Abstract: A method includes measuring an amplitude of a state of a quantum circuit, the amplitude corresponding to a first location in an object database. In the embodiment, the method includes executing, using a classical processor and a first memory, a verification operation, responsive to measuring the amplitude, to verify a target object in the first location. In the embodiment, the method includes re-measuring a second amplitude of a second state of the quantum circuit, the second amplitude having undergone a first plurality of amplitude amplifications, the second amplitude corresponding to a second location in the object database, the second location being verified as the target object, and wherein a total number of the first plurality of amplitude amplifications being less than a square root of a set of objects in the object database.

Abstract: Techniques regarding quantum algorithm concatenation are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a concatenation component, operatively coupled to the processor, that can concatenate a first quantum algorithm and a second quantum algorithm by using an output of the first quantum algorithm as an initial parameter in the second quantum algorithm.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate applying a reinforcement learning policy to available actions are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a state encoder that maps, based on one or more encoding parameters, a state of an environment on to one or more qubits of a quantum device. The system can further comprise a variational component that combines a reinforcement learning policy with a sampling of the one or more qubits, resulting, based on one or more variational parameters, in a probability distribution of a plurality of available actions at the state of the environment.

Abstract: In an embodiment, a method of estimating the cost of a software project comprising receiving natural language software descriptions and electronic source code files for respective completed software projects; storing, in a computer memory, an input set of functional labels and size data extracted from each of the source code files, the functional labels corresponding to labels in a software development library; training a natural language processing model to output a project set of functional labels for one of the source code files; training a regression analysis model to output a project size for the one of the source code files; predicting, using the natural language understanding model, a proposal set of functional labels; predicting, using the regression analysis model, a proposal size; and using the proposal size to calculate a proposal cost; and preparing a software development proposal that includes a natural language proposed-software description and the proposal cost.

Abstract: A method for parallelization of a numeric optimizer includes detecting an initialization of a numeric optimization process of a given function. The method computes a vector-distance between an input vector and a first neighbor vector of a set of neighbor vectors. The method predicts, using the computed vector-distance, a subset of the set of neighbor vectors. The method pre-computes, in a parallel processing system, a set of evaluation values in parallel, each evaluation value corresponding to one of the subset of the set of neighbor vectors. The method detects a computation request from the numeric optimization process, the computation request involving at least one of the set of evaluation values. The method supplies, in response to receiving the computation request, and without performing a computation of the computation request, a parallelly pre-computed evaluation value from the set of evaluation values to the numeric optimization process.

Abstract: A quantum computational device includes a plurality n of qubits, each qubit comprising at least two quantum states and having associated therewith 2n product states. The quantum computational device includes an initialization circuit configured to receive up to 2n input values and to associate the up to 2n input values with up to 2n of the 2n product states to provide an input vector. The quantum computational device includes a processing circuit configured to communicate with the initialization circuit to receive the input vector and to provide an output value based on the input vector.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate quantum domain computation of classical domain specifications are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an input transformation component that can be adapted to receive one or more types of domain-specific input data corresponding to at least one of a plurality of domains. The input transformation component can transform the one or more types of domain-specific input data to quantum-based input data. The computer executable components can further comprise a circuit generator component that, based on the quantum-based input data, can generate a quantum circuit.

Abstract: A method for parallelization of a numeric optimizer includes detecting an initialization of a numeric optimization process of a given function. The method computes a vector-distance between an input vector and a first neighbor vector of a set of neighbor vectors. The method predicts, using the computed vector-distance, a subset of the set of neighbor vectors. The method pre-computes, in a parallel processing system, a set of evaluation values in parallel, each evaluation value corresponding to one of the subset of the set of neighbor vectors. The method detects a computation request from the numeric optimization process, the computation request involving at least one of the set of evaluation values. The method supplies, in response to receiving the computation request, and without performing a computation of the computation request, a parallelly pre-computed evaluation value from the set of evaluation values to the numeric optimization process.

Abstract: A method for parallelization of a numeric optimizer includes detecting an initialization of a numeric optimization process of a given function. The method computes a vector-distance between an input vector and a first neighbor vector of a set of neighbor vectors. The method predicts, using the computed vector-distance, a subset of the set of neighbor vectors. The method pre-computes, in a parallel processing system, a set of evaluation values in parallel, each evaluation value corresponding to one of the subset of the set of neighbor vectors. The method detects a computation request from the numeric optimization process, the computation request involving at least one of the set of evaluation values. The method supplies, in response to receiving the computation request, and without performing a computation of the computation request, a parallelly pre-computed evaluation value from the set of evaluation values to the numeric optimization process.

Abstract: In an embodiment, a method includes measuring a first number of control qubits in a quantum algorithm, wherein a quantum circuit representation of the quantum algorithm includes a multiple-controlled-NOT gate. In an embodiment, a method includes measuring a second number of ancilla qubits in a quantum computer. In an embodiment, a method includes comparing the first number and the second number to determine an optimum compilation method for a quantum circuit. In an embodiment, a method includes compiling, in response to the comparison determining the second number is greater than one and less than the difference of the first number and 2, a quantum circuit from the quantum algorithm using a hybrid method.

Abstract: A method includes measuring an amplitude of a state of a quantum circuit, the amplitude corresponding to a first location in an object database. In the embodiment, the method includes executing, using a classical processor and a first memory, a verification operation, responsive to measuring the amplitude, to verify a target object in the first location. In the embodiment, the method includes re-measuring a second amplitude of a second state of the quantum circuit, the second amplitude having undergone a first plurality of amplitude amplifications, the second amplitude corresponding to a second location in the object database, the second location being verified as the target object, and wherein a total number of the first plurality of amplitude amplifications being less than a square root of a set of objects in the object database.

Abstract: A method for parallelization of a numeric optimizer includes detecting an initialization of a numeric optimization process of a given function. The method computes a vector-distance between an input vector and a first neighbor vector of a set of neighbor vectors. The method predicts, using the computed vector-distance, a subset of the set of neighbor vectors. The method pre-computes, in a parallel processing system, a set of evaluation values in parallel, each evaluation value corresponding to one of the subset of the set of neighbor vectors. The method detects a computation request from the numeric optimization process, the computation request involving at least one of the set of evaluation values. The method supplies, in response to receiving the computation request, and without performing a computation of the computation request, a parallelly pre-computed evaluation value from the set of evaluation values to the numeric optimization process.

Abstract: In a quantum circuit, an inversion gate is implemented with two controls each connected to a control qubit. In the quantum circuit responsive to determining that each control of the inversion gate is currently connected to a control qubit, an ancilla gate is implemented, a first control of the ancilla gate connected to a control qubit moved from a first control of the inversion gate, a second control of the ancilla gate connected to a control qubit selected from the set of unplaced control qubits, a target of the ancilla gate connected to the first control of the inversion gate. At a gate implemented in the quantum circuit responsive to determining that the gate has a maximum number of controls not connected to control qubits, a control of the gate is connected to a control qubit selected from the set of unplaced control qubits.

Abstract: Techniques regarding quantum algorithm concatenation are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a concatenation component, operatively coupled to the processor, that can concatenate a first quantum algorithm and a second quantum algorithm by using an output of the first quantum algorithm as an initial parameter in the second quantum algorithm.

Abstract: From a quantum program a first mutant is generated using a processor and a memory, where the first mutant is a randomly-generated transformation of the quantum program. A quality score, a correctness distance, and a probability of acceptance corresponding to the first mutant are computed. An acceptance corresponding to the first mutant is determined according to the probability of acceptance. Upon determining that an acceptance of the first mutant corresponding to the probability of acceptance exceeds an acceptance threshold, the quantum program is replaced with the first mutant. Upon determining that the quality score exceeds a storage threshold and that the correctness distance is zero, the first mutant is stored. These actions are iterated until reaching an iteration limit.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate quantum domain computation of classical domain specifications are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an input transformation component that can be adapted to receive one or more types of domain-specific input data corresponding to at least one of a plurality of domains. The input transformation component can transform the one or more types of domain-specific input data to quantum-based input data. The computer executable components can further comprise a circuit generator component that, based on the quantum-based input data, can generate a quantum circuit.

Abstract: Techniques facilitating simplified quantum programming are provided. In one example, a computer-implemented method comprises reducing, by a device operatively coupled to a processor, a first computing problem of a problem type to a second computing problem of the problem type, wherein the second computing problem is associated with a quantum circuit; facilitating, by the device, execution of the quantum circuit at a quantum computer, resulting in a first output corresponding to the second computing problem; and mapping, by the device, the first output to a second output corresponding to the first computing problem.