Hybrid Analog Digital Processor for Quantum Optimization
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Solution Overview
Problem
Current computational devices, such as digital computers, face limitations in solving complex problems like protein folding and optimization issues due to their reliance on classical computing methods, which result in unfavorable scaling with problem size and time constraints.
Innovation Solution
A hybrid system combining a digital processor with an analog processor, where the analog processor uses physical evolution to solve computational problems, bypassing the need for long qubit coherence times and enabling faster solutions to complex problems through adiabatic or annealing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quantum error correction is implemented in circuit model quantum computers, then computational reliability is improved, but qubit coherence time requirements increase dramatically
Solution Approach 1:
The patent replaces the circuit model quantum computer's gate-based computational mechanism with an analog quantum computer's physical evolution mechanism. This substitution eliminates the need for discrete quantum gates and their associated error correction requirements, thereby reducing coherence time demands while maintaining computational reliability through natural quantum dynamics
Solution Approach 2:
The patent changes the fundamental operational parameters from discrete gate operations with strict coherence requirements to continuous physical evolution processes. By transforming the computational approach from digital quantum gates to analog Hamiltonian evolution, the system achieves reliable computation with significantly relaxed coherence time constraints
2Adaptability or versatility
If digital computers are used to solve complex optimization problems, then computational universality is maintained, but solution time and scalability deteriorate
Solution Approach 1:
The patent substitutes digital computational mechanics with analog quantum physical evolution. By encoding optimization problems into quantum Hamiltonians and utilizing natural quantum dynamics to evolve toward optimal solutions, the system achieves exponential speedup for certain problem classes while maintaining broad adaptability through problem encoding flexibility
Solution Approach 2:
The patent transitions from classical digital computation to quantum analog computation, adding the dimension of quantum mechanical behavior to the computational process. This dimensional shift enables parallel exploration of solution spaces through quantum superposition and tunneling, dramatically improving productivity for complex optimization problems
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for efficient solving of NP-hard problems and optimization issues beyond the capabilities of traditional digital computers, providing faster and more scalable solutions by leveraging the physical properties of quantum systems.
Implementation Method 1
the analog processor uses physical evolution to solve computational problems
Implementation Method 2
adiabatic or annealing processes
Implementation Method 3
adiabatic or annealing processes
Data Source
AI summary
Systems, devices, and methods for using an analog processor to solve computational problems. A digital processor is configured to track computational problem processing requests received from a plurality of different users, and to track at least one of a status and a processing cost for each of the computational problem processing requests. An analog processor, for example a quantum processor, is operable to assist in producing one or more solutions to computational problems identified by the computational problem processing requests via a physical evolution.