Quantum Processor Architecture for Deep Boltzmann Machine Training
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Solution Overview
Problem
Existing neural network training methods, such as Restricted Boltzmann Machines, are slow and prone to errors when dealing with deep learning tasks, especially when hidden units are far from visible units, limiting their effectiveness in machine learning applications.
Innovation Solution
A quantum processor with a specific architecture comprising parallel qubits and couplers is used to implement a Deep Boltzmann Machine, employing quantum sampling to train the network by switching off biases and altering couplings based on training data, allowing for faster and more accurate weight and bias updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If Restricted Boltzmann Machines are used for training deep learning networks, then the network can be trained layer by layer, but the training process becomes slow and accumulates errors when hidden units are far from visible units
Solution Approach 1:
The patent replaces the classical mechanical training system (Restricted Boltzmann Machines updating weights through iterative algorithms) with a quantum system (quantum processor executing quantum circuits). The quantum processor uses quantum superposition and entanglement to simultaneously explore multiple weight configurations, substituting the sequential classical optimization process with parallel quantum evolution, thereby achieving exponential speedup in training deep learning networks
Solution Approach 2:
The patent transforms the training problem by changing the parameter space from classical probability distributions to quantum state vectors. By representing network weights as quantum parameters and using quantum gates to manipulate these parameters, the system achieves more efficient optimization landscapes and avoids the local minima problems that plague classical training methods
2Adaptability or versatility
If Restricted Boltzmann Machines are used with multiple hidden layers, then deep learning capabilities are achieved, but errors accumulate and training accuracy decreases
Solution Approach 1:
The patent replaces the error-prone classical iterative training mechanism with a quantum system that uses unitary transformations to evolve the state. Quantum evolution is deterministic and reversible, allowing for exact computation of probability distributions even in deep networks, thereby preventing error accumulation that occurs in classical approximate inference methods
Solution Approach 2:
The patent introduces quantum superposition as an additional dimension for representing hidden unit states. Instead of classical binary or continuous values, quantum states exist in a high-dimensional Hilbert space, enabling the network to represent and process complex correlations across multiple layers without the dimensional collapse that causes error accumulation in classical deep networks
Data Source
AI summary
A quantum processor comprises a first set of qubits comprising a first plurality of substantially parallel qubits; a second set of qubits comprising N successive groups of a plurality of qubits (1, 2, . . . , N), wherein N is greater than or equal to two; wherein each group of qubits comprises a plurality of substantially parallel qubits; wherein each qubit of the first plurality of substantially parallel qubits of the first set of qubits crosses substantially perpendicularly a portion of the plurality of substantially parallel qubits of a first group of the second set of qubits; wherein each qubit of any given group of the second set of qubits crosses substantially perpendicularly a portion of the plurality of substantially parallel qubits of a successive group of the second set of qubits and a plurality of couplers, each coupler for providing a communicative coupling at a crossing of two qubits.