International Business Machines Corporation

Rudy Raymond Harry Putra

Takayuki Osogami

Marco Pistoia

Jay Michael Gambetta

Stephen Peter Wood

Shaohan Hu

Takashi Imamichi

Sakyasingha Dasgupta

Antonio Mezzacapo

Ikko Hamamura

Marco Aurelio Stelmar Netto

Takayuki Katsuki

Marcos Dias De Assuncao

Sergey Bravyi

Bruno Flach

Maira A De C Gatti

Hiroshi Horii

Tetsuro Morimura

Richard Chen

Satoshi Hara

Yohei Ikawa

Nikolaj Moll

Peng Liu

Julia Elizabeth Rice

Hidemasa Muta

Ivano Tavernelli

Panagiotis Barkoutsos

Muhammad Anis Uddin Nasir

VQE is accelerated by performing receiving a qubit Hamiltonian representing a linear combination of a plurality of Pauli strings. Selecting, among the plurality of Pauli strings, one or more Pauli strings that have less influence than a threshold on an eigenvalue of the qubit Hamiltonian. Grouping, based on joint measurability, the unselected Pauli strings among the plurality of Pauli strings into a plurality of groups of jointly measurable Pauli strings Determining that one or more of the selected one or more Pauli strings is jointly measurable with Pauli strings in one of the plurality of groups And adding one or more of the selected one or more Pauli strings to the one of the plurality of groups.

Selection of pauli strings for variational quantum eigensolver

A method for a determinantal Point Process-based prediction includes obtaining, using a hardware processor, a training data set stored on one or more computer readable storage mediums operably coupled to the hardware processor, training an asymmetric kernel of a Determinantal Point Process (DPP) from a training data set by calculating an inverse matrix of a sum of the asymmetric kernel and an identity matrix in a recursive manner to reduce time and computational resources utilized, and determining a prediction model by training the asymmetric kernel as at least part of a prediction model to make a prediction.

Training asymmetric kernels of determinantal point processes

Systems, computer-implemented methods, and computer program products to facilitate estimation of an expected energy value of a Hamiltonian based on data of the Hamiltonian, the quantum state produced by a quantum device and/or entangled measurements 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 a selection component that selects a quantum state measurement basis having a probability defined based on a ratio of a Pauli operator in a Hamiltonian of a quantum system. The computer executable components can further comprise a measurement component that captures a quantum state measurement of a qubit in the quantum system based on the quantum state measurement basis.

Estimation of an expected energy value of a Hamiltonian

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.

Automatic quantum searching of object databases

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.

Adaptive quantum circuit construction for multiple-controlled-not gates

Selection of Pauli strings for Variational Quantum Eigensolver

An apparatus for implementing a computing system to predict preferences includes at least one processor device operatively coupled to a memory. The at least one processor device is configured to calculate a parameter relating to a density of a prior distribution at each sample of a set of samples associated with the prior distribution. The at least one parameter including a distance from each sample to at least one neighboring sample. The at least one processor device is further configured to estimate, for the plurality of samples, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample. The estimation is performed without sampling the at least one posterior distribution to reduce consumption of resources of the computing system.

Computational estimation of a characteristic of a posterior distribution

Adaptive quantum circuit construction for multiple-controlled-NOT gates

Methods and systems for selecting and performing group actions include selecting parameters for an approximated action-value function, which determines a reward value associated with a particular group action taken from a particular state, using a determinant of a parameter matrix for the action-value function. A group action is selected using the approximated action-value function and the selected parameters. Agents are triggered to perform respective tasks in the group action.

Determinantal reinforced learning in artificial intelligence

A method is provided for commodity management. The method generates, using a Dynamic Boltzmann Machine (DyBM), a future mean prediction and a future standard deviation prediction of a financial time-series dataset for a commodity. The method measures, using Hellinger Distance (HD), an accuracy of the future mean prediction and the future standard deviation prediction. The method combines the future mean prediction and the future standard deviation prediction with the Hellinger Distance to determine a DyBM trustworthy prediction time period in which predictions by the DyBM, including the future mean prediction and the future standard deviation prediction, are deemed trustworthy. The method selectively performs an action relating to an ownership of the commodity based on at least one of the future mean prediction and the future standard deviation prediction, responsive to the future mean prediction and the future standard deviation prediction being generated during the DyBM trustworthy prediction time period.

Hellinger distance for measuring accuracies of mean and standard deviation prediction of dynamic Boltzmann machine

A computer-implemented method includes employing a dynamic Boltzmann machine (DyBM) to predict a higher-order moment of time-series datasets. The method further includes acquiring the time-series datasets transmitted from a source node to a destination node of a neural network including a plurality of nodes, learning, by the processor, a time-series generative model based on the DyBM with eligibility traces, and obtaining, by the processor, parameters of a generalized auto-regressive heteroscedasticity (GARCH) model to predict a time-varying second-order moment of the times-series datasets.

Dynamic Boltzmann machine for estimating time-varying second moment

A computer-implemented method is provided for autonomously making continuous trading decisions for assets using a first eligibility trace enabled Neural Network (NN). The method includes pretraining the first eligibility trace enabled NN, using asset price time series data, to generation predictions of future asset price time series data. The method further includes initializing a second eligibility trace enabled NN for reinforcement learning using learned parameters of the first eligibility trace enabled NN. The method also includes augmenting state information of the second eligibility trace enabled NN for reinforcement learning using an output from the first eligibility trace enabled NN. The method additionally includes performing continuous actions for trading assets at each of multiple time points.

Autonomous trading with memory enabled neural network learning

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Yokohama, Japan

Rudy Raymond Harry Putra