Solving Climate Change Through Quantum Computing
AI-powered Quantum Co-Pilot that accelerates quantum R&D with zero setup and zero quantum expertise.
What is Feynman?
AI-Powered Quantum Co-Pilot
Generate, optimize, and deploy quantum algorithms using simple natural language — no quantum programming required.
No Setup or Infrastructure Needed
Everything runs in the cloud, including real quantum hardware execution — no installation, no configuration.
From Classical to Quantum Automatically
Feynman translates classical scientific problems into efficient quantum circuits, tailored for real-world impact.
Built to Accelerate Climate-Focused Research
Whether you’re modeling climate systems or optimizing EV batteries, Feynman helps cut algorithm deployment from weeks to minutes.
Why Feynman?
Quantum computing is powerful, but hard to use. Feynman makes it easy, fast, and accessible.
Natural Language Input
Real Quantum Execution
AI-Powered Circuit Design
10-Minute Algorithm Deployment
No Infrastructure Needed
Auto-Tuned Results
Classical-to-Quantum Mapping
Framework-Agnostic
Natural Language Input
Real Quantum Execution
AI-Powered Circuit Design
10-Minute Algorithm Deployment
Auto-Tuned Results
No Infrastructure Needed
Classical-to-Quantum Mapping
Framework-Agnostic
How Feynman Works?
Describe Your Problem
Input your classical requirement in natural language.
Feynman Maps & Builds Quantum Code
Automatically generates and optimizes quantum circuits.
Run on Quantum Hardware
Code is deployed and executed on actual quantum machines.
Results Are Decoded for You
Results are interpreted and presented in a clear, readable format.
Speed Up Quantum Research by 3–5x
from qiskit import QuantumCircuit, Aer, transpile, assemble, execute
from qiskit.circuit.library import GroverOperator, PhaseOracle
from qiskit.visualization import plot_histogram
import matplotlib.pyplot as plt
# Define the oracle for a simple problem
oracle = PhaseOracle("a & b")
# Create Grover's algorithm circuit
grover_op = GroverOperator(oracle)
grover_circuit = QuantumCircuit(grover_op.num_qubits)
grover_circuit.h(range(grover_op.num_qubits - 1))
grover_circuit.append(grover_op, range(grover_op.num_qubits))
grover_circuit.measure_all()
# Simulate the circuit
aer_sim = Aer.get_backend('aer_simulator')
qobj = assemble(transpile(grover_circuit, aer_sim))
result = aer_sim.run(qobj).result()
counts = result.get_counts()You Focus on the Science. We’ll Handle the Quantum
Don’t let quantum programming slow down your climate research. Feynman handles the code, optimization, and execution. So you can focus on solving the world’s biggest crisis.
<10 mins to deploy a quantum algorithm
30–50% productivity gain
1–2 weeks saved per algorithm
3–5x faster iteration cycles
Pricing plans tailored for you
Have any question? Find answer here.
Some frequently asked questions about our Feynman Co-pilot.