23 cutting-edge quantum algorithms ready to accelerate your applications
Leverage quantum computing for exponential speedups in ML tasks
Variational Quantum Eigensolver for finding ground states of quantum systems.
Quantum Chemistry, Material ScienceQuantum Approximate Optimization Algorithm for combinatorial problems.
Logistics, Portfolio OptimizationQNN with parameterized quantum circuits for classification and regression.
Pattern Recognition, ForecastingGenerative Adversarial Network powered by quantum circuits.
Data Generation, Image SynthesisReinforcement learning with quantum policy gradients.
Game Playing, RoboticsKernel methods using quantum feature spaces for SVM and regression.
Classification, Anomaly DetectionFast posterior sampling using amplitude estimation.
Uncertainty QuantificationQBM for sampling complex probability distributions.
Probabilistic ModelingExponential speedups for solving linear systems and matrix operations
Solve Ax = b with O(log N) complexity vs O(N³) classical.
Differential Equations, SimulationsTime-dependent linear systems with quantum advantage.
Dynamical Systems, Control TheoryQuadratic speedup for estimating expectation values.
Monte Carlo, Risk AnalysisHandle noisy linear systems with quantum resilience.
Noisy Data, Sensor FusionCutting-edge research implementations
Simulate time evolution of quantum systems variationally.
Quantum Simulation, ChemistryState estimation with quantum-enhanced predictions.
Signal Processing, TrackingDesign optimal control pulses for quantum gates.
Quantum Hardware, Gate DesignLearn Hamiltonian dynamics from quantum data.
System IdentificationContinuous-depth quantum neural networks.
Time-Series, DynamicsMeta-learning for quantum systems across tasks.
Few-Shot Learning, TransferBuild and visualize quantum circuits in real-time
Real-world simulation capabilities and quantum speedup benchmarks
Get started with quantum computing in minutes
from aios import QuantumStateEngine, QuantumVQE # Create quantum state engine (auto-selects backend) qc = QuantumStateEngine(num_qubits=5) # Build superposition for i in range(5): qc.hadamard(i) # Measure expectation value energy = qc.expectation_value('Z0') print(f"Energy: {energy}") # Run VQE for ground state vqe = QuantumVQE(num_qubits=4, depth=3) ground_energy, params = vqe.optimize(hamiltonian) print(f"Ground state: {ground_energy:.6f}")
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