Systems and methods for quantum bayesian networks
AssigneeD-Wave Systems Inc.
InventorsYanbo Xue, William G. Macready
Life of this patent
Estimating the term…
Abstract
Techniques are provided for computing problems represented as directed graphical models via quantum processors with topologies and coupling physics which correspond to undirected graphs. These include techniques for generating approximations of Bayesian networks via a quantum processor capable of computing problems based on a Markov network-based representation of such problems. Approximations may be generated by moralization of Bayesian networks to Markov networks, learning of Bayesian networks' probability distributions by Markov networks' probability distributions, or otherwise, and are trained by executing the resulting Markov network on the quantum processor.
Claims21
A method for quantum computing given a problem represented by a Bayesian network, the method executed by circuitry including at least one processor, the at least one processor in communication with a quantum processor comprising a plurality of qubits and couplers, the couplers operable to symmetrically couple qubits, the method comprising:
- obtaining a representation of the problem, the representation of the problem comprising a Bayesian network having a first plurality of nodes and a first plurality of directed edges;
- transforming the Bayesian network to a Markov network having a second plurality of nodes and a second plurality of undirected edges;
- transmitting the Markov network to the quantum processor and, by said transmitting, causing the quantum processor to execute based on the Markov network;
- obtaining one or more samples from the quantum processor;
- and
- determining one or more parameters of the Markov network based on the one or more samples to generate a parametrized Markov network;
- and
- determining an approximation of a predicted solution for the problem based on the parametrized Markov network.
A hybrid computational system, comprising:
- a quantum processor comprising a plurality of qubits and couplers, the couplers operable to symmetrically couple qubits;
- at least one processor in communication with the quantum processor;
- and
- at least one nontransitory processor-readable storage medium that stores at least one of processor-executable instructions or data which, when executed by the at least one processor cause the at least one processor to execute a method having the following acts:
- obtaining a representation of the problem, the representation of the problem comprising a Bayesian network having a first plurality of nodes and a first plurality of directed edges;
- transforming the Bayesian network to a Markov network having a second plurality of nodes and a second plurality of undirected edges;
- transmitting the Markov network to the quantum processor and, by said transmitting, causing the quantum processor to execute based on the Markov network;
- obtaining one or more samples from the quantum processor;
- and
- determining one or more parameters of the Markov network based on the one or more samples to generate a parametrized Markov network;
- and
- determining an approximation of a predicted solution for the problem based on the parametrized Markov network.
A computational system, comprising:
- at least one processor in communication with a quantum processor;
- and
- at least one nontransitory processor-readable storage medium that stores at least one of processor-executable instructions or data which, when executed by the at least one processor cause the at least one processor to execute a method having the following acts:
- obtaining a representation of the problem, the representation of the problem comprising a Bayesian network having a first plurality of nodes and a first plurality of directed edges;
- transforming the Bayesian network to a Markov network having a second plurality of nodes and a second plurality of undirected edges;
- transmitting the Markov network to the quantum processor and, by said transmitting, causing the quantum processor to execute based on the Markov network;
- obtaining one or more samples from the quantum processor;
- and
- determining one or more parameters of the Markov network based on the one or more samples to generate a parametrized Markov network;
- and
- determining an approximation of a predicted solution for the problem based on the parametrized Markov network.
Dependencies are inferred from the claim text.