International Business Machines Corporation

Peng Liu

Marco Pistoia

Shaohan Hu

Richard Chen

Stephen Peter Wood

Antonio Mezzacapo

Jay Michael Gambetta

Omer Tripp

John A Gunnels

Arthur Giuseppe Rattew

Rudy Raymond Harry Putra

Ali Javadiabhari

Nikolaj Moll

Ismael Faro Sertage

Julia Elizabeth Rice

Ivano Tavernelli

Francisco Jose Martin Fernandez

Yunhui Zheng

Panagiotis Barkoutsos

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.

Quantum reinforcement learning agent

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.

Monte Carlo Markov chain based quantum program optimization

A test sequence generation method, system, and computer program product, include creating an &#x3b5;-greedy policy from a recurrent neural network (RNN) model to prioritize an action from an action sequence.

System, method and recording medium for generating mobile test sequences

A hybrid data processing environment comprising a classical computing system and a quantum computing system is configured. A configuration of a first quantum circuit is produced from the classical computing system, the first quantum circuit being executable using the quantum computing system. Using the quantum computing system, the first quantum circuit is executed. Using a pattern recognition technique, a portion of the first quantum circuit that can be transformed using a first transformation operation to satisfy a constraint on the quantum circuit design is identified. The portion is transformed to a second quantum circuit according to the first transformation operation, wherein the first transformation operation comprises reconfiguring a gate in the first quantum circuit such that a qubit used in the gate complies with the constraint on the quantum circuit design while participating in the second quantum circuit. Using the quantum computing system, the second quantum circuit is executed.

Quantum circuit optimization using machine learning

Symbolic backend for execution of quantum programs is provided. A parser receives an input qasm and parses it as a circuit graph. A layering component segments the circuit graph into multiple layers. An evaluation engine reads respective layers, translates the respective layers into a mathematic expression over qubits, and performs a simplification of the input qasm. A checker determines whether the input qasm and the simplified qasm are equivalent.

Symbolic backend for execution of quantum programs

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.

Simplified quantum programming

Synthesizing sanitization code for applications based upon a probabilistic prediction model includes receiving a set of applications. The set of applications is partitioned into a first subset of applications and a second subset of applications. The first subset has one or more malicious payloads associated therewith, and the second subset has one or more non-malicious payloads associated therewith. A probabilistic prediction model is computed based upon the malicious payloads associated with the first subset of applications. One or more predicted malicious payloads are predicted from the probabilistic prediction model.

Synthesizing sanitization code for applications based upon probabilistic prediction model

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.

Quantum algorithm concatenation

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.

Parallelization of numeric optimizers

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.

Quantum computations of classical specifications

The subject disclosure relates generally to an automated testing tool for quantum software development kits (SDKs). A system in accordance with an embodiment comprises a memory that stores computer-executable components. A processor is operably coupled to the memory and executes the computer-executable components stored in the memory. The computer-executed components comprises: a transformation component that receives a qasm program and transforms the qasm program; a testing component that tests the transformed qasm program on the SDK; and a reporting component that reports whether a quantum SDK has functioned properly or failed for the transformed qasm program.

Fuzz testing for quantum SDK

A test sequence generation method, system, and computer program product, include collecting an action sequence, training a recurrent neural network (RNN) model to encode a frequency of actions in the action sequence and determine meaningful action sequences, and applying the RNN model to prioritize the meaningful action sequences that have a frequency less than a predetermined threshold following the action sequence.

Generating mobile test sequences

A user interface (UI)-level clone detection method, system, and computer program product, include running applications from an application database to obtain a screenshot of each of the applications, comparing a first object of a first screenshot of a first application with a second object from a second screenshot of a second application to determine a similarity between the first object and the second object, and analyzing a code for each of the first object and the second object when the similarity is greater than a predetermined threshold value to identify a same-functionality code.

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Yorktown Heights, NY, United States of America

Peng Liu