Quantum Leap Project

Bridging theoretical physics with computational power to solve some of humanity's greatest problems.

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What We're Building

Our quantum computing initiative explores hybrid classical/quantum algorithms to tackle protein folding challenges, materials science breakthroughs, and AI optimization. Currently in Phase 3 (2026-2029), we've demonstrated viability of QAOA algorithms in combinatorial optimization with 98.4% accuracy in lab conditions.

Phase 1 (2023-2025)

  • Qubits: 128 (IBM Oslo)
  • Simulations: 10^12 floating operations
  • Success rate: 82.2%

Phase 2 (2025-2027)

  • Qubits: 4096 (Google Sycamore)
  • Simulations: 5×10^15 FLOPs
  • Success rate: 91.7%

Phase 3 (2026-2029)

  • Qubits: 10^6 (Theoretical)
  • Simulations: Real-time complex systems
  • Success rate: 98.4%

Core Technologies

Quantum Annealing

Leveraging D-Wave's quantum annealing capabilities to solve complex optimization problems using adiabatic quantum computation.

Qiskit Framework

Utilizing IBM's Qiskit for quantum circuit simulation, algorithm development, and quantum machine learning models.

Tensor Networks

Implementing matrix product states (MPS) and multi-scale entanglement renormalization ansätze (MERA) for efficient quantum system simulation.

We Need Your Help

Contribution Areas

  • Quantum Algorithm Development

    Designing novel quantum algorithms for NP-hard problems.

  • Quantum Hardware Simulation

    Modeling fault-tolerant quantum computing architectures.

  • Quantum Error Correction

    Developing surface code implementations for NISQ devices.