A method is provided for solving a computational problem that is reducible to a problem of counting solutions to an associated decision problem. The method includes estimating a number of the solutions to the decision problem using a quantum computer by determining if there is at least one simultaneous solution to both (i) the decision problem and (ii) an associated hashing problem. The method also includes increasing a precision of the estimated number of the solutions to the decision problem using the quantum computer by determining if there are multiple solutions to the decision problem that are simultaneously solutions to the associated hashing problem. The method further includes outputting or using the estimated number of the solutions to the decision problem as a solution to the computational problem.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for solving a computational problem that is reducible to a problem of counting solutions to an associated decision problem, the method comprising: using a quantum computer, estimating a number of the solutions to the decision problem by determining if there is at least one simultaneous solution to both (i) the decision problem and (ii) an associated hashing problem; using the quantum computer, increasing a precision of the estimated number of the solutions to the decision problem by determining if there are multiple solutions to the decision problem that are simultaneously solutions to the associated hashing problem; and outputting or using the estimated number of the solutions to the decision problem as a solution to the computational problem. 2. The method of claim 1, wherein: the hashing problem is associated with a hash function; and each solution to the hashing problem denotes a solution that obtains a specified random hash value from the hash function. 3. The method of claim 2, wherein the hash function is based on the discrete logarithm. 4. The method of claim 1, wherein the associated hashing problem is solved efficiently with the quantum computer but cannot be solved efficiently with a classical computer. 5. The method of claim 1, wherein: the computational problem involves finding a percentile of a function ƒ(x) over inputs x; and the associated decision problem involves deciding whether there is an input x such that ƒ(x)<c for a given value of c. 6. The method of claim 1, wherein: the computational problem involves finding an average, sum, or integral of a function ƒ(x) over inputs x; and the associated decision problem involves deciding whether there is a pair (x, y) for which y<ƒ(x). 7. The method of claim 1, wherein: the computational problem involves finding a conditional expectation p( ) over inputs (x1, . . . , xd), where p(x1, . . . , xd)=ƒ(x1, . . . , xd; averagexd+1p(x1, . . . , xd+1)) for a function ƒ( ) over truncated inputs (x1, . . . , xd; a); and the associated decision problem involves computing averages of p(x1, . . . , xd+1)pi(x1, . . . , xd) and pi(x1, . . . , xd)pj(x1, . . . , xd) over the truncated inputs (x1, . . . , xd+1), where pi and pj are regression functions. 8. The method of claim 1, wherein the associated hashing problem is solvable for a sequence of N inputs using a quantum circuit having a size that is linear or approximately linear in N. 9. The method of claim 1, wherein: the hashing problem is associated with a hash function; and the hash function for a sequence of N inputs is constructed in the quantum computer as a first concatenated hash function with N first components, a permutation, and a second concatenated hash function with N second components. 10. The method of claim 9, wherein: a total number of output qubits for the hash function is b; each of the first components of the first concatenated hash function has approximately (b/N+log2 N) output qubits; and each of the second components of the second concatenated hash function has approximately b/N output qubits. 11. An apparatus comprising: a quantum computer comprising at least one quantum circuit; wherein, to solve a computational problem that is reducible to a problem of counting solutions to an associated decision problem, the quantum computer is configured to: determine, using the at least one quantum circuit, if there is at least one simultaneous solution to both (i) the decision problem and (ii) an associated hashing problem in order to estimate a number of the solutions to the decision problem; and determine, using the at least one quantum circuit, if there are multiple solutions to the decision problem that are simultaneously solutions to the associated hashing problem in order to increase a precision of the estimated number of the solutions to the decision problem; wherein the estimated number of the solutions to the decision problem represents a solution to the computational problem. 12. The apparatus of claim 11, wherein: the hashing problem is associated with a hash function; and each solution to the hashing problem denotes a solution that obtains a specified random hash value from the hash function. 13. The apparatus of claim 11, wherein the associated hashing problem is solved efficiently with the quantum computer but cannot be solved efficiently with a classical computer. 14. The apparatus of claim 11, wherein: the computational problem involves finding a percentile of a function ƒ(x) over inputs x; and the associated decision problem involves deciding whether there is an input x such that ƒ(x)<c for a given value of c. 15. The apparatus of claim 11, wherein: the computational problem involves finding an average, sum, or integral of a function ƒ(x) over inputs x; and the associated decision problem involves deciding whether there is a pair (x, y) for which y<ƒ(x). 16. The apparatus of claim 11, wherein: the computational problem involves finding a conditional expectation p( ) over inputs (x1, . . . , xd), where p(x1, . . . , xd)=ƒ(x1, . . . , xd; averagexd+1p(x1, . . . , xd+1)) for a function ƒ( ) over truncated inputs (x1, . . . , xd; a); and the associated decision problem involves computing averages of p(x1, . . . , xd+1)pi(x1, . . . , xd) and pi(x1, . . . , xd)pj(x1, . . . , xd) over the truncated inputs (x1, . . . , xd+1), where pi and pj are regression functions. 17. The apparatus of claim 11, wherein: the hashing problem is associated with a hash function; and the at least one quantum circuit comprises: a first level of quantum circuits configured to implement a first concatenated hash function using N inputs; and a second level of quantum circuits configured to implement a second concatenated hash function that operates on a permutation of outputs of the first level of quantum circuits. 18. The apparatus of claim 17, wherein: a total number of output qubits for the hash function is b; each of the quantum circuits in the first level has approximately (b/N+log2 N) output qubits; and each of the quantum circuits in the second level has approximately b/N output qubits. 19. The apparatus of claim 11, wherein: the associated decision problem comprises a non-deterministic polynomial time (NP) class problem; and the problem of counting the solutions to the associated decision problem comprises a #P class problem. 20. A system comprising: a quantum computer comprising at least one quantum circuit; and a classical computer comprising at least one processor configured to execute instructions stored in at least one memory; wherein, to solve a computational problem that is reducible to a problem of counting solutions to an associated decision problem, the classical computer is configured to use the quantum computer to: estimate a number of the solutions to the decision problem by determining if there is at least one simultaneous solution to both (i) the decision problem and (ii) an associated hashing problem; and increase a precision of the estimated number of the solutions to the decision problem by determining if there are multiple solutions to the decision problem that are simultaneously solutions to the associated hashing problem; wherein the estimated number of the solutions to the decision problem represents a solution to the computational problem.