A method and a system are disclosed for determining a weight allocation in a group comprising a large plurality of items using an optimization oracle, the method comprising obtaining an indication of a plurality of data for each item of a large plurality of items; generating a covariance matrix for the plurality of data; generating a hierarchical tree structure having a plurality of clusters, each cluster having a corresponding item associated therewith, the generating comprising until there is one item associated per cluster of the hierarchical tree structure, recursively formulating an optimization problem to divide a given set of items into two different clusters, translating the formulated optimization problem into an unconstrained binary optimization problem, providing an indication of the unconstrained binary optimization problem to an optimization oracle, receiving an indication of at least one solution from the optimization oracle, assigning a cluster to each item of the given set of items using the at least one solution; recursively determining a weight allocation for each item of the plurality of items using the covariance matrix and the generated hierarchical tree structure and providing an indication of the determined weight allocation for each item in the group comprising a plurality of items.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for determining a weight allocation in a group comprising a large plurality of items using an optimization oracle, the method comprising: obtaining using a processor an indication of a plurality of data for each item of a large plurality of items; generating using the processor a covariance matrix for the plurality of data; generating a hierarchical tree structure having a plurality of clusters, each cluster having a corresponding item associated therewith, the generating comprising: until there is one item associated per cluster of the hierarchical tree structure, recursively formulating an optimization problem to divide a given set of items into two different clusters using the processor, translating using the processor the formulated optimization problem into an unconstrained binary optimization problem, providing using the processor an indication of the unconstrained binary optimization problem to an optimization oracle, receiving an indication of at least one solution from the optimization oracle using the processor, using the processor assigning a cluster to each item of the given set of items using the at least one solution; using the processor, recursively determining a weight allocation for each item of the plurality of items using the covariance matrix and the generated hierarchical tree structure; and providing using the processor an indication of the determined weight allocation for each item in the group comprising a plurality of items. 2. The method as claimed in claim 1, wherein the indication of a plurality of data for each item of a large plurality of items is obtained from a user interacting with the processor. 3. The method as claimed in claim 1, wherein the indication of a plurality of data for each item of a large plurality of items is obtained from a memory unit comprised in the processor. 4. The method as claimed in claim 1, wherein the indication of a plurality of data for each item of a large plurality of items is obtained from a remote processing unit operatively coupled with the processor. 5. The method as claimed in claim 1, further comprising reordering using the processor the plurality of items using the generated hierarchical tree structure to provide an ordered list of items and rearranging using the processor the generated covariance matrix using the ordered list of items. 6. The method as claimed in claim 1, wherein the indication of the determined weight allocation for each item in the group comprising a plurality of items is provided to a user interacting with the processor. 7. The method as claimed in claim 1, wherein the indication of the determined weight allocation for each item in the group comprising a plurality of items is stored in a memory unit comprised in the processor. 8. The method as claimed in claim 1, wherein the indication of the determined weight allocation for each item in the group comprising a plurality of items is provided to a remote processing unit operatively coupled with the processor. 9. The method as claimed in claim 1, wherein each item is an asset and the group comprising a large plurality of items is a portfolio comprising a large plurality of assets; further wherein the plurality of data of a given item comprises a value of the asset over time. 10. A digital computer comprising: a central processing unit; a display device; a communication port for operatively connecting the digital computer to an analog computer comprising a quantum processor; a memory unit comprising an application for determining a weight allocation in a group comprising a large plurality of items, the application comprising: instructions for obtaining an indication of a plurality of data for each item of a large plurality of items; instructions for generating a covariance matrix for the plurality of data; instructions for generating a hierarchical tree structure having a plurality of clusters, each cluster having a corresponding item associated therewith, the generating comprising until there is one item associated per cluster of the hierarchical tree structure, recursively formulating an optimization problem to divide a given set of items into two different clusters, translating the formulated optimization problem into an unconstrained binary optimization problem, providing an indication of the unconstrained binary optimization problem to the analog computer, receiving an indication of at least one solution from the analog computer, assigning a cluster to each item of the given set of items using the at least one solution; instruction for recursively determining a weight allocation for each item of the large plurality of items using the covariance matrix and the generated hierarchical tree structure; and instructions for providing an indication of the determined weight allocation for each item in the group comprising a plurality of items. 11. A non-transitory computer readable storage medium for storing computer-executable instructions which, when executed, cause a digital computer to perform a method for determining a weight allocation in a group comprising a large plurality of items using an optimization oracle, the method comprising: obtaining using a digital computer an indication of a plurality of data for each item of a large plurality of items; generating using the digital computer a covariance matrix for the plurality of data; generating a hierarchical tree structure having a plurality of clusters, each cluster having a corresponding item associated therewith, the generating comprising until there is one item associated per cluster of the hierarchical tree structure, recursively formulating an optimization problem to divide a given set of items into two different clusters using the digital computer, translating using the digital computer the formulated optimization problem into an unconstrained binary optimization problem, providing using the digital computer an indication of the unconstrained binary optimization problem to an optimization oracle, receiving an indication of at least one solution from the optimization oracle using the digital computer, using the digital computer assigning a cluster to each item of the given set of items using the at least one solution; using the digital computer, recursively determining a weight allocation for each item of the plurality of items using the covariance matrix and the generated hierarchical tree structure; and providing using the digital computer an indication of the determined weight allocation for each item in the group comprising a plurality of items. 12. A method for operating a system comprising a digital computer and an optimization oracle coupled to the digital computer to determine a weight allocation in a group comprising a large plurality of items, the method comprising: obtaining using a digital computer an indication of a plurality of data for each item of a large plurality of items; generating using the digital computer a covariance matrix for the plurality of data; generating a hierarchical tree structure having a plurality of clusters, each cluster having a corresponding item associated therewith, the generating comprising: until there is one item associated per cluster of the hierarchical tree structure, recursively formulating an optimization problem to divide a given set of items into two different clusters using the digital computer, translating using the digital computer the formulated optimization problem into an unconstrained binary optimization problem, providing using the digital computer an indication of the unconstrained binary optimization problem to an optimization oracle, solving the unconstrained binary optimization problem using the optimization oracle, receiving an indication of at least one solution from the optimization oracle using the digital computer, using digital computer assigning a cluster to each item of the given set of items using the at least one solution; using the digital computer, recursively determining a weight allocation for each item of the plurality of items using the covariance matrix and the generated hierarchical tree structure; and providing using digital computer an indication of the determined weight allocation for each item in the group comprising a plurality of items.