Abstract: A quantum-enhanced system and method for natural language processing (NLP) for generating a word embedding on a hybrid quantum-classical computer. A training set is provided on the classical computer, wherein the training set provides at least one pair of words, and at least one binary value indicating the correlation between the pair of words. The quantum computer generates quantum state representations for each word in the pair of words. The quantum component evaluates the quantum correlation between the quantum state representations of the word pair using an engineering likelihood function and a Bayesian inference. Training the word embedding on the quantum computer is provided using an error function containing the binary value and the quantum correlation.

Abstract: A quantum contextual measurement is generated from a quantum device capable of performing continuous time evolution, by generating a first measurement result and a second measurement result and combining the first measurement result and the second measurement result to generate the quantum contextual measurement. The first measurement result may be generated by initializing the quantum device to a first initial quantum state, applying a first continuous time evolution to the first initial state to generate a first evolved state, and measuring the first evolved state to generate the first measurement result. A similar process may be applied to generate a second evolved state which is at least approximately equal to the first evolved state, and then applying another continuous time evolution to the second evolved state to generate a third evolved state, and measuring the third evolved state to generate the second measurement result.

Abstract: The present disclosure relates to a polyurethane foam composition, comprising (A) a polyol component, comprising one or more polyols selected from the group consisting of a polyester polyol, a polyether polyol, and the combination thereof, wherein the one or more polyols have an average hydroxyl group functionality of from 3 to 7 and an average hydroxyl group number of from 300 to 1000 mg KOH/g; and (B) an isocyanate component, comprising one or more isocyanate compounds; wherein in either or both of the (A) polyol component and the (B) isocyanate component, the polyurethane foam composition further comprises one or more flame retardants in an amount of no more than 15 percent by weight based on the total weight of the polyurethane foam composition, and, one or more blowing agents; and wherein the NCO/OH ratio of the isocyanate component to the polyol component is within the range of from 0.5:1 to 5:1.

Abstract: The application relates to N-(4-fluoro-3-(6-(3-methylpyridin-2-yl)-[1,2,4]triazolo[1,5-a]pyrimidin-2-yl)phenyl)-2,4-dimethyloxazole-5-carboxamide (Compound I) fumaric acid co-crystals and X-ray amorphous complexes of Compound (I) and fumaric acid. The application also provides methods of making the same; pharmaceutical compositions comprising them; and their use in treating, preventing, inhibiting, ameliorating, or eradicating the pathology and/or symptomology of a disease caused by a kinetoplastid parasite, such as leishmaniasis, human African trypanosomiasis and Chagas disease.

Abstract: A quantum computer directs an amplitude of a qubit to be proportional to the value of a function g of N variables {right arrow over (xk)} by: (A) initializing M+1 qubits on the quantum computer, the M+1 qubits comprising: (1) a target qubit t having an amplitude of a reference state; and (2) a control register with M qubits {ql}; and (B) changing the value of the amplitude of the reference state on the target qubit t, the changing comprising: (B)(1) applying a sequence of SU(2) gates to the target qubit t, the sequence of SU(2) gates comprising M controlled quantum gates Gi and at least one rotation parameter, wherein at least one qubit of the control register acts as a control qubit for the controlled quantum gate Gi; and (B)(2) tuning the at least one rotation parameter until a halting criterion based on the amplitude of the reference state is satisfied.

Abstract: A method and apparatus for generating quantum-enhanced learning agents that can be used for optimizing tasks such as time series analysis, natural language processing, reinforcement learning, and combinatorial optimization. The method may be implemented on a hybrid quantum-classical computer. A learning agent is defined having an initial state S1, a set of parameters T1, and an input X1. The set of parameters are updated iteratively based on the input X1. The updated parameter set is generated, the agent state is updated, and an output is generated. Further enhancements include unrolling the agent in time and maintaining multiple copies of the agent across multiple iterations and entangling the copies of the agents. The disclosed technology may be used for computer chip design optimization for arranging chip components on a substrate, where circuit board parameters are efficiently assembled piece by piece, instead of a single optimization solution.

Abstract: A hybrid quantum-classical (HQC) computer takes advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. The HQC computer derives inspiration from quantum metrology, phase estimation, and the more recent “alpha-VQE” proposal, arriving at a general formulation that is robust to error and does not require ancilla qubits. The HQC computer uses the “engineered likelihood function” (ELF)to carry out Bayesian inference. The ELF formalism enhances the quantum advantage in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.

Abstract: A quantum optimization system and method estimate, on a classical computer and for a quantum state, an expectation value of a Hamiltonian, expressible as a linear combination of observables, based on expectation values of the observables; and transform, on the classical computer, one or both of the Hamiltonian and the quantum state to reduce the expectation value of the Hamiltonian.

Abstract: Model-free error correction in quantum processors is provided, allowing tailoring to individual devices. In various embodiments, a quantum circuit is configured according to a plurality of configuration parameters. The quantum circuit comprises an encoding circuit and a decoding circuit. Each of a plurality of training states is input to the quantum circuit. The encoding circuit is applied to each of the plurality of training states and to a plurality of input syndrome qubits to produce encoded training states. The decoding circuit is applied to each of the encoded training states to determine a plurality of outputs. A fidelity of the quantum circuit is measured for the plurality of training states based on the plurality of outputs. The fidelity is provided to a computing node. The computing node determines a plurality of optimized configuration parameters. The optimized configuration parameters maximize the accuracy of the quantum circuit for the plurality of training states.

Abstract: A quantum computer includes an efficient and exact quantum circuit for performing quantum state compression.

Abstract: A quantum-enhanced system and method for natural language processing (NLP) for generating a word embedding on a hybrid quantum-classical computer. A training set is provided on the classical computer, wherein the training set provides at least one pair of words, and at least one binary value indicating the correlation between the pair of words. The quantum computer generates quantum state representations for each word in the pair of words. The quantum component evaluates the quantum correlation between the quantum state representations of the word pair using an engineering likelihood function and a Bayesian inference. Training the word embedding on the quantum computer is provided using an error function containing the binary value and the quantum correlation.

Abstract: A computer optimizes transport of a set of ingredients between a plurality of sources, at least one terminal, and a plurality of pools, described by an objective function, a set of variables, and a set of constraints, by: (A) transforming the objective function, variables, and constraints into a binary cost function, including: discretizing the set of variables into a set of a binary variables; transforming the objective function into a binary cost function of the set of binary variables; and adding, for each constraint in the set of constraints, one or more terms to the binary cost function, to create a completed cost function; and (B) providing the completed cost function to a solver to obtain a solution or approximate solution representing a flow of the set of ingredients between the plurality of sources, the plurality of pools, and the at least one terminal.

Abstract: A method evolves a lattice of qubits in a quantum computer. The lattice of qubits includes a first plurality of qubits and a second plurality of qubits in the quantum computer. Each qubit in the first plurality of qubits is adjacent to at least one qubit in the second plurality of qubits. The method includes: (A) applying, in parallel, a first set of quantum gates between the first plurality of qubits and the second plurality of qubits to create a first set of entangled pairs of qubits; (B) after (A), swapping, in parallel, pairs of qubits, the swapping comprising: (B) (1) swapping pairs of adjacent qubits in the first plurality of qubits according to a first swap criterion; and (B) (2) swapping pairs of adjacent qubits in the second plurality of qubits according to a second swap criterion, wherein the second swap criterion differs from the first swap criterion.

Abstract: A hybrid quantum-classical (HQC) computer takes advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. The HQC computer derives inspiration from quantum metrology, phase estimation, and the more recent “alpha-VQE” proposal, arriving at a general formulation that is robust to error and does not require ancilla qubits. The HQC computer uses the “engineered likelihood function” (ELF) to carry out Bayesian inference. The ELF formalism enhances the quantum advantage in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.

Abstract: A hybrid quantum-classical (HQC) computer prepares a quantum Boltzmann machine (QBM) in a pure state. The state is evolved in time according to a chaotic, tunable quantum Hamiltonian. The pure state locally approximates a (potentially highly correlated) quantum thermal state at a known temperature. With the chaotic quantum Hamiltonian, a quantum quench can be performed to locally sample observables in quantum thermal states. With the samples, an inverse temperature of the QBM can be approximated, as needed for determining the correct sign and magnitude of the gradient of a loss function of the QBM.

Abstract: A computer system, designed according to a particular architecture, compiles and execute a general quantum program. Computer systems designed in accordance with the architecture are suitable for use with a variety of programming languages and a variety of hardware backends. The architecture includes a classical computer and a quantum device (which may be remote from the local computer) which includes both classical execution units and a quantum processing unit (QPU).

Abstract: A quantum contextual measurement is generated from a quantum device capable of performing continuous time evolution, by generating a first measurement result and a second measurement result and combining the first measurement result and the second measurement result to generate the quantum contextual measurement. The first measurement result may be generated by initializing the quantum device to a first initial quantum state, applying a first continuous time evolution to the first initial state to generate a first evolved state, and measuring the first evolved state to generate the first measurement result. A similar process may be applied to generate a second evolved state which is at least approximately equal to the first evolved state, and then applying another continuous time evolution to the second evolved state to generate a third evolved state, and measuring the third evolved state to generate the second measurement result.

Abstract: A method and system are provided for modeling the relative performance of algorithms, including quantum algorithms, over a set of problem instances. The model, referred to as a performance estimator, is generated from a selected algorithm and a set a set of problem instances as input, resulting in a generated model. Unlike prior methods, which model the performance of a fixed algorithm on a set of instances, embodiments of the present technology produce a performance estimate without needing to explicitly model the underlying algorithm. The model, once generated by the disclosed technology, may then be utilized to estimate the performance of new algorithms that the model has not been trained on.

Abstract: Mapping of logical qubits to physical qubits is provided. In various embodiments, a first candidate subgraph is selected from a hardware graph. The hardware graph represents a physical quantum circuit. The hardware graph comprises a plurality of nodes corresponding to physical qubits and a plurality of edges corresponding to coupling among the plurality of qubits.

Abstract: A hybrid quantum-classical computer solves systems of equations and eigenvalue problems utilizing non-unitary transformations on the quantum computer. The method may be applied, for example, to principal component analysis, least squares fitting, regression, spectral embedding and clustering, vibrations in mechanics, fluids and quantum chemistry, material sciences, electromagnetism, signal processing, image segmentation and data mining.