Software systems and methods for A-OALP execution on reversible-logic or measurement-based gates
View Patent ↗Advanced output-affecting linear pathways (A-OALPs), each defined as having one or more inputs and a single output, are shown to be continuous, differentiable, single-valued, and either monotonic or constant, permitting the derivation of output complexity as a primary analytic (as are advanced time and advanced space complexity) whose data transformations are equivalent to the data transformations of its associated A-OALP. Single input variable A-OALPs and output complexities are reversible when monotonic. Advanced output vectors enable reversibility of single- or multiple-input A-OALPs and their execution on reversible-logic gates. Linked A-OALPs may form reversible directed acyclic graphs (DAGs) representing neural networks and other algorithmic structures that are compressible, reversible, and quantum-executable. Advanced output vectors further enable A-OALP and A-OALP network reconstruction and parallel execution using dynamic loop-unrolling parallelism, the associative parallel principle, A-OALP graph-based parallelism, and superposition parallelism to achieve energy-efficient, reversible, and quantum-compatible computation.
1 . A method for optimization of software or one or more algorithms, comprising:
receiving the software or the one or more algorithms, from an operator;
decomposing the software or the one or more algorithms into one or more executable and analyzable advanced output-affecting linear pathways (A-OALPs), wherein each of the one or more A-OALPs includes runtime information generation for one or more instances;
executing the one or more A-OALPs;
receiving one or more input variable attribute values in a source values table;
comparing the one or more input variable attribute values that correspond to a pathway selection entry in an A-OALP selection table, wherein the A-OALP selection table uses the one or more input variable attribute values;
selecting at least one A-OALP that corresponds to the pathway selection;
determining which of the one or more input variable attribute values vary a processing time of the selected at least one A-OALP;
determining which of the one or more input variable attribute values vary memory allocation of the selected at least one A-OALP;
determining whether at least one output complexity value of the selected at least one A-OALP is monotonic, continuous, differentiable, or single-valued;
determining at least one advanced output vector of the selected at least one A-OALP;
determining at least one superposed plane wave form of the at least one output complexity value of the selected at least one A-OALP;
enabling reversibility of the selected at least one A-OALP by use of the at least one advanced output vector; and
executing the reversible selected at least one A-OALP.
2 . The method of claim 1 , wherein the execution is on a reversible logic gate.
3 . The method of claim 1 , wherein the execution is on a quantum circuit.
4 . The method of claim 3 , wherein the quantum circuit is a measurement-based quantum circuit.
5 . The method of claim 1 , wherein the at least one advanced output vector comprises at least one of quadrant data, monotonicity data, or full-state metadata.
6 . The method of claim 1 , wherein the at least one advance output vector provides algorithmic compression.
7 . The method of claim 1 , wherein the at least one output complexity value is executed on a measurement-based quantum circuit.
8 . The method of claim 1 , further comprising executing the selected at least one A-OALP within a processing framework selected from at least one of a serial A-OALP framework, a parallel A-OALP framework, a reversible parallel A-OALP framework, or a context-aware A-OALP framework.
9 . A system for optimization of software or one or more algorithms, comprising:
a memory; and
a processor operatively coupled with the memory, wherein the processor is configured to execute a program code to:
receive the software or the one or more algorithms, from an operator;
decompose the software or the one or more algorithms into one or more executable and analyzable advanced output-affecting linear pathways (A-OALPs), wherein each of the one or more A-OALPs includes runtime information generation for one or more instances;
execute the one or more A-OALPs;
receive one or more input variable attribute values in a source values table;
compare the one or more input variable attribute values that correspond to a pathway selection entry in an A-OALP selection table, wherein the A-OALP selection table uses the one or more input variable attribute values;
select at least one A-OALP that corresponds to the pathway selection;
determine which of the one or more input variable attribute values vary a processing time of the selected at least one A-OALP;
determine which of the one or more input variable attribute values vary memory allocation of the selected at least one A-OALP;
determine whether at least one output complexity value of the selected at least one A-OALP is monotonic, continuous, differentiable, or single-valued;
determine at least one advanced output vector of the selected at least one A-OALP; and
determine at least one superposed plane wave form of the at least one output complexity value of the selected at least one A-OALP;
enable reversibility of the selected at least one A-OALP by use of the at least one advanced output vector; and
execute the reversible selected at least one A-OALP.
10 . The system of claim 9 , wherein the execution is on a reversible logic gate.
11 . The system of claim 9 , wherein the execution is on a quantum circuit.
12 . The system of claim 11 , wherein the quantum circuit is a measurement-based quantum circuit.
13 . The system of claim 9 , wherein the at least one advanced output vector comprises at least one of quadrant data, monotonicity data, or full-state metadata.
14 . The system of claim 9 , wherein the at least one advance output vector provides algorithmic compression.
15 . The system of claim 9 , wherein the at least one output complexity value is executed on a measurement-based quantum circuit.
16 . The system of claim 9 , wherein the processor is further configured to execute the program code to execute the selected at least one A-OALP within a processing framework selected from at least one of a serial A-OALP framework, a parallel A-OALP framework, a reversible parallel A-OALP framework, or a context-aware A-OALP framework.