Dense Evolution

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "dense-evolution": {
      "command": "npx",
      "args": [
        "-y",
        "dense-evolution"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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

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 →

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

▍ Install

Terminal

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]

# 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):

python

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

▍ Quick Start

server.ts

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:

server.ts

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:

server.ts

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):

server.ts

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):

Terminal

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

Anti-OOM for large circuits:

server.ts

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 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.
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, and a traversable-wormhole-inspired teleportation protocol — 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)

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Dense Evolution

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "dense-evolution": { "command": "npx", "args": ["-y", "Dense Evolution"] } }

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Technical Specs & Signals

CategoryDeveloper Tools

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to Dense Evolution →Install in Claude Desktop Install in Cursor Install in VS Code

Dense Evolution

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "dense-evolution": {
      "command": "npx",
      "args": [
        "-y",
        "dense-evolution"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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

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

▍ What It Is

📖 Full documentation, API reference, and worked examples →

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

▍ Install

Terminal

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]

# 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

Google Colab (3 lines):

python

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

▍ Quick Start

server.ts

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:

server.ts

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:

server.ts

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):

server.ts

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):

Terminal

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

Anti-OOM for large circuits:

server.ts

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 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.
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, and a traversable-wormhole-inspired teleportation protocol — 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)

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Dense Evolution

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "dense-evolution": { "command": "npx", "args": ["-y", "Dense Evolution"] } }

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Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

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<a href="https://allmcps.com/mcp/dense-evolution"><img src="https://allmcps.com/api/badge/dense-evolution?style=directory" alt="Dense Evolution on AllMCPs" /></a>

Technical Specs & Signals

CategoryDeveloper Tools

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

★ Featured

Moxie Docs MCP

MCP & Agent Skills for Automated Documentation, and codebase conventions + context

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to Dense Evolution →Install in Claude Desktop Install in Cursor Install in VS Code

Dense Evolution

Quick Install

Client Config & Setup

Documentation Overview

Table of Contents

▍ What It Is

▍ Install

▍ Quick Start

▍ Key Features

▍ Benchmarks

Related MCP Servers

Reviews

Frequently Asked Questions about Dense Evolution

AllMCPs Directory Badge

Technical Specs & Signals

Moxie Docs MCP

Own this project?

Share & Embed

Explore more

Dense Evolution

Quick Install

Client Config & Setup

Documentation Overview

Table of Contents

▍ What It Is

▍ Install

▍ Quick Start

▍ Key Features

▍ Benchmarks

Related MCP Servers

Reviews

Frequently Asked Questions about Dense Evolution

AllMCPs Directory Badge

Technical Specs & Signals

Moxie Docs MCP

Own this project?

Share & Embed

Explore more