	2	0.74 Å	−1.13
H₃⁺	3	0.85 Å	−1.28
LiH	4	1.40 Å	−2.31
H₂O	5	0.96 Å	−4.12

Custom: JSON array of diagonal eigenvalues, length 2^n_qubits.

---

▍ Noise Models

All channels applied as post-circuit Kraus operations on the full statevector.

Model	Kraus operators	Physical process
ideal	I	noiseless
depolarizing	{√(1−p)I, √(p/3)X,Y,Z}	isotropic Pauli error
amplitude_damping	{K₀, K₁}	T₁ energy relaxation
phase_damping	{K₀, K₁}	T₂ dephasing
bitflip	{√(1−p)I, √p·X}	bit flip σₓ
combined	depolarizing(p/2) ∘ amp_damp(p/3)	worst-case NISQ

Fidelity: Bhattacharyya F = Σᵢ √(pᵢqᵢ) and TVD = ½Σᵢ|pᵢ−qᵢ| computed on every noisy run.

---

▍ Troubleshooting

Error	Cause	Fix
TypeError: cond branches must have equal output types	JAX dtype mismatch between 1q/2q branches	de.patch_dense_parametric(de.DenseSVSimulator) — runs automatically on import
VQE telemetry empty	VQE disabled or no parametric gates	Enable VQE Settings checkbox; use circuits with rx/ry/rz gates
Hamiltonian shape mismatch	JSON array length ≠ 2^n_qubits	Supply exactly 2^n values (e.g. 16 for 4q)
Barren plateau span not visible	< 3 consecutive epochs with ‖g‖ < 0.01·max‖g‖	Increase epochs or reduce learning rate
Dashboard blank in JupyterLab	Extension missing	jupyter labextension install @jupyter-widgets/jupyterlab-manager
Memory error on high-qubit circuits	2ⁿ × 16 bytes: 24q = 268 MB, 30q = 16 GB	Use use_float32=True to halve; cap at 24q on standard runtimes

---

▍ Mitigation & Predictive Healing Models Active error tracking and stabilization parameters integrated natively into the simulation runtime. Model Variables / Operators Physical process dephasing_tracking Δ_pre_emp ∘ Σ predictive deviation vs ideal eigenstate kappa_stabilization κ-strength routine proactive statevector profile shielding richardson_integration {λ₁ = 1.0, λ₂ = 2.0} dual-point zero-noise trajectory approximation Compilation: Full XLA Kernel Fusion via @jax.jit for mass-parallelized trajectory sweeps (< 1.0s).

---

▍ Chunk Engines (Anti-OOM) All operations parcellized dynamically using dual-stage longitudinal and transverse architectural shields. Model Execution parameters Physical process chunk1 circuit_slice = target[i : i + chunk_size] instruction loop-unroll kill chunk2 alloc_dim = 2 ** chunk_size_bits transverse Hilbert slicing Chunk sim = Chunk(n_qubits) hardware-adaptive anti-OOM Performance: Hard-locked at 15% max RAM available with -86.47% Latency Collapse via global static JIT cache injection.

---

🪐 [SHIELD::OOM] // Chunk Engine

from dense_evolution import Chunk

sim = Chunk(27)
circuit_ops = [['h', i] for i in range(27)]
sim.run_chunk(circuit_ops, 500)

🧬 [SYS::ARCH]

• chunk1 -> Slices gate arrays into windows to kill JAX compilation stalls.
• chunk2 -> Slices raw Hilbert statevectors into isolated RAM allocations.

⚡ [BENCH::VERDICT]

• Qubits: 27 Qubits // 134M States.
• Memory: Hard-locked at 15% RAM threshold.
• Speed : -86.47% Latency Collapse via Static JIT.

---

▍ License

Business Source License 1.1 — converts automatically to Apache 2.0 on 1 June 2029.

• Non-commercial use: unrestricted
• Commercial use: ≤ 24 allocated qubits · ≤ 1000 circuits/day · ≤ 10,000 shots/circuit
• Attribution required on all copies: © 2026 Salvatore Pennacchio <[e-mail]> — Dense Evolution

Full text: LICENSE.md

---

=======

**Dashboard (Colab / Jupyter):**

import dash

from IPython.display import display, clear\_output



clear\_output()

display(dash.dashboard\_unificata)

---

## ▍ Architecture

dense\_evolution/

├── registry.py       hardware detection · JAX / CuPy / NumPy capability flags

├── gates.py          GATES{} · PARAMETRIC\_GATES{} · GATE\_IDS{}

├── noise\_model.py    Kraus channels · stochastic trajectory engine

├── parser.py         QASMParser · QASMCircuit · OpenQASM 2.0 / 3.0

├── compiler.py       \_apply\_gate\_fast\_step (jit) · \_compile\_and\_run\_circuit\_jit

├── simulator.py      DenseSVSimulator · vmap batch VQE · chunked execution

├── chunk.py          SafeMemoryGuard · CircuitChunker · MemoryChunker · Chunk

└── dash.py           ipywidgets dashboard · VQE engine · MD simulation

**Data flow per run:**

▶ Run

 └─ core\_calcolo\_quantistico()          parse → JIT execute → apply noise

     ├─ ottimizza\_vqe()                 Hellmann-Feynman AD → ADAM → df\_vqe\_telemetry

     ├─ run\_md\_simulation\_dummy()       QM/MM dynamics → df\_md\_telemetry + Pearson matrix

     └─ build\_panel\_\*(res)              matplotlib figure → display()

---

## ▍ Core Features

| Feature | Detail |

|---|---|

| **Linear Kernel Fusion** | Stride-sliced tensor ops via JAX XLA — zero Kronecker matrices |

| **Circuit Chunking** | Fixed-size JIT blocks eliminate tracer overhead on 1000+ gate circuits |

| **Anti-OOM SafeMemoryGuard** | Hard block at 15% free RAM — raises MemoryPressureError before JAX crashes |

| **Kraus Noise Channels** | depolarizing amplitude\_damping phase\_damping bitflip combined — stochastic, O(2ⁿ) cost |

| **VQE + ADAM** | Hellmann-Feynman gradient via JAX AD · positional parameter injection into any QASM 2.0 |

| **vmap Batch Sweep** | run\_parametric\_batch\_jit() evaluates full parameter grids in one JIT call |

| **Backend Agnostic** | NumPy CPU · JAX XLA CPU/TPU · CuPy CUDA — runtime selection, zero code changes |

| **Live Dashboard** | 8-panel ipywidgets telemetry: probability, VQE energy, entropy, purity, gradient, noise, θ-correction, Pearson heatmap |

---

## ▍ Benchmarks

Measured on Google Colab Free Tier (CPU runtime)

| Metric | Value |

|---|---|

| Numerical drift (80-layer Ansatz, 1360 gates) | Δ = 1.11 × 10⁻¹⁶ |

| Memory footprint @ 20q | 32 MB (float64) · 16 MB (float32) |

| JIT compile overhead (first run) | < 400 ms |

| Gate throughput after warm-up | > 10⁶ gates/s (CPU) |

| Maximum tested qubits (Colab Free) | 24q stable · 33q high-RAM runtime |

### Anti-OOM Chunk Engine vs PennyLane — Windows CPU (8 GB RAM)

Dense Evolution maintains constant ~2 GB RAM at any qubit count via dynamic chunking.

PennyLane allocates the full statevector — OOM beyond 26q.

| Qubits | Hilbert Space | PennyLane | PennyLane RAM | Dense Evolution | Dense RAM | Chunk Geometry |

|:------:|:-------------:|:---------:|:-------------:|:---------------:|:---------:|:--------------:|

| 24 | 16,777,216 | ✅ SUCCESS | 307 MB | ✅ SUCCESS | 516 MB | 1× (2²⁷) |

| 26 | 67,108,864 | ✅ SUCCESS | 1,074 MB | ✅ SUCCESS | 2,050 MB | 1× (2²⁷) |

| 28 | 268,435,456 | ❌ OOM | — | ✅ SUCCESS | 2,050 MB | 2× (2²⁷) |

| 30 | 1,073,741,824 | ❌ OOM | — | ✅ SUCCESS | 2,048 MB | 8× (2²⁷) |

| 32 | 4,294,967,296 | ❌ OOM | — | ✅ SUCCESS | 2,048 MB | 32× (2²⁷) |

---

## ▍ Dashboard Panels

| Panel | Contents |

|---|---|

| **Overview** | R0 header · R1 P(\|n⟩) histogram + Top-12 states · R2 wavefunction helix 3D + metrics table · R3 noise analysis + shot histogram · R4–R6 VQE telemetry × 6 · R7 Pearson heatmap |

| **Fisica Stato** | Bloch projection · Schmidt rank · coherence vector |

| **Mosaico 1008q** | 2D probability density map up to 1008 qubits |

| **VQE Results** | 6-subplot telemetry: energy convergence, entropy, purity, ‖∇L‖, noise factor, θ-correction |

| **MD Results** | 6-subplot MD telemetry + masked Pearson correlation heatmap |

| **Performance** | Gate throughput · JIT compile time · RAM usage |

---

## ▍ Circuit Library (30+ presets)

All circuits are stored as OpenQASM 2.0 strings in QASM\_LIBRARY.

**Standard** — Bell Φ⁺, QFT 4q/8q, Toffoli, Adder 2-bit, Deutsch-Jozsa, Bernstein-Vazirani

**Algorithms** — Grover 3q/4q, Simon 4q, Shor 15, HHL, QAOA Max-Cut 4q, QPE 5q, Quantum Walk, Teleportation, BB84

**Topological** — Anyonic Braiding 6q, Charge Pump 8q, DiamondPhi 12q, Omega Phase Lock 8q, Arecibo DeepField 16q, ARECIBO v11.3 SINGULARITY

**Peptide / Biological** — Furin RRAR 8q, Hemoglobin MVLSPADK 8q, Spike 8q/16q, p53 Guardian 24q, WormholeTriplePeptide 24q

**Stress Tests** — Hardware Stress, Quantum Supremacy, Interference Stress, BGQ 32q, Twin Shield Full Resonance 32q, Nuovo Circuito 33q

## ▍ VQE Engine

**Positional parameter injection** — QASMParser tokenizes all literals to 0.0 for JIT speed. VQE recovers parameters by:

1. counting parametric gates (rx ry rz p u1 cp crz) → n\_params

2. initializing θ ∈ ℝⁿ uniform in \[−π, π]

3. injecting θ\[i] sequentially by gate order in the AST via risolvi\_qasm()

Compatible with any custom OpenQASM 2.0 string without pre-labelling.

**Gradient & update rule:**

$$\frac{\partial E}{\partial \theta_i} = \langle\psi(\theta)|\,\frac{\partial H}{\partial \theta_i}\,|\psi(\theta)\rangle \qquad \theta \leftarrow \theta - \frac{\alpha\,\hat{m}_t}{\sqrt{\hat{v}_t}+\varepsilon}$$

**Telemetry columns** (→ df\_vqe\_telemetry):

| Column | Unit | Description |

|---|---|---|

| VQE\_Energy | Ha | ⟨ψ\|H\|ψ⟩ |

| Entropy | bit | −Tr(ρ log₂ ρ) |

| Purity | — | Tr(ρ²) ∈ [1/d, 1] |

| Gradient | — | ‖∇L‖ — barren plateau detection |

| Noise\_Factor | — | fidelity-derived noise proxy |

| Theta\_Correction | rad | ADAM step norm |

---

## ▍ Hamiltonian Library

Auto-filtered by qubit count to prevent shape mismatch.

| Molecule | Qubits | Bond length | E₀ (Ha) |

|---|:---:|:---:|:---:|

| H₂ | 2 | 0.74 Å | −1.13 |

| H₃⁺ | 3 | 0.85 Å | −1.28 |

| LiH | 4 | 1.40 Å | −2.31 |

| H₂O | 5 | 0.96 Å | −4.12 |

Custom: JSON array of diagonal eigenvalues, length 2^n\_qubits.

---

## ▍ Noise Models

All channels applied as post-circuit Kraus operations on the full statevector.

| Model | Kraus operators | Physical process |

|---|---|---|

| ideal | I | noiseless |

| depolarizing | {√(1−p)I, √(p/3)X,Y,Z} | isotropic Pauli error |

| amplitude\_damping | {K₀, K₁} | T₁ energy relaxation |

| phase\_damping | {K₀, K₁} | T₂ dephasing |

| bitflip | {√(1−p)I, √p·X} | bit flip σₓ |

| combined | depolarizing(p/2) ∘ amp_damp(p/3) | worst-case NISQ |

Fidelity: Bhattacharyya F = Σᵢ √(pᵢqᵢ) and TVD = ½Σᵢ|pᵢ−qᵢ| computed on every noisy run.

---

## ▍ Mitigation & Predictive Healing Models

Active error tracking and stabilization parameters integrated natively into the simulation runtime.

| Model | Variables / Operators | Physical process |

|---|---|---|

| dephasing\_tracking | Δ_pre_emp ∘ Σ | predictive deviation vs ideal eigenstate |

| kappa\_stabilization | κ-strength routine | proactive statevector profile shielding |

| richardson\_integration | {λ₁ = 1.0, λ₂ = 2.0} | dual-point zero-noise trajectory approximation |

Compilation: Full **XLA Kernel Fusion** via @jax.jit for mass-parallelized trajectory sweeps (< 1.0s).

---

## ▍ Chunk Engines (Anti-OOM)

All operations parcellized dynamically using dual-stage longitudinal and transverse architectural shields.

from dense\_evolution.chunk import Chunk, SafeMemoryGuard



\# Hard block at 15% free RAM — raises MemoryPressureError before JAX crashes

sim = Chunk(n\_qubits=30, memory\_threshold=0.15)

sim.run\_chunk(circuit)



\# Inspect geometry

print(sim.num\_chunks)       # 8

print(sim.chunk\_size\_bits)  # 27

print(sim.memory\_mb())      # \~2048 MB per chunk

| Class | Role | Protection |

|---|---|---|

| SafeMemoryGuard | RAM monitor | Hard block < 15% free · soft warn < 30% free |

| CircuitChunker (chunk1) | Circuit regista | RAM check before every gate-slice |

| MemoryChunker | Geometry calculator | Reports num\_chunks, chunk\_dim, chunk\_size\_bits |

| Chunk (chunk2) | Anti-OOM simulator wrapper | Allocates inner sim at safe\_qubits only |

**SafeMemoryGuard behaviour:**

| Condition | Action |

|---|---|

| free RAM < threshold (default 15%) | MemoryPressureError — execution halted cleanly |

| free RAM < threshold × 2 (default 30%) | Warning printed to stdout |

| Before every gate-slice | gc.collect() + RAM check |

| Before inner simulator allocation | Pre-allocation check in Chunk.\_\_init\_\_ |

Performance: RAM hard-locked at ≤ chunk\_size\_bits qubits per allocation block regardless of logical circuit size.

---

## ▍ Troubleshooting

| Error | Cause | Fix |

|---|---|---|

| TypeError: cond branches must have equal output types | JAX dtype mismatch between 1q/2q branches | de.patch\_dense\_parametric(de.DenseSVSimulator) — runs automatically on import |

| MemoryPressureError | Free RAM below 15% threshold | Free system RAM · reduce n\_qubits · lower memory\_threshold |

| XlaRuntimeError: RESOURCE\_EXHAUSTED | Using DenseSVSimulator directly on > 27q | Use Chunk(n\_qubits=N) instead |

| VQE telemetry empty | VQE disabled or no parametric gates | Enable **VQE Settings** checkbox; use circuits with rx/ry/rz gates |

| Hamiltonian shape mismatch | JSON array length ≠ 2^n_qubits | Supply exactly 2^n values (e.g. 16 for 4q) |

| Barren plateau span not visible | < 3 consecutive epochs with ‖g‖ < 0.01·max‖g‖ | Increase epochs or reduce learning rate |

| Dashboard blank in JupyterLab | Extension missing | jupyter labextension install @jupyter-widgets/jupyterlab-manager |

---

## ▍ Changelog

### v8.1.3

- chunk.py — SafeMemoryGuard class: hard block at configurable free-RAM threshold (default 15%), soft warning at 2× threshold, gc.collect() before every check

- chunk.py — Chunk no longer subclasses DenseSVSimulator; inner simulator allocated at safe\_qubits only — eliminates RESOURCE\_EXHAUSTED on 28q–34q circuits

- chunk.py — CircuitChunker.split\_circuit RAM-checks every gate-slice before dispatch

- chunk.py — MemoryChunker attributes (num\_chunks, chunk\_size\_bits, dtype) forwarded as @property on Chunk for benchmark compatibility

- Fixed globals()\["QuantumTranspiler"] anti-pattern → direct relative import

- Fixed \_\_version\_\_ mismatch between \_\_init\_\_.py and PyPI release

## ▍ License

**Business Source License 1.1** — free for research, academic, and non-commercial use.

Commercial use requires written permission from the author.

Contact: [[e-mail]](mailto:[e-mail])

---

_{Dense Evolution · Salvatore Pennacchio · 2026}

< _{© 2026 Salvatore Pennacchio — Dense Evolution}