Quantum memory, classical learning.
QMANN combines classical neural networks with quantum memory operations to push learning capacity past what classical models alone can reach. The reference implementation provides practical quantum-inspired algorithms suitable for near-term NISQ devices, with three reproducible modes — Theoretical, Simulation, and Hardware.
Theoretical
Unlimited (ideal quantum computer)
Simulation
Up to 20 (with noise modelling)
Hardware
4–12 (NISQ-bounded)
FIG 01 · Hardware mode runs cost estimator before any shots are submitted
3
Reproducible modes
20
Qubits in simulation
4–12
Qubits on hardware
Apache-2.0
Open licence
Pick the mode, not the marketing.
Most QML libraries hide the cost surface and the noise model. QMANN exposes both, and ships three explicit modes so you always know what your run actually means.
Theoretical
Validate algorithm structure and theoretical bounds before any cost is incurred. Perfect gates, infinite coherence.
- Cost
- Free
- Qubits
- Unlimited (ideal quantum computer)
- Audience
- Research papers, theoretical bounds, algorithm design
Simulation
Classical simulation with noise modelling and quantum-inspired operations. Ship the science before paying for shots.
- Cost
- Free (compute only)
- Qubits
- Up to 20 (with noise modelling)
- Audience
- Algorithm validation, development, education, reproducible research
Hardware
Run on real quantum devices with cost-estimator gating. Hardware mode requires explicit credentials and a confirmed budget.
- Cost
- Paid (IBM ~$0.001 / shot, IonQ ~$0.01 / shot)
- Qubits
- 4–12 (NISQ-bounded)
- Audience
- Proof-of-concept, hardware benchmarking, quantum advantage validation
Hybrid learning research needs a path from simulation to governed hardware runs.
QMANN now sits inside a clearer professional context: theoretical mode, simulation mode, hardware budgets, and repeatable evidence for quantum-machine-learning programs.
FIG 02 · Quantum ML operations — from theoretical bounds to governed hardware runs
Four components, hybrid by design.
01
Q-Matrix
Quantum memory layer with entangled qubit registers.
02
Quantum-LSTM
Hybrid controller with parameterised quantum circuits.
03
Energy-optimal protocols
Advanced error mitigation and measurement strategies.
04
Real-world applications
Healthcare, industrial IoT, autonomous systems modules.
Citations, not vibes.
Every technique below maps to a published reference. Pull the paper, read the section, then run the code.
01
Quantum LSTM with segment processing
QSegRNN-style segment-based quantum recurrent neural networks. Quantum gates for LSTM operations, hybrid memory cells, enhanced temporal pattern recognition.
EPJ Quantum Technology · March 2025
02
QAOA with warm-start adaptive bias
Conditional diffusion-based parameter generation. Classical solution warm-start, adaptive bias correction. Faster convergence with reduced optimisation overhead.
Physical Review · 2025; EPJ Quantum Technology · August 2025
03
Grover dynamics optimisation
Grover-inspired amplitude amplification for optimisation problems. O(√N) speedup for unstructured search with adaptive oracle construction.
Cornell Lawler Research · January 2025
04
Quantum-enhanced transformers
Quantum-classical attention integration. Multi-head quantum attention, entanglement-based correlations, configurable quantum attention ratio (0–100%).
arXiv:2504.00068 · arXiv:2501.15630
05
Circuit-noise-resilient virtual distillation
Enhanced virtual distillation with noise resilience. Multiple virtual copies, adaptive thresholds, maintains effectiveness with imperfect gates.
Communications Physics · October 2024
06
Learning-based error mitigation
ML models for quantum error prediction and correction. Neural network error models, adaptive strategies, real-time error prediction and correction.
Recent QEM literature · 2025
Five sectors with concrete pipelines.
Sector — example pipelines
- Finance
- QAOA portfolio optimisation, fraud detection on transaction streams, market prediction with quantum LSTMs, quantum-enhanced risk modelling and stress testing.
- Drug discovery
- Quantum transformers for molecular property prediction, drug-target binding affinity, Grover-dynamics molecule generation, ADMET safety prediction.
- Materials science
- Quantum property prediction, adaptive-VQE crystal structure refinement, battery material design, catalyst design via quantum simulation.
- Healthcare
- Pattern recognition for diagnosis, optimisation for personalised medicine, protein folding via quantum simulation, clinical-trial patient matching.
- Industrial
- Quantum anomaly detection for predictive maintenance, classifiers for defect detection, QAOA for logistics, quantum simulation for chemical processes.
Pick a mode. Run an example.
$ git clone https://github.com/neuraparse/QMANN.git $ cd QMANN $ pip install -e . # 🔬 Theoretical mode (FREE) $ python examples/01_theoretical_mode.py # 💻 Simulation mode (FREE) $ python examples/02_simulation_mode.py # ⚛️ Hardware mode (PAID — estimate first!) $ python scripts/estimate_hardware_costs.py --qubits 6 --shots 1000 $ python examples/03_hardware_mode.py
QMANN compiles through qmesh.
When you target hardware, QMANN circuits flow through qmesh for IR lowering, error mitigation, and ed25519-signed run manifests. The same provenance chain that satisfies regulated industries also gives ML researchers fully reproducible benchmarks.