Tatopenn/dense-Evolution

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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.

A Streamlit dashboard (app_dashboard.py) provides live telemetry across 8 panels per simulation run — Quantum Simulator and Vector Healing tabs, run locally with streamlit run app_dashboard.py. legacy/dash.py is the original Google Colab notebook this was ported from, kept for reference only (not installable — see the file header).

---

▍ Install

pip install dense-evolution  # JAX is a core dependency, installed by default

# full stack: GPU · dashboard · Qiskit/PennyLane interop
pip install dense-evolution[full]

# just the interop bridge
pip install dense-evolution[qiskit]
pip install dense-evolution[pennylane]

# 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 / 3.0 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.to_tuples())

probs = sim.get_probabilities()
sv    = sim.get_statevector()

Dashboard (local, Streamlit):

pip install "dense-evolution[dashboard]"  # JAX already included by default
streamlit run app_dashboard.py

Anti-OOM for large circuits:

from dense_evolution import Chunk

sim = Chunk(27)                                    # logical 27 qubits
circuit_ops = [['h', i] for i in range(27)]
sim.run_chunk(circuit_ops, chunk_size_gates=500)   # SafeMemoryGuard active

---

▍ Architecture

dense_evolution/
├── registry.py     hardware detection · JAX/CuPy/NumPy flags · NoiseModel (Kraus channels)
├── gates.py        GATES{} · PARAMETRIC_GATES{} · GATE_IDS{}
├── healing.py      predictive state engine · Phi_AB · vettore dinamico · MemoryReflectionEngine
├── parser.py       QASMParser · QASMCircuit · OpenQASM 2.0 / 3.0
├── compiler.py     QuantumTranspiler · _apply_gate_fast_step (jit) · gate decomposition
├── chunk.py        SafeMemoryGuard · MemoryChunker · CircuitChunker · Chunk (Anti-OOM)
└── simulator.py    DenseSVSimulator · run_parametric_batch_jit · vmap batch VQE

ia_utils/
└── vector_healing.py   median_healing · enhanced_dense_healing_hybrid (NaN/Inf-safe, lazy JAX import)

app_dashboard.py + dashboard_core.py + ui_pages/   Streamlit dashboard — VQE engine · QM/MM · MD simulation · 3D wavefunction
legacy/dash.py                                     original Colab notebook, reference only (not installed as a module)

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
Parametric Batch JIT	run_parametric_batch_jit() evaluates full parameter grids in one jax.vmap + jax.jit call
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 · positional parameter injection into any OpenQASM 2.0 circuit
Anti-OOM Engine	SafeMemoryGuard blocks execution before JAX raises RESOURCE_EXHAUSTED
Predictive Healing	healing.py — Φ_AB alignment, dynamic vector, Σ-sync, MemoryReflectionEngine
Vector Sequence Healing	ia_utils/ — median_healing, enhanced_dense_healing_hybrid — NaN/Inf-safe, lazy JAX import
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

---

▍ Scientific Validation & Applications

To demonstrate the numerical accuracy and stability of Dense Evolution, the simulator was stress-tested across 3,500 continuous spatial sampling points to compute the Silicon Dimer (Si2) Dissociation Curve via Variational Quantum Eigensolver (VQE).

• Physical Accuracy: The simulation successfully maps the exact Born-Oppenheimer Potential Energy Curve (PEC), capturing the deep quantum ground state bound minimum at ~3.55 Å with negative total energy, before converging asymptotically toward full molecular dissociation.
• Numerical Precision: Calculations are locked at Double Precision (float64), proving the simulator's resilience against cumulative machine epsilon errors (~ 1.11 × 10⁻¹⁶) across thousands of sequential circuit executions.
• Run this molecular experiment instantly on Google Colab Free Tier: Open Notebook on Google Colab

---

============================================================
🔬 MOLECULAR VQE: EXACT POTENTIAL ENERGY CURVE (PEC)
============================================================
Distanza R: 1.200 Å | Energia Totale Molecola: +155.761158 eV
Distanza R: 1.671 Å | Energia Totale Molecola: +34.372692 eV
Distanza R: 2.142 Å | Energia Totale Molecola: +6.583098 eV
Distanza R: 2.614 Å | Energia Totale Molecola: +0.727422 eV
Distanza R: 3.085 Å | Energia Totale Molecola: -0.253226 eV
Distanza R: 3.557 Å | Energia Totale Molecola: -0.273498 eV
Distanza R: 4.028 Å | Energia Totale Molecola: -0.170948 eV
Distanza R: 4.500 Å | Energia Totale Molecola: -0.093048 eV

Variational Quantum Chemistry Plot

Below is the physical validation plot showing the Born-Oppenheimer potential energy curve:

👉 For the full suite of physical benchmarks, including the Transverse Field Ising Model (TFIM) and Phase Transition mappings, visit the main Dense-Evolution-Ising-Tests repository. You can also view the raw script for this specific molecular run here.

---

▍ API Reference

DenseSVSimulator

sim = DenseSVSimulator(
    n_qubits   : int,
    use_gpu    : bool = False,
    use_float32: bool = False,
)

Method	Description
set_initial_state(state=None)	Reset to |0⟩ⁿ or inject custom statevector
run_circuit(circuit, transpile=True)	Plain (non-JIT) gate execution — takes the tuple format below
run_circuit_jit_beast_mode(circuit)	JIT-compiled gate execution — primary execution path
run_circuit_with_chunking(circuit, chunk_size=500)	Chunked execution for long circuits
run_parametric_batch_jit(base_circuit, parameter_batch)	vmap over parameter grid — returns full batch of statevectors
get_probabilities() → np.ndarray	|ψ_i|² for all basis states
get_statevector() → np.ndarray	Full complex statevector
measure(qubit_idx) → int	Projective measurement with state collapse
memory_mb() → float	Current RAM usage in MB
apply_gate_1q(gate, qubit)	Apply arbitrary 2×2 unitary
apply_gate_2q(gate, q1, q2)	Apply arbitrary 4×4 unitary

QASMParser

parser  = QASMParser()
circuit = parser.parse(qasm_str)   # → QASMCircuit
valid, msg = parser.validate(circuit)

QASMCircuit fields: n_qubits, n_cbits, ops (list of gate dicts, e.g. {'name': 'h', 'qubits': [0], 'params': []}). Use circuit.to_tuples() to convert ops to the (name, qubit0[, qubit1, ...][, param0, ...]) tuple format that run_circuit / run_circuit_jit_beast_mode expect — don't build that format by hand.

NoiseModel

noise = NoiseModel()
noise.apply_to_sv(sv, n=4, model='depolarizing', p=0.01, rng=rng)
desc  = NoiseModel.kraus_description('amplitude_damping')

End-to-end: parse → run → apply noise

parser  = QASMParser()
circuit = parser.parse(qasm_str)                  # → QASMCircuit
sim     = DenseSVSimulator(n_qubits=circuit.n_qubits)
sim.run_circuit(circuit.to_tuples())               # dicts -> tuples, then execute

sv_noisy = NoiseModel().apply_to_sv(
    sim.get_statevector(), n=circuit.n_qubits, model='depolarizing', p=0.01,
    rng=np.random.default_rng(42),
)

Chunk (Anti-OOM)

sim = Chunk(
    n_qubits         : int,
    chunk_size_gates : int   = 500,
    memory_threshold : float = 0.15,   # block below 15% free RAM
    use_gpu          : bool  = False,
    use_float32      : bool  = False,
)
sim.run_chunk(circuit, chunk_size_gates=500)

Backward-compatibility aliases: chunk1 = MemoryChunker, chunk2 = Chunk, Chunk2Incrociato = Chunk.

ia_utils.vector_healing

from ia_utils.vector_healing import median_healing, enhanced_dense_healing_hybrid

healed, radius   = median_healing(vettori, radius_baseline=None)
healed, metadata = enhanced_dense_healing_hybrid(vettori, radius_baseline=None)

See IA Utils — Vector Sequence Healing above for details.

---

▍ Gate Library

Fixed gates (no parameters):

Gate	Symbol	Gate	Symbol
h	Hadamard	x	Pauli-X
y	Pauli-Y	z	Pauli-Z
s	S gate	sdg	S† gate
t	T gate	tdg	T† gate
sx	√X gate	id	Identity
cx	CNOT	cz	CZ
cy	CY	swap	SWAP
iswap	iSWAP	ecr	ECR
ccx	Toffoli

Parametric gates:

Gate	Parameters	Description
rx(θ)	θ	X-rotation
ry(θ)	θ	Y-rotation
rz(θ)	θ	Z-rotation
p(λ)	λ	Phase gate
u1(λ)	λ	U1 (≡ p)
u2(φ, λ)	φ, λ	U2 rotation
u3(θ, φ, λ)	θ, φ, λ	Generic single-qubit
cp(λ, ctrl, tgt)	λ	Controlled-Phase
crz(λ, ctrl, tgt)	λ	Controlled-RZ

---

▍ Interop — Qiskit / PennyLane

Run a circuit you already wrote in Qiskit or PennyLane on Dense-Evolution's simulator, no manual gate-by-gate rewrite. Both bridges go through OpenQASM 2.0 (qiskit.qasm2.dumps / qml.to_openqasm) and the existing QASMParser — not a bespoke translator, so gate coverage matches whatever the parser/simulator already support (see Gate Library above).

from qiskit import QuantumCircuit
from dense_evolution import run_qiskit_circuit

qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)

sim, probs = run_qiskit_circuit(qc)   # probs already in Qiskit's own bit order

import pennylane as qml
from dense_evolution import run_pennylane_circuit

dev = qml.device("default.qubit", wires=2)

@qml.qnode(dev)
def circuit():
    qml.Hadamard(wires=0)
    qml.CNOT(wires=[0, 1])
    return qml.probs(wires=[0, 1])

sim, probs = run_pennylane_circuit(circuit)   # no reordering needed, see below

from_qiskit(circuit) / from_pennylane(circuit) return a QASMCircuit (structural conversion only) for anyone who wants to manage their own DenseSVSimulator/Chunk instead of the convenience runners above.

Bit-order — read this before comparing arrays across frameworks. Qiskit indexes probability/statevector arrays little-endian (qubit 0 = least-significant bit); Dense-Evolution indexes MSB-first everywhere (phys = n_qubits - 1 - qubit, the same convention apply_gate_1q/apply_gate_2q/measure/beast-mode use). run_qiskit_circuit reorders its output into Qiskit's own convention so it's directly comparable to Statevector(circuit).probabilities(). PennyLane's own wire convention already matches Dense-Evolution's MSB-first indexing natively — run_pennylane_circuit does not reorder, on purpose; verified directly on an asymmetric circuit that the two frameworks genuinely need different treatment here, not just "symmetric for simplicity."

Known limits (inherited from the QASM2 bridge, not something this layer works around):

• No classical control flow — if/while and mid-circuit-measurement-conditioned gates are parsed out, not executed (same limitation as native QASM3 circuits, see Changelog v8.1.13).
• No expansion of composite/custom gates. A Qiskit call like mcx with 3+ controls gets exported as a named gate mcx { ... } definition; the definition is parsed cleanly (no longer corrupts what follows it) but the gate itself isn't a primitive Dense-Evolution knows how to execute, so a call to it is a silent no-op — same as referencing any unrecognized gate name elsewhere in this simulator. Stick to the gates in the Gate Library table above for results you can trust.
• Only a plugin/backend-free bridge — no qiskit.providers.BackendV2 or PennyLane Device registration, so you still call run_qiskit_circuit/run_pennylane_circuit explicitly rather than pointing existing framework code at a new backend/device string.
• run_qiskit_circuit/run_pennylane_circuit themselves are not differentiable. from_pennylane/from_qiskit materialize every gate parameter into a plain Python float inside the QASM text before parsing — the value leaves the JAX trace entirely. jax.grad through run_pennylane_circuit does not raise: it silently returns 0.0, which looks like "already converged" rather than "not wired up." Verified directly, not just documented from a guess. For a real gradient, use circuit_to_energy_fn instead (see next section) — pass it the QASMCircuit that from_qiskit/from_pennylane returns, before that float-baking happens, and jax.grad works correctly (also verified directly, on a PennyLane circuit imported this exact way).

---

▍ Differentiable Circuits — circuit_to_energy_fn

The real VQE gradient engine (jax.value_and_grad through a jax.lax.scan-based circuit template, verified against finite differences to ~1e-11) used to live only inside dashboard_core.py, unreachable from outside the Streamlit app. It's now public, dependency-light (needs only the base dense-evolution install, no dashboard/pandas/streamlit), and composes directly with the interop bridge above:

import jax
from dense_evolution import QASMParser, circuit_to_energy_fn

circ = QASMParser().parse("OPENQASM 2.0; include \"qelib1.inc\"; qreg q[1]; rx(0.5) q[0];")
energy_fn, n_params = circuit_to_energy_fn(circ, circ.n_qubits)

h_matrix = ...  # your Hamiltonian, shape (2**n_qubits, 2**n_qubits)
theta = jax.numpy.zeros(n_params)

(energy, statevector), grad = jax.value_and_grad(energy_fn, argnums=0, has_aux=True)(theta, h_matrix)

energy_fn(theta, h_matrix, stato_zero=None) -> (energy, statevector) is a pure JAX function differentiable in theta; stato_zero defaults to |0...0⟩. circuit can come from QASMParser.parse, from_qiskit, or from_pennylane interchangeably — this is what closes the interop bridge's non-differentiability gap noted above.

---

▍ Noise Models

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

Model	Kraus operators	Physical process
ideal	I	Noiseless
depolarizing	{√(1−p)I, √(p/3)X, √(p/3)Y, √(p/3)Z}	Isotropic Pauli error
amplitude_damping	{K₀=diag(1,√(1−γ)), K₁=[[0,√γ],[0,0]]}	T₁ energy relaxation
phase_damping	{K₀, K₁}	T₂ dephasing
bitflip	{√(1−p)I, √p·X}	Bit flip σₓ
combined	depolarizing(p/2) ∘ amplitude_damping(p/3)	Worst-case NISQ

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

---

▍ Mitigation & Predictive Healing

Active error tracking and stabilization integrated natively into the simulation runtime via healing.py.

Model	Operators	Description
dephasing_tracking	Δ_pre_emp ∘ Σ	Predictive deviation vs ideal eigenstate
phi_ab_alignment	Φ_AB(state_A, state_B, ipg)	Semantic + coherence alignment between two quantum states
vettore_dinamico	V_din = K · log(E_B/E_A) · Φ_AB	Log-differential energetic evolution vector

All core functions compiled via @jax.jit. Event history managed by MemoryReflectionEngine with JAX Zero-Drift spectral aggregation.

---

▍ IA Utils — Vector Sequence Healing

ia_utils/vector_healing.py — standalone module for cleaning sequences of vectors (e.g. hidden states / embeddings) that may contain NaN or Inf entries. Both functions preprocess the input (Inf → NaN → column-mean imputation) before healing, so corrupted values never propagate into the output.

Function	Approach	Returns
median_healing(vettori, radius_baseline=None)	scipy.ndimage.median_filter, dynamic radius min(20, max(3, n // 3))	(healed: np.ndarray, radius: int)
enhanced_dense_healing_hybrid(vettori, radius_baseline=None)	Blends the dense_evolution.healing Φ-trigger logic with a median fallback, decided per-step	(healed: np.ndarray, metadata: dict)

enhanced_dense_healing_hybrid metadata:

Key	Type	Description
fallback_triggered	bool	True only if the input contained genuine NaN/Inf corruption AND the median fallback fired to correct it -- does not reflect Phi-Trigger corrections on structurally noisy-but-valid data
adaptive_radius_used	int	Baseline radius actually applied
reconstruction_error	float	Mean norm of the correction applied vs. the sanitized input

import numpy as np
from ia_utils.vector_healing import median_healing, enhanced_dense_healing_hybrid

vettori = np.random.default_rng(0).normal(size=(50, 128))
vettori[10, 3] = np.nan          # simulate a corrupted hidden state
vettori[30, 7] = np.inf

healed, radius = median_healing(vettori)

healed_hybrid, meta = enhanced_dense_healing_hybrid(vettori)
print(meta)
# {'fallback_triggered': True, 'adaptive_radius_used': 16, 'reconstruction_error': 11.48}

jax is imported lazily inside enhanced_dense_healing_hybrid (a leftover from when JAX was optional) — harmless now that dense-evolution always installs JAX as a core dependency, but it does mean median_healing and the module import itself never actually needed it in the first place.

---

▍ Anti-OOM Chunk Engine

All operations parcellized dynamically using a 4-layer architectural shield.

Layer	Class	Role
1	SafeMemoryGuard	Pre-allocation RAM check — blocks before JAX raises RESOURCE_EXHAUSTED
2	MemoryChunker	Geometry calculator — computes num_chunks, chunk_dim, chunk_size_bits from available RAM without any JAX allocation
3	CircuitChunker	Per-slice execution — SafeMemoryGuard fires before every gate-slice dispatch
4	Chunk	Top-level wrapper — logical n_qubits decoupled from physical allocation at safe_qubits

Benchmark 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	✅	307 MB	✅	516 MB	1× (2²⁷)
26	67,108,864	✅	1,074 MB	✅	2,050 MB	1× (2²⁷)
28	268,435,456	❌ OOM	—	✅	2,050 MB	2× (2²⁷)
30	1,073,741,824	❌ OOM	—	✅	2,048 MB	8× (2²⁷)
32	4,294,967,296	❌ OOM	—	✅	2,048 MB	32× (2²⁷)

from dense_evolution import Chunk

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

print(sim)
# Chunk(n_qubits=27, safe_qubits=27, num_chunks=1,
#       chunk_size_bits=27, mem_per_chunk=2048.0 MB, ram_free=42.3%, has_jax=True)

---

▍ Benchmarks

Measured on Google Colab Free Tier (CPU runtime)

Metric	Value
Numerical drift (30-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 latency reduction (static JIT cache)	−86.47%

---

▍ 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 ×3 · R7 Pearson heatmap
Fisica Stato	Bloch projection · Schmidt rank · coherence vector
Mosaico	2D probability density map up to 1008 qubits
VQE Results	6-subplot: 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

---

▍ VQE Engine

Built on circuit_to_energy_fn (see previous section) — no separate mechanism. Parameter injection:

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, via a -1.0 sentinel in the compiled op template patched in with jnp.where inside a jax.lax.scan — never a Python float() call, which would sever the JAX trace and make the gradient below fake.

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

Gradient & update rule:

$$\frac{\mathrm{\partial }E}{\mathrm{\partial }{\theta }_i}=⟨\psi (\theta )\mid \frac{\mathrm{\partial }H}{\mathrm{\partial }{\theta }_i}\mid \psi (\theta )⟩\theta \leftarrow \theta -\frac{\alpha {\widehat{m}}_t}{\sqrt{{\widehat{v}}_t}+\epsilon }\backslash frac\{\backslash partial\; E\}\{\backslash partial\; \backslash theta\_i\}\; =\; \backslash left\backslash langle\backslash psi(\backslash theta)\backslash left|\backslash frac\{\backslash partial\; H\}\{\backslash partial\; \backslash theta\_i\}\backslash right|\backslash psi(\backslash theta)\backslash right\backslash rangle\; \backslash qquad\; \backslash theta\; \backslash leftarrow\; \backslash theta\; -\; \backslash frac\{\backslash alpha\backslash ,\backslash hat\{m\}\_t\}\{\backslash sqrt\{\backslash hat\{v\}\_t\}+\backslash varepsilon\}$$

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.

---

▍ Circuit Library (30+ presets)

All circuits 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

---

▍ Changelog

v8.1.25

• Fixed: MPSSimulator._svd_truncate used to silently violate jsd_budget whenever max_bond was too small for the circuit's real entanglement -- the while jsd_val > self.jsd_budget and chi_new < max_possible loop exits with no signal once chi_new hits max_bond, even if jsd_val is still far above budget. summary()'s avg_JSD only reports the mean of the per-step local errors, not the accumulated global error, so it can read deceptively low while the final contracted state is badly wrong -- verified directly on an 8-qubit/15-layer entangling circuit with max_bond=2: TVD ~0.97 against DenseSVSimulator (a near-total mismatch) while avg_JSD read a reassuring-looking 0.0534. Added a budget_violations counter (incremented every time this happens, exposed in summary()) and a UserWarning on the first violation ("bond dimension capped at max_bond=..., jsd_budget=... not honored ... results may be unreliable") -- not an exception, so existing code that tolerates the tradeoff keeps working, but now with an explicit, checkable signal instead of a silently-optimistic average.

v8.1.24

• Fixed: dense_evolution.healing.calculate_phi_ab raised ValueError: Clip received a complex value when called with complex statevectors (e.g. sim.get_statevector()) -- jnp.dot(semantic_change, ipg_vector) is the bilinear (non-conjugated) product on complex arrays and stays complex, which then hit jnp.clip at the end of the function. Fixed by using jnp.real(jnp.vdot(...)) -- the correct Hermitian-inner-product real part, identical to jnp.dot for the real-valued inputs every existing caller already uses (ia_utils.vector_healing.enhanced_dense_healing_hybrid), and now also correct for genuinely complex input. Verified against a manual NumPy Re(vdot(...)) calculation on a case with nonzero imaginary amplitudes (H+S gates), not just the crash repro (H+CX alone never produces a nonzero imaginary part, so it only proved "doesn't crash," not "computes the right number").
• Fixed: ia_utils.vector_healing.median_healing/enhanced_dense_healing_hybrid emitted RuntimeWarning: Mean of empty slice whenever an input column was entirely NaN -- np.nanmean on an all-NaN slice returns NaN with a warning (silently caught and zeroed by the very next line, so the output was already correct, only the warning was noise). Fixed by pre-replacing whole all-NaN columns with 0.0 before calling nanmean, so it never sees an empty slice -- verified byte-identical output, zero warnings, in both functions (the preprocessing block was duplicated verbatim in each).
• Fixed: enhanced_dense_healing_hybrid's fallback_triggered metadata flag used to reflect only the internal Phi-Trigger heuristic classifying a step as "static", regardless of whether the input actually contained any NaN/Inf -- verified directly that a clean, uncorrupted random Gaussian array (no corruption at all) still came back fallback_triggered=True. Root cause: the heuristic is tuned for trajectories with real underlying dynamics (verified separately against a real noisy VQE run, where it correctly stayed quiet at low noise and fired at high noise) and mistakes pure structureless IID noise -- which has no coherent trend to recognize as "genuine change" -- for anomalous static behavior on nearly every step. fallback_triggered is now gated on the original input actually containing NaN/Inf (checked before sanitization) in addition to the fallback having fired -- the dashboard's "Fallback scattato" indicator will now only light up for genuine NaN/Inf corruption, not general noise-driven Phi-Trigger corrections (those corrections still happen internally, they're just no longer mislabeled as a NaN/Inf fallback).
• Docs: removed two README rows (kappa_stabilization, richardson_integration) describing functions that never existed in dense_evolution/healing.py (confirmed against the full 140-line file -- 7 functions plus MemoryReflectionEngine, neither name present anywhere), and corrected two code examples that still showed a median_fallback_threshold parameter on enhanced_dense_healing_hybrid that was removed from the actual function signature back when its corresponding UI slider was dropped (commit 69fc8a4) without updating the docs to match.

v8.1.23

• Added: MPSSimulator (dense_evolution/mps.py, re-exported from the package root) -- a JAX-backed Matrix Product State simulator with adaptive SVD-truncated bond dimension. Verified exact against DenseSVSimulator on entangling circuits (TVD=0). Selectable as a dashboard engine (Quantum Simulator page) for circuits up to 24 qubits; for larger low-entanglement circuits, get_probabilities_sampled/get_top_k_probable_states scale to hundreds of qubits without ever materializing a (2**n,) statevector.
• Fixed: large-qubit circuits submitted through the dashboard used to crash the whole Streamlit process (uncatchable OS-level OOM) instead of failing cleanly. They now route automatically through the existing Chunk anti-OOM wrapper, which raises a catchable, informative MemoryPressureError instead.
• Changed (breaking): JAX is now a required core dependency (dependencies, not optional-dependencies) -- pip install dense-evolution installs it by default, no [jax] extra needed anymore. Every simulator backend in this package already required JAX in practice; the previous try/except ImportError numpy-fallback path was never a real, maintained alternative and is no longer reachable (the numpy code itself is left in place for reference, just dead). If you were pinning an environment without JAX and relying on degraded-numpy behavior, this release will break that -- install dense-evolution<8.1.23 to keep the old behavior.

v8.1.22

• Added: ui_pages/quantum_scars.py — a new "Quantum Scars" dashboard page (app_dashboard.py's st.navigation), an interactive live demo of the PXP quantum many-body scar model (Rydberg blockade): builds H_PXP via sparse Pauli operators, exact-diagonalizes it (scipy.linalg.eigh, cached via st.cache_resource keyed on qubit count — the first use of that decorator in this codebase, since diagonalizing a dense 2**n_qubits matrix is genuinely expensive and depends only on that one slider), propagates a Néel initial state under real-time Hamiltonian evolution, injects real noise via NoiseModel.apply_to_sv, and lets you compare fidelity revival with no protection, a cheap constraint-subspace projection (no extra diagonalization needed — a combinatorial mask of which computational-basis bitstrings have no two adjacent 1-bits), or an idealized exact-eigenstate "tower" projection. Distills the investigation already published at quantum_scar_investigation — which found no genuine scar in Dense Evolution's own frustrated Ising grids (wrong observable + gauge equivalence), then validated the same verification pipeline against PXP, where scars are real and well documented — into something runnable instead of only readable. Verified via streamlit.testing.v1.AppTest (real button-click simulation, zero exceptions) and a new test_quantum_scars.py (17 tests, all passing) unit-testing the numerical core directly: validity-mask combinatorics, H_PXP Hermiticity and Hilbert dimension, fidelity=1/norm-preservation under propagation, and both projections staying normalized with zero weight outside their target subspace.

v8.1.21

• Added: QASMCircuit.__iter__ — duck-types a parsed circuit as an iterable of the same tuples .to_tuples() returns, so it works anywhere a plain circuit list is expected (Chunk.run_chunk, QuantumTranspiler.transpile, ...) without remembering to call .to_tuples() first. Found via a user's own Colab testing: Chunk.run_chunk(QASMParser().parse(qasm)) — a very natural thing to try — raised TypeError: 'QASMCircuit' object is not iterable.
• Fixed / Performance: Chunk's multi-chunk dispatch (num_chunks > 1) used to apply every gate through a Python loop calling non-JIT apply_gate_1q/apply_gate_2q — measured 6x slower than run_circuit_jit_beast_mode on an identical workload. Replaced with a single jax.lax.scan over the whole circuit operating directly on the stacked (num_chunks, chunk_dim) representation — never materializing a (2**n_qubits,) array, preserving the anti-OOM property Chunk exists for. The 6 gate/qubit-location cases were ported formula-for-formula from the old Python-loop implementation and verified case-by-case against it (all 18 pre-existing TestChunkMultiPiece tests, which cross-check against DenseSVSimulator, pass unchanged against the new kernel) before the old code was removed. Gate coverage is now built via GATE_IDS instead of the old GATES/PARAMETRIC_GATES lookup, aligning it with beast-mode's own coverage. Measured speedup on the exact benchmark that surfaced the problem (10 qubits/200 gates/4 forced chunks): 2.2s → 0.42s, now close to beast-mode's 0.37s on the same non-chunked workload.
• Note: a Colab report of "17s vs milliseconds" that prompted this investigation turned out, on reproduction, to be num_chunks==1 (not the multi-chunk path at all) — 0.49s locally on the identical circuit, most likely first-time JIT compilation overhead on Colab's specific hardware rather than a code defect. The 6x slowdown that was real and is fixed here was found and confirmed with a separate synthetic benchmark (num_chunks forced via monkeypatch), not the original report.

v8.1.20

• Fixed: from_pennylane/run_pennylane_circuit silently renumbered qubits whenever wires weren't touched in ascending order — both PennyLane's qml.to_openqasm and the older tape.to_openqasm() number exported QASM qubits by first-touch order, not by actual wire index (e.g. PauliX(wires=2) then CNOT(wires=[2,1]) exported as x q[0]; cx q[0],q[1];, silently mapping wire 2→q[0] and wire 1→q[1]). Found via independent fuzz testing (20 random circuits touching 4 wires in random order: 9/20 matched PennyLane's own results before the fix, 20/20 after). Fixed by passing an explicit wires= argument — the device's declared wire order for a QNode, the tape's own wires sorted ascending for a bare tape — forcing the true wire order into the export instead of relying on touch order.
• Fixed: NoiseModel.apply_to_sv's depolarizing channel (and the combined channel's depolarizing sub-channel) picked which Pauli error (X/Y/Z) to apply using thresholds p/3 and 2p/3 compared against a draw uniform on the full [0,1) range — but that draw should only ever decide which Pauli fires, independent of the overall fire-rate p, so the thresholds needed to be the fixed values 1/3 and 2/3 instead. The bug skewed every depolarizing/combined-noise circuit heavily toward Z regardless of p (verified: at p=0.3, P(X|fire)=P(Y|fire)=10%, P(Z|fire)=80% instead of the documented 33.3% each — confirmed both via an isolated 100k-sample trace of the raw branch logic and via full statevector simulation, both matching the bug's predicted skew to within statistical noise). Found via independent statistical fuzz testing comparing measured frequencies against the analytic prediction — a test that only checks "the channel runs without crashing" would never have caught this. bitflip, phaseflip, and amplitude_damping were verified unaffected (correct by construction, don't use this three-way branch).
• Docs: opened a tracking issue for a related robustness gap found during the same fuzzing pass — unrecognized gate names (a typo like 'crx' instead of 'crz', or any gate not in GATE_IDS) are silently dropped everywhere in the simulator instead of raising, same pattern already documented for the Qiskit interop bridge's unsupported custom gates. Not fixed here — would be a breaking-change decision for run_circuit/run_circuit_jit_beast_mode/run_parametric_batch_jit's public behavior, tracked separately.

v8.1.19 — Security fix

• Fixed (security): QASMParser's gate-parameter evaluator (_eval_param, used for expressions like rx(...), p(...)) called eval() with {'__builtins__': {}} as its only protection. That blocks direct builtin names (open, len, __import__, ...) but does not block attribute/dunder traversal of the live Python object graph (().__class__.__bases__[0].__subclasses__()...), which needs no builtin name at all — from there, any class loaded in the process is reachable, including ones whose __globals__ reference os/subprocess. Verified directly: a crafted gate-parameter expression, passed through the public QASMParser.parse() entry point (the primary entry point of the whole library — used by the dashboard, the Qiskit/PennyLane interop bridge, and any direct usage), executed successfully. Anyone parsing untrusted QASM text was affected, in every previously published version. Fixed by replacing eval() with an AST node-type whitelist evaluator (_eval_ast_node) — only literals, +-*/%** arithmetic, and calls/lookups restricted to the documented math environment (pi, sin, sqrt, ...) are ever evaluated; an ast.Attribute node (produced by any . in the expression) is never one of the handled cases, so attribute-based escapes are structurally impossible rather than blocklisted. _resolve_int_expr (QASM3 for-loop bounds) used the same eval() pattern but was already protected by a pre-filter regex rejecting any non-arithmetic character — verified safe before this fix — now shares the same AST evaluator for consistency, so no raw eval()/exec() remains anywhere in the codebase (confirmed via full-repo search). No public API or behavior change for legitimate expressions — every previously-supported parameter syntax (pi, pi/2, sqrt(2), cos(0.3), etc.) evaluates identically.

If you parse QASM text from any source you don't fully trust, upgrade immediately.

v8.1.18

• Fixed: removed a global warnings.filterwarnings('ignore') from registry.py, run unconditionally on import dense_evolution. It silenced every Python warning process-wide for the importing user's whole session — not just this package's, but their own code's and every other library's too. Inherited unchanged from the original Colab notebook (added in v8.0.6, never reconsidered once this became a real pip package). Concretely masked real signal: the JAX float64→float32 truncation UserWarnings visible throughout this project's own test output (precision silently lost under use_float32=True) would have been invisible to anyone using the package normally.

v8.1.17

• Added: donate_argnums=(0,) on run_circuit_jit_beast_mode's statevector buffer — the only one of _compile_and_run_circuit_jit's four call sites where it's safe (self.sv is always rebound immediately after, verified across chunked/repeated calls and separate simulator instances). run_parametric_batch_jit (its init_sv is a vmap-broadcast closure shared across the whole batch) and circuit_to_energy_fn's VQE loop (same stato_zero reused every epoch) are deliberately left un-donated — donating there would make JAX raise on the second use instead of helping. Verified with a real measurement, not just a claim: RSS growth on a 22-qubit/300-gate circuit drops from +89.4MB to +4.5MB.

v8.1.16

• Note: v8.1.15's published PyPI package does not contain the from_pennylane Python 3.10 fix described below, despite the changelog entry — the fix landed in the repo before the PyPI upload, but the actual pip install-able wheel/sdist for 8.1.15 was built and uploaded from an earlier commit. PyPI doesn't allow re-uploading files under an already-published version, so this release exists specifically to ship that fix as an installable package. If you're on 8.1.15, upgrade to 8.1.16 — don't rely on 8.1.15's changelog matching what you actually have installed.

v8.1.15

• Added: dense_evolution.autodiff.circuit_to_energy_fn(circuit, n_qubits) — the real VQE gradient engine (jax.value_and_grad through a jax.lax.scan circuit template, verified against finite differences to ~1e-11) is now public API, independent of dashboard_core.py/Streamlit. Takes a QASMCircuit — the same type from_qiskit/from_pennylane return — so it closes the non-differentiability gap documented in v8.1.14: circuit_to_energy_fn(from_pennylane(qnode, ...), n_qubits) now gives a real, non-zero jax.grad, verified directly, where run_pennylane_circuit alone silently returned 0.0.
• Changed: dashboard_core.py's _build_vqe_template/_vqe_energy_fn removed — _run_vqe_telemetry_body now calls the same public circuit_to_energy_fn, one engine instead of two copies of the same math that could silently drift apart. Verified behaviorally identical: all existing dashboard VQE tests pass unchanged, same tolerances.
• Fixed: found while testing the newly-public API — calling the engine on a circuit with zero parametric gates crashed on empty-array indexing during JAX tracing. Previously unreachable because dashboard_core.py always special-cased n_params == 0 before calling in; a real gap once this became public API someone could call directly. Fixed with a static (non-traced) branch.
• Docs: the README's VQE Engine section had drifted stale, still describing the deleted risolvi_qasm() mechanism from before the real-gradient rewrite — corrected, and a new "Differentiable Circuits" section documents circuit_to_energy_fn with a verified end-to-end example.
• Fixed: from_pennylane broke on Python 3.10 — CI caught it (3.10 job red, 3.11/3.12 green). Newer PennyLane releases dropped Python 3.10 support, so pip resolves an older PennyLane (0.42.3) there instead of the version this bridge was built against (0.45.1); qml.to_openqasm(tape) behaves incompatibly between the two for a bare tape/QuantumScript input (crashes with AttributeError: 'QuantumTape' object has no attribute 'func' on the older one). Verified against both versions directly (installed 0.42.3 in an isolated venv to reproduce). from_pennylane now picks whichever serialization path the installed PennyLane version actually supports instead of assuming the newer one unconditionally.

v8.1.14

• Added: interop bridge for Qiskit and PennyLane — from_qiskit/from_pennylane convert an existing circuit to a QASMCircuit by reusing the existing QASMParser (via qiskit.qasm2.dumps / qml.to_openqasm) instead of a bespoke gate-by-gate translator; run_qiskit_circuit/run_pennylane_circuit execute it directly on DenseSVSimulator. Handles the bit-order mismatch explicitly instead of leaving it as a silent trap: Qiskit indexes arrays little-endian (qubit 0 = LSB), Dense-Evolution is MSB-first everywhere else in the codebase, so run_qiskit_circuit reorders its output to match Qiskit's own convention (verified against Statevector(...).probabilities() on an asymmetric circuit); PennyLane's own wire order already matches Dense-Evolution's natively (verified the same way), so run_pennylane_circuit does not reorder — kept as two separate code paths on purpose. New optional extras dense-evolution[qiskit] / dense-evolution[pennylane].
• Fixed: found while building the Qiskit bridge — qiskit.qasm2.dumps exports composite gates (e.g. mcx) as a gate NAME params { ... } definition on a single line, the same brace-delimited block corruption already fixed for QASM3 for/if/while/def in v8.1.13, just not covered because gate wasn't in that fix's keyword set (verified: before the fix, a 4-qubit circuit using mcx silently inflated to n_qubits=5 with a ghost op). Extended the same brace-matching preprocessor to also strip gate definitions cleanly.

v8.1.13

• Fixed: QASMParser declared OpenQASM 3.0 support but for/if/while/def blocks — brace-delimited, not ;-terminated — were mishandled by the naive split(';') statement splitter: a for-loop's body was never extracted, and its closing } merged into whatever real statement followed on the same line, corrupting it too (verified: for int i in [0:2] { h q[i]; } cx q[0],q[1]; produced a single ghost op named '}', with the loop body lost and the real cx silently dropped — executed circuit stayed |000⟩ at 100% probability, no error). Needed for writing VQE ansätze with a loop over qubits instead of one line per qubit. Added _process_block_constructs, run before the ;-split: for-loops with resolvable integer bounds (literals, or int/const int variables declared earlier in the source — QASM3's inclusive-end range semantics) are now genuinely unrolled by substituting the loop variable into the body per iteration; if/while/def blocks and for-loops with unresolvable bounds are cleanly stripped instead of corrupting the source that follows them.

v8.1.12

• Fixed: run_circuit_jit_beast_mode / run_parametric_batch_jit silently dropped cy, cp, crz, u1, p, sx — they weren't in GATE_IDS, so if name not in GATE_IDS: continue skipped them with no error (verified: h(0);h(1);crz(0,1,1.2) produced the exact same output as h(0);h(1) alone). dashboard_core.py already treats these as first-class gates, so any circuit using them — dashboard-built or hand-written QASM — silently ran the wrong physics through the fast path nearly everything uses. Added the missing GATE_IDS entries and the missing kernel implementations for cy/crz/sx in _apply_gate_fast_step — crz specifically needed its own kernel, not reuse of cp's (CP phases |11⟩ only; CRZ phases the target conditioned on its own bit, a different gate).
• Fixed: run_circuit_jit_beast_mode used the raw qubit index as bit position (LSB-first) instead of the documented MSB-first convention (phys = n_qubits - 1 - qubit) used by run_circuit()/apply_gate_1q()/apply_gate_2q()/measure() elsewhere in the simulator. Pre-existing, not introduced by the fix above — found while verifying it, masked until now because every beast-mode circuit tested to date happened to be symmetric under qubit reversal (Bell states, GHZ states, uniform superpositions), so the wrong labeling never showed up in the probabilities. Verified with X on qubit 0 in a 3-qubit register: gave index 1 (LSB) instead of index 4 (MSB, correct). do_1q/do_2q now compute physical bit positions consistently with the rest of the simulator; Chunk's num_chunks==1 (via beast mode) and num_chunks>1 (via apply_gate_1q/apply_gate_2q) paths are now finally consistent with each other too.
• Fixed: the VQE gradient (run_vqe_telemetry) was never a real derivative — grad_vqe_params[i] = 0.5*(energy-target)*sin(theta[i]) + gaussian_noise, no jax.grad, no parameter-shift rule, no backprop on θ anywhere in the codebase (the only real jax.value_and_grad usage, in QMMMForceEngine, differentiates classical QM/MM forces w.r.t. atomic positions, not circuit parameters). risolvi_qasm (the old circuit-building path) converted θ to a Python float before use, severing the JAX trace, so backprop couldn't pass through it. Replaced with a real jax.grad pipeline reusing run_parametric_batch_jit's own sentinel-injection pattern (θ substituted via jnp.where inside a jax.lax.scan, never a float() call) — verified against a finite-difference gradient (~1.5e-10 agreement) on a real circuit from QASM_LIBRARY, and confirmed genuine Adam-optimizer convergence (monotonic energy descent to a minimum) over 40 epochs, unlike the old noisy formula. Public signature and DataFrame columns of run_vqe_telemetry unchanged.

v8.1.11

• Fixed: dash.py (the original Colab notebook) was declared as an installable module (py-modules = ["dash"]) with the same name as the real Plotly dash package, itself listed as an optional dependency in the very same pyproject.toml — a genuine packaging collision, not just a local dev annoyance. It also had unconditional module-level from google.colab import files / import ipywidgets, so import dash crashed immediately outside Colab. Nothing in the maintained codebase (dashboard_core.py/app_dashboard.py, the real Streamlit port) imports it anymore. Moved to legacy/dash.py (reference only, not packaged), removed from py-modules. The dashboard extra now installs what the real dashboard actually needs (streamlit, pandas, seaborn, plotly) instead of the unused dash package.
• Docs: README's Quick Start (the very first example in the file) passed circuit.ops — raw dicts — to run_circuit_jit_beast_mode, which expects the tuple format from circuit.to_tuples(); crashed with KeyError: 0. Fixed, and the "Dashboard" quick-start snippet now points at streamlit run app_dashboard.py instead of the retired Colab-only import dash pattern.

v8.1.10

• Fixed: run_circuit_jit_beast_mode / run_parametric_batch_jit — a gate referencing a qubit index out of range silently corrupted the entire statevector to zero instead of raising (verified: get_probabilities().sum() went from 1.0 to 0.0, no exception). apply_gate_1q/apply_gate_2q already validated qubit indices, but these two JIT fast paths build their own compiled ops and never called them. Both now validate before dispatch, matching the existing behavior of the non-JIT path.
• Fixed: Chunk — for n_qubits beyond the RAM-safe budget (chunk_size_bits), it silently ran the circuit on a smaller inner simulator (min(n_qubits, chunk_size_bits)) instead of genuinely chunking: num_chunks/chunk_dim were computed but never used to combine multiple pieces. Found testing Chunk(n_qubits=28): get_probabilities() returned 2**27 elements, not 2**28. Now implements real multi-chunk simulation (RAM-only, no disk paging — covers moderate overflow beyond the safe budget, not arbitrarily large qubit counts): num_chunks independent chunk-sized simulators held in memory, with gate dispatch across chunk boundaries for all six local/chunk-select combinations. Verified against a plain DenseSVSimulator running the identical circuit (exact match, not just "looks right"). A sized RAM check now raises MemoryPressureError up front if the chunks wouldn't fit, instead of attempting and OOMing.

v8.1.9

• Fixed: ia_utils/vector_healing.py — enhanced_dense_healing_hybrid had an unreachable third branch (a dense/blend fallback): the underlying trigger signal from evaluate_phi_trigger is strictly binary (0.0/1.0), so the branch could never execute. Collapsed to the genuine 2-state logic (pass-through vs. median fallback); runtime output is unchanged since the branch never ran.
• Fixed: dashboard_core.py — run_simulation / run_vqe_telemetry mutated the process-wide JAX jax_enable_x64 flag without ever restoring it, so running one float32 simulation silently downgraded numerical precision for unrelated code later in the same process (e.g. the Vector Healing page, which sets no precision of its own). Both now save/restore the flag around their own execution.
• Docs: README's NoiseModel example called a nonexistent .apply() method with a wrong parameter name (n_qubits instead of n) — corrected to apply_to_sv(sv, n=..., ...). Documented QASMCircuit.to_tuples() and DenseSVSimulator.run_circuit, which already existed and work correctly but were never mentioned in the README.

v8.1.8

• Fixed: parser.py — controlled two-qubit gates (cx/cy/cz/cp/crz) parsed from QASM in the dashboard layer had control and target swapped relative to compiler.py's documented (gate, control, target) contract, breaking entanglement for circuits run through the dashboard. The core QASMCircuit.to_tuples() path was already correct.
• Fixed: parser.py — range syntax (q[0:3]) on single-qubit gates only applied to the first qubit in the range, silently dropping the rest. Now expands into one gate application per qubit, matching the parser's own documented contract.
• Fixed: from dense_evolution import Chunk raised ImportError — Chunk is now re-exported from the package root. Added get_probabilities()/get_statevector() to Chunk for parity with DenseSVSimulator.
• Removed: dense_evolution/test2.py and stress_test.py — byte-identical, assertion-free debug scripts that shipped inside every install with 0% test coverage. Their one real check (Kraus noise is genuinely stochastic across independent runs) is now a real regression test.

v8.1.7

• ia_utils/ — new package: median_healing, enhanced_dense_healing_hybrid for vector sequence healing (NaN/Inf-safe)
• jax import in ia_utils.vector_healing made lazy — importable without the [jax] extra
• Fixed reconstruction_error telemetry returning NaN when input contained NaN/Inf
• Added scipy to core dependencies (was used but undeclared)

v8.1.6

• Modular package structure (dense_evolution/ directory)
• Split registry.py, gates.py, healing.py, chunk.py into dedicated modules

v8.1.5

• chunk.py — SafeMemoryGuard: 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

v8.1.0

• healing.py — Predictive State Engine: calculate_phi_ab, calculate_vettore_dinamico, calculate_delta_preemp, evaluate_phi_trigger, calculate_jax_reflection — all @jax.jit
• MemoryReflectionEngine — event logging + JAX Zero-Drift spectral aggregation

v8.0.x

• run_parametric_batch_jit() — jax.vmap over full parameter grids in single XLA call
• run_circuit_jit_beast_mode() — static JIT compilation with QuantumTranspiler
• OpenQASM 2.0/3.0 dual-mode parser with paren-depth-aware expression splitting
• NoiseModel Kraus channels in registry.py

---

▍ 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 · ≤ 1,000 circuits/day · ≤ 10,000 shots/circuit
• Attribution required: © 2026 Salvatore Pennacchio <jtatopenn@libero.it> — Dense Evolution

Full text: LICENSE.md

---

_{© 2026 Salvatore Pennacchio — Dense Evolution}