Practical quantum for industry

Turn your use case into quantum or hybrid code in minutes, execute on simulators or QPUs with full cost, speed and accuracy metrics.

Built for Every Quantum Explorer

From quantum specialists to classical developers exploring quantum advantage, Superpositions Studio serves the entire quantum computing ecosystem.

Quantum
Developers

Technical professionals in innovation and transformation units exploring quantum computing for competitive advantage.

AI/ML
Practitioners

Academic researchers and quantum science enthusiasts seeking to explore quantum computing and its applications.

Enterprise
R&D

Software engineers and developers interested in learning quantum computing and building quantum applications.

Academic
Researchers

Artificial intelligence and machine learning professionals exploring quantum computing for advanced AI applications.

The Quantum Development Platform

From quantum prototype to practical solution in five simple steps.

Describe Your Problem

I need to detect rare fraudulent card transactions in a stream of 20M payments a day using my rules and a trained risk score.

Or choose a template:

Mapped Use Case

Oracle-based rare-event search. Build a reversible oracle that flags a transaction as fraud/not-fraud from rules or a scorer, then search for marked items using amplitude amplification.

Recommended Quantum Algorithm

Grover Search (ML oracle)

Dataset Requirements

Required datasets will appear here...

Recommended

Best for your use case

QAOA (Quantum Approximate Optimization)

Designed for combinatorial optimization problems. Provides quantum speedup for portfolio optimization with 30+ assets.

Hardware Requirements50+ qubits

Alternative Algorithms

Classical Baseline

Simulated Annealing

Traditional optimization method for comparison

Runtime

~15 minutes

Accuracy

85%

Why QAOA?

Native support for constraint handling

Proven quantum advantage for optimization

Available on current quantum hardware

Auto-Generated Quantum Circuit

import pennylane as qml
from pennylane import numpy as np
dev = qml.device("default.qubit", wires=4)
@qml.qnode(dev)
def qnn(x, w):
  qml.AngleEmbedding(
      x,
      wires=range(4),
      rotation="Y",
  )
  qml.layers.StronglyEntanglingLayers(w, wires=range(4))
  return qml.expval(qml.PauliZ(0))
w = qml.init.strong_ent_layers_normal(1, 4, seed=1)
print(qnn(np.ones(4), w))

Code Features

Production-ready PennyLane code

Code works on simulators or real devices via plugins

Configurable parameters

Reproducible runs

One-click export to Python script

Available Backends

Quantum SimulatorFree

IBM Quantum (Real)5 credits

IonQ Harmony12 credits

Prediction Visualization

Actual Values
Predicted Values

Architecture & Model Details

Qubits

Layers

Weights

114

Training Time

42s

Business Impact

Measurable value delivered by the model

Cost Savings

$127,500

ROI

234%

Accuracy

94%

Time Saved

67%

Quantum solution delivers measurable business value through improved accuracy and reduced operational costs.

Performance Comparison

Classical

Time15 min

Profit11.8%

CPU cores8

Quantum

Time3 min

Profit12.4%

Qubits16

Quantum Advantage

5x faster execution with 5% better returns for this problem size.

Advantage confirmed for 30+ assets

Future Projections

Next 12 months

With error rates improving by 10x, expect 3x additional speedup

2-3 years

Fault-tolerant systems could achieve 100x speedup for large portfolios

Scaling Analysis

T_classical = O(n³)

T_quantum = O(n²)

Quantum advantage increases with problem size

Pricing — Flexible and Fair

Simple credit-based model. You only pay for what you actually use. No lock-in, unused credits roll over.

Pro

€20

/ per month

Lock your price

Quantum hardware access

Hybrid compute (CPU/GPU/QPU)

Dashboards & live monitoring

Export as Python

Scientific-paper reports

Price lock

Price may increase over time

or skip trial and pay now

Key Features

Everything you need to transform industrial problems into quantum solutions.

AI Multi-Agent Navigator

The multi-agent system maintains a dialogue, clarifies parameters, maps the task to the nearest case, reforms the dataset if necessary, and selects a quantum algorithm and hyperparameters.

Access to QPUs from IBM Quantum, IonQ, Rigetti, and IQM

All trademarks belong to their respective owners; mentions are for identification only and do not imply endorsement or affiliation.

Get Started Today — Explore Your Quantum Use Case

Start with a real use case. In minutes, Superpositions Studio delivers runnable quantum code, one-click runs on simulators or QPUs, full cost/speed/accuracy metrics, and a shareable PDF report.

Practical quantum algorithms — built for results, not hype.

The next breakthrough is waiting in superposition — let's collapse it together into reality.

support@superpositions.studio