Dense Evolution

Name: Dense Evolution
Author: tatopenn-cell

by tatopenn-cellversion 8.1.52i★ –iRuns on your computer

Registry nameio.github.tatopenn-cell/dense-evolution

Install

Overview

Description from the maker (original English)

Real quantum circuit simulator, VQE, molecular Hamiltonians, QM/MM forces, and MD via 21 tools.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: DENSE_EVOLUTION_KERNEL_URL

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "dense-evolution": {
      "command": "uvx",
      "args": [
        "dense-evolution",
        "mcp"
      ],
      "env": {
        "DENSE_EVOLUTION_KERNEL_URL": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add dense-evolution --env DENSE_EVOLUTION_KERNEL_URL=your-value -- uvx dense-evolution mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "dense-evolution": {
      "command": "uvx",
      "args": [
        "dense-evolution",
        "mcp"
      ],
      "env": {
        "DENSE_EVOLUTION_KERNEL_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "dense-evolution": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "dense-evolution",
        "mcp"
      ],
      "env": {
        "DENSE_EVOLUTION_KERNEL_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

DENSE_EVOLUTION_KERNEL_URL env
URL of the running Dense-Evolution Composer kernel. Start it first with 'dense-evolution serve' (requires the [composer] extra). Defaults to http://127.0.0.1:8800.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account tatopenn-cell, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32568 servers in this category
Published to the registry more recently than 22% of the other 32568 servers in this category
Basic checks equal to or better than 100% of the other 32568 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Dense Evolution safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (8.1.52) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, FlyDocs

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

A high-performance quantum simulation toolkit Statevector/MPS engines with compilation, noise, VQE, QEC, chemistry, and agent-native tooling.

Download the Dense Evolution Dashboard (Streamlit)

Windows (.bat) · macOS / Linux (.sh)

What It Is
Install
Quick Start
Key Features
Benchmarks
Composer & MCP Server
Key Resources

▍ What It Is

Run up to 28 qubits in about 3 seconds, without crashing. Dense Evolution JIT-compiles statevector circuits through JAX XLA, automatically chunks and — past even that RAM ceiling — spills to disk when memory fills up, so a real simulation stays alive instead of OOM-ing.

📖 [Full documentation, API reference, and worked examples →](https://tatopenn-cell.github.io/Dense-Evolution/)

A local Streamlit dashboard and web-based Composer editor are also included — see Composer & MCP Server below.

▍ 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]

native Hartree-Fock (basis-set data)

pip install dense-evolution[hf]

Composer's local kernel (see "Composer" below)

pip install dense-evolution[composer]

MCP server for the Composer kernel (see "MCP Server" below)

pip install dense-evolution[mcp]

development

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

⚠️ macOS + dense-evolution[qiskit] users

Qiskit's own QuantumCircuit.__init__ is known to segfault the whole process on macOS/arm64 (an upstream Qiskit bug, not something Dense-Evolution can fix from its side — see release v8.1.43 for the full reproduction). dense_evolution/interop.py now warns (RuntimeWarning, once per process) the first time you touch the Qiskit bridge on sys.platform == 'darwin', but it does not block — some Qiskit/macOS combinations may work fine. If you hit a crash, pip install dense-evolution[pennylane] gives the same circuit-interop functionality without constructing any Qiskit object.

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 -- single-qubit rotations, a barrier

(a real OpenQASM synchronization marker: parsed like hardware would, no

effect on the simulated state), then an entangling layer

qasm = """ OPENQASM 2.0; include "qelib1.inc"; qreg q[3]; rx(pi/3) q[0]; ry(pi/4) q[1]; h q[2]; barrier q; cx q[0], q[1]; cx q[1], q[2]; rz(pi/6) q[2]; """

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

sim = DenseSVSimulator(n_qubits=3) sim.run_circuit_jit(circuit.to_tuples())

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

probs = [0.3201 0.3201 0.0549 0.0549 0.0183 0.0183 0.1067 0.1067]

Noise:

import numpy as np

from dense_evolution import DenseSVSimulator, QASMParser, NoiseModel

qasm = 'OPENQASM 2.0; include "qelib1.inc"; qreg q[2]; h q[0]; cx q[0],q[1];' circuit = QASMParser().parse(qasm)

sim = DenseSVSimulator(n_qubits=2) sim.run_circuit_jit(circuit.to_tuples())

noisy_sv = NoiseModel.apply_to_sv(np.asarray(sim.sv), n=2, model='depolarizing', p=0.05, rng=np.random.default_rng(0)) np.abs(noisy_sv) ** 2

[0. 0.5 0.5 0. ]

VQE:

import jax

import jax.numpy as jnp from dense_evolution import QASMParser, circuit_to_energy_fn

qasm = 'OPENQASM 2.0; include "qelib1.inc"; qreg q[1]; ry(0.0) q[0];' circuit = QASMParser().parse(qasm) energy_fn, n_params = circuit_to_energy_fn(circuit, n_qubits=1) h = jnp.array([[1.0, 0.0], [0.0, -1.0]], dtype=jnp.complex128) # Pauli Z

theta = jnp.array([0.1]) grad_fn = jax.value_and_grad(energy_fn, argnums=0, has_aux=True) for _ in range(40): (energy, sv), grad = grad_fn(theta, h) theta = theta - 0.5 * grad

energy = -1.0, theta = [3.14159265]

Mitigation (ZNE):

import dense_evolution as de

e1, e2, e3 = 1.234, 0.876, 0.611 # values at 1x, 2x, 3x noise de.zero_noise_extrapolation([e1, e2, e3], [1.0, 2.0, 3.0])

1.622

Dashboard (local, Streamlit):

pip install "dense-evolution[dashboard]"  # JAX already included by default

streamlit run tools/dashboard/app.py

Anti-OOM for large circuits:

from dense_evolution import Chunk, QASMParser

qasm = 'OPENQASM 2.0; include "qelib1.inc"; qreg q[27]; h q[0:27];' circuit = QASMParser().parse(qasm)

sim = Chunk(27) sim.run_chunk(circuit.to_tuples(), chunk_size_gates=500)

▍ Key Features

JIT-fused statevector engine. No Kronecker-product overhead — stride-sliced linear kernel fusion compiled through JAX XLA, real GPU/TPU dispatch with no code change. Simulator · MPS backend for low-entanglement circuits at scale.
Anti-OOM `Chunk` engine. Circuits too large for one array, split dynamically and sized off the real compute device's own free memory — with disk-backed overflow past even that ceiling. Chunk guide.
Real noise, real mitigation. Stochastic Kraus channels, real-device noise imported from Qiskit backends, and Zero-Noise Extrapolation, with the shot budget split optimally across noise factors and a single-qubit Bloch-vector variant that always returns a valid state, to correct for it. Noise · Mitigation · what noise/mitigation/healing each mean.
Differentiable VQE, from scratch. circuit_to_energy_fn is the same JAX-differentiable engine real molecular VQE runs on — real Hartree-Fock Hamiltonians, UCCSD/hardware-efficient ansätze, Adam optimization. Autodiff.
libcint integrals on Windows, macOS and Linux. pip install dense-evolution ships libcint compiled for each system: the full two-electron tensor of ethanol in 6-31G* in 0.32 s. Open-shell molecules with UHF and ROHF (run_uhf, run_cuhf). native_hf.
Reversible arithmetic. Register adders, subtraction, comparisons and modular arithmetic up to modular exponentiation, applied as exact basis permutations, and as OPENQASM 2.0 gate circuits (Cuccaro, Draper and Beauregard adders, controlled modular multiplication), up to Shor order finding with one control qubit. Arithmetic.
Postselection. Keep the branch where a qubit is found in |0>, |1>, |±> or |±i>, with its probability. Postselection.
OpenQASM 2.0/3.0, both directions. A real parser, plus Qiskit/PennyLane interop bridges. QASM Parser · Interop.
Code-agnostic QEC decoding, Majorana/Jordan-Wigner fermion mapping, from-scratch Hartree-Fock for elements outside PennyLane's own basis set, a traversable-wormhole-inspired teleportation protocol, and real QM/MM region partitioning (ring-safe BFS, Diffuse2Seg-derived relevance propagation) — see the full API reference for all of it.

▍ Benchmarks

Measured on Windows, CPU only, 8 GB RAM — PennyLane's default.qubit allocates the full statevector; Dense Evolution's Chunk holds constant ~2 GB regardless of qubit count.

Qubits	Hilbert Space	PennyLane	Dense Evolution	Chunk Geometry
26	67,108,864	✅ 1,074 MB	✅ 2,050 MB	1× (2²⁷)
28	268,435,456	❌ OOM	✅ 2,050 MB	2× (2²⁷)
32	4,294,967,296	❌ OOM	✅ 2,048 MB	32× (2²⁷)

On Google Colab (12 GB RAM), n=28 runs in ~3s end-to-end (JIT-compiled, num_chunks=2) — the multi-chunk path is fast, not just OOM-safe. Past the real RAM ceiling (Chunk alone raises MemoryPressureError cleanly rather than crashing the process), allow_disk_overflow=True falls back to a slower disk-backed path instead of failing — correctness-first, not benchmarked for speed yet. Full measured t

Only the beginning is shown; read the rest in the repo

Dense Evolution

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

FlyDocs

README from the maker

Table of Contents

▍ What It Is

▍ Install

full stack: GPU · dashboard · Qiskit/PennyLane interop

just the interop bridge

native Hartree-Fock (basis-set data)

Composer's local kernel (see "Composer" below)

MCP server for the Composer kernel (see "MCP Server" below)

development

▍ Quick Start

parse any OpenQASM 2.0 / 3.0 string -- single-qubit rotations, a barrier

(a real OpenQASM synchronization marker: parsed like hardware would, no

effect on the simulated state), then an entangling layer

probs = [0.3201 0.3201 0.0549 0.0549 0.0183 0.0183 0.1067 0.1067]

[0. 0.5 0.5 0. ]

energy = -1.0, theta = [3.14159265]

1.622

▍ Key Features

▍ Benchmarks