The technology is a photon-assisted quantum annealing system in which qubits model a Boolean problem and the solution is found through quantum annealing with multiphoton, inelastic collective scattering.

Quantum annealing is a quantum computing technique used to solve optimization problems. However, conventional quantum annealing methods are hampered by the sensitivity to low-frequency decoherence near small gaps, making it challenging to solve complex Boolean optimization problems accurately and efficiently. There is a pressing need for improved methods that can address these challenges. Current approaches to quantum annealing drive qubits using a quasi-static field transverse to the computational direction. This invariably restricts the potential solutions to specific states and can compromise the effectiveness of the optimization process. Another major shortcoming of current methods is their inability to enable continuous, quantum non-demolition measurement of the system, which limits real-time monitoring and adjustments.

Technology Description

The described technology is a photon-assisted quantum annealing system that makes use of a collection of qubits to model a Boolean optimization problem. The solution to the problem is found not by driving the qubits with a field transverse to the computational direction, but by allowing the spins to evolve between computational states using multiphoton, inelastic collective scattering into a common waveguide that is coupled transversely to all of the qubits. What sets this technology apart is its ability to enable transitions between arbitrary states through the continuum of modes of the waveguide. It circumvents the exponential sensitivity to low-frequency decoherence near small gaps that is characteristic of conventional quantum annealing. Also, because the transverse coupling to the waveguide averages to zero, the spin of each qubit experiences a net field purely in the computational direction. This innovative feature allows for the continuous, quantum non-demolition measurement of the system.

Benefits

  • Overcomes sensitivity to low-frequency decoherence
  • Enables transitions between arbitrary states
  • Allows continuous, quantum non-demolition measurement of the system
  • Addresses complex optimization problems more efficiently
  • Improves real-time monitoring and adjustment capabilities

Potential Use Cases

  • Solving complex optimization problems in machine learning and artificial intelligence
  • Enhancing solutions for cryptography and code-breaking
  • Improving simulations in quantum chemistry and physics
  • Developing more efficient resource allocation systems
  • Innovating for traffic optimization and logistics