Maestro Examples

🚀 Go beyond your laptop. Try Maestro GPU mode with a free trial. Sign up at maestro.qoroquantum.net to run these simulations at scale.

Example simulations demonstrating quantum simulation capabilities with the Maestro high-performance quantum circuit simulator — each structured as a two-phase tutorial showing the jump from local CPU simulation to GPU-accelerated execution.

Why GPU Mode?

MPS simulation accuracy is controlled by bond dimension χ — but tensor contractions scale as O(χ³). On a CPU, doubling χ means 8× the runtime. For large systems with high entanglement, CPU-only simulation hits a wall fast.

Maestro's GPU backend (SimulatorType.Gpu) parallelizes those O(χ³) contractions on NVIDIA GPUs, delivering 10–100× speedups on the expensive steps — with zero code changes:

# CPU — works, but slow at high bond dimension
result = qc.estimate(
    simulator_type=maestro.SimulatorType.QCSim,
    simulation_type=maestro.SimulationType.MatrixProductState,
    max_bond_dimension=64,
)

# GPU — same API, same code, just swap one argument
result = qc.estimate(
    simulator_type=maestro.SimulatorType.Gpu,          # ← GPU
    simulation_type=maestro.SimulationType.MatrixProductState,
    max_bond_dimension=256,                                   # ← go higher
)

Every example below follows the same pattern: Phase 1 runs locally on CPU with modest parameters to prove the physics works. Phase 2 scales up with GPU mode — because your CPU shouldn't be the bottleneck.

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Getting Started

pip install qoro-maestro

👉 Start your free GPU trial →

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Examples

1. Rydberg Atom Simulation ⚡

Phase diagram and spatial correlations of a 64-atom Rydberg array. Sweeps (Δ, Ω) parameter space to map the Z2 ordered phase.

The bottleneck: 64 atoms × 144 parameter points × MPS simulation each. Scaling to higher bond dimension for accuracy multiplies cost by χ³. The fix: GPU mode handles high-χ MPS at a fraction of the CPU time — sharper phase boundaries without the wait.

📓 Interactive notebook — step-by-step tutorial

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2. Classical Shadows ⚡

Entanglement detection via MPS-based shadow tomography. Estimates 2nd Rényi entropy during Trotter evolution of the transverse-field Ising model.

The bottleneck: Hundreds of independent MPS snapshots at 36 qubits. Each snapshot is a full simulation — sequentially, it crawls. The fix: GPU-accelerated MPS cuts per-snapshot time dramatically, making large-scale shadow tomography feasible.

📓 Interactive notebook — step-by-step tutorial

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3. Fermi-Hubbard Model ⚡

Adaptive 3-tier simulation of a 200-qubit Fermi-Hubbard system. The Lieb-Robinson light cone reduces a 200-qubit problem to ~40 active qubits.

The bottleneck: The precision tier (χ=256) dominates runtime. O(χ³) tensor contractions on CPU take hours. The fix: GPU acceleration on the precision tier → ~10× speedup on the most expensive step. The whole pipeline finishes in minutes.

📓 Interactive notebook — step-by-step tutorial

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4. Adaptive Bond Dimension ⚡

CPU↔GPU backend switching during MPS time evolution. As entanglement grows, the simulation automatically upgrades from low-χ CPU to high-χ GPU.

The bottleneck: High bond dimension means O(χ³) per step. CPU grinds to a halt exactly when accuracy matters most. The fix: GPU mode parallelizes the heavy tensor contractions — accurate AND fast. Same code, one argument change.

📓 Interactive notebook — step-by-step tutorial

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5. Quantum Many-Body Scarring ⚡

PXP fidelity revivals from the Néel state in a Rydberg chain. Tracks staggered magnetization oscillations — a dramatic ETH violation.

The bottleneck: 64 atoms × 60 Trotter steps at moderate bond dimension = hours on CPU. The fix: GPU mode delivers the full revival structure of a 64-atom chain without overnight runs.

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6. Dynamical Quantum Phase Transition ⚡

Loschmidt echo cusps after a sudden TFIM quench. Non-analytic singularities in the rate function — dynamical analogs of thermodynamic phase transitions.

The bottleneck: Multi-quench sweep × 40 Trotter steps each. Crisp cusps need χ=64+ on 80 qubits. The fix: GPU mode makes the full 80-qubit, 3-quench sweep run in reasonable time.

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7. Surface Code Noise ⚡

Coherent vs Pauli noise analysis on surface code circuits. Reveals error correlations that stabiliser simulators (Stim) fundamentally cannot capture.

The bottleneck: Surface codes at d=5+ have 49+ qubits with deep CX networks. MPS at high χ is needed to faithfully track coherent noise correlations. The fix: GPU mode enables high-fidelity noise characterisation at scale — understanding real hardware noise beyond Pauli approximations.

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Ready to Scale?

Every example in this repo works locally on CPU. But when you're ready to go beyond toy parameters:

1. Start your free GPU trial — no credit card required
2. pip install qoro-maestro
3. Add --gpu to any example script

That's it. Same code, GPU scale.

# CPU (default)
python scarring_demo.py

# GPU — one flag, 10-100× faster
python scarring_demo.py --gpu

👉 maestro.qoroquantum.net

Maestro Features Demonstrated

Feature	API	Examples
Matrix Product State	SimulationType.MatrixProductState	All examples
Pauli Propagator	SimulationType.PauliPropagator	Fermi-Hubbard (Tier 1)
Bond dimension control	max_bond_dimension=χ	All examples
Expectation values	qc.estimate(observables=...)	All examples
Bitstring sampling	qc.execute(shots=N)	Rydberg, Classical Shadows
CPU backend	SimulatorType.QCSim	All examples
GPU acceleration	SimulatorType.Gpu	All examples (Phase 2)

License

See LICENSE for details.