01
Method · State-vector & density-matrix
Circuit Simulation
Full-state simulation up to 30 qubits with custom unitaries, native gate sets, and mid-circuit measurement. Exact where exact is tractable.
30 qubits
Launch The Engine
Written by physicists. Packaged for engineers.
Six computational engines. One coherent stack, built by the team that runs it. Your business problem in, a running quantum algorithm out — fourteen working days. Not a simulator license. Not a wrapper on somebody else’s runtime. Code the engineers can defend line by line.
6
Engines One coherent stack
14d
Problem → running code
80%
QPU-fidelity Benchmarked
The Team
Every line written inside.
Every line written inside.
Not licensed. Not wrapped.
The engine is not a rebadged open-source distribution and not a shim over somebody else’s runtime. From the noise-channel calibration to the variational optimiser, every subsystem was written by engineers on the team — the same people who will run it on your problem.
When a circuit plateaus, when a noise channel dominates, when an ansatz under-performs, we can defend the answer mathematically. That is the difference between a team that wrote the simulator and a team that imports one.
- 01 Authored, not imported. The engineers who built the stack are the ones who deploy it.
- 02 Hardware-aware from day one. Calibrated against real backend data, not textbook noise toys.
- 03 Exports, not exports. The code leaves in your framework, not ours.
The Stack
Six engines. One system.
Six engines. One system.
Each does one thing precisely.
A quantum simulator is not one program. It is a stack of specialised engines that must cooperate: you encode the problem, you parameterise the circuit, you model the hardware, you optimise the cost function, you benchmark the result, and you hand it to a human who can read the output.
02
Method · Hybrid classical-quantum loops
Optimizer
Variational optimisation with SPSA, COBYLA, and gradient-aware backends. Fewer function evaluations per convergence.
+50% VQA efficiency
03
Method · Parameterised circuit search
Ansatz
Problem-aware ansatz construction. Hardware-efficient, symmetry-preserving, and adaptive variants with barren-plateau diagnostics.
Hardware-aware
04
Method · Calibrated noise channels
Noise
Depolarising, amplitude damping, thermal relaxation, and correlated crosstalk channels calibrated from real backend data.
<5% deviation
05
Method · Fidelity + resource estimation
Analysis
Automated benchmarking against classical baselines, fidelity bounds, convergence curves, and resource estimation. Every result ships with its error bar.
80% QPU-fidelity
06
Method · Visual circuit builder
Interface
A working dashboard, not a Jupyter tutorial. Drag-and-drop circuit construction, real-time simulation, side-by-side classical benchmark.
No CLI required
The Export
Yours to run.
Yours to run.
Yours to port.
Every engagement ends with code in your repository, written in the framework your team already uses. No proprietary runtime. No vendor lock. The day a quantum processor becomes viable for your workload, you are already ported.
# Exported from DeployQuantum Engine
# Problem: portfolio optimisation (4-asset test case)
# Hardware target: IBM Eagle, 127q
# Noise profile: calibrated, <5% deviation from backend data
from qiskit import QuantumCircuit, transpile
from qiskit_aer.noise import NoiseModel
from qiskit_algorithms.optimizers import SPSA
ansatz = QuantumCircuit(4)
for i in range(4):
ansatz.ry(params[i], i)
for i in range(3):
ansatz.cx(i, i + 1)
noise_model = NoiseModel.from_backend("ibm_eagle")
compiled = transpile(ansatz, optimization_level=3)
result = SPSA().optimize(cost_fn, params, compiled, noise_model)
# result.optimal_value → benchmarked against classical SLSQP baseline
# result.fidelity → reported with error bar The Pipeline
Fourteen working days.
Fourteen working days.
Four stages. One artefact per stage.
A two-week engagement is a discipline, not a rush. Each stage ends with a concrete artefact that you keep, in your language, in your repository.
- Day 1–3
Encode the problem
A working session, not a scoping call. We unpack the business objective and translate it into a quantum cost function with a classical baseline to beat.
Output Cost function, classical benchmark, success criteria. - Day 4–7
Build the circuit
Ansatz selection, circuit compilation, first optimisation pass. Results on the simulator, caveats labelled, convergence curves reviewed.
Output Parameterised circuit, first convergence on synthetic data. - Day 8–11
Profile the hardware
Calibrated noise. IBM Eagle and IonQ Aria profiles. The circuit runs under realistic error conditions before a single QPU shot is booked.
Output Noise-aware benchmark, hardware recommendation, fidelity bound. - Day 12–14
Hand it over
Code export, documentation, benchmark report. A session with your engineers to walk the stack end-to-end. A session with your executives to walk the number.
Output Qiskit/PennyLane/Qulacs code, report, board briefing, hardware plan.
The Numbers
Metrics with the benchmark,
Metrics with the benchmark,
never without.
A number without its benchmark is marketing. These are ours, alongside the reference.
QPU-equivalent fidelity
Measured at up to 30 qubits. Reference: published hardware benchmarks on
IBM Eagle and IonQ Aria, matched against our calibrated noise model.
Noise-channel deviation
Channel-by-channel comparison against vendor-published calibration data.
Depolarising, amplitude-damping, and correlated crosstalk within tolerance.
VQA efficiency gain
Fewer function evaluations per convergence vs. baseline SPSA/COBYLA implementations
in standard open-source distributions, on our internal VQE benchmark suite.
Bring the problem
We have already built the rest.
Describe the business question. We will tell you whether quantum simulation genuinely helps, what a fourteen-day engagement looks like, and what the benchmark will be. If the honest answer is “not yet,” we will say so — and tell you what to watch.