International Business Machines Corporation

Jsr Corporation

United Kingdom Research and Innovation

Edward Oliver Pyzer-Knapp

Mario Motta

Stephen Peter Wood

Gavin O Jones

Ivano Tavernelli

Panagiotis Barkoutsos

Michael Johnston

Joseph Latone

Pauline Ollitrault

Richard Anderson

Yuya Onishi

Clyde Fare

Systems, computer-implemented methods and/or computer program products that can facilitate providing a defined parameter, determining whether to employ the defined parameter for a variational quantum algorithm, and running the variational quantum algorithm on a quantum system, 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 decision component that determines, based upon an uncertainty prediction regarding the usability of the defined parameter that has been output from a machine learning model, whether to employ the defined parameter in a variational quantum algorithm, such as run on a quantum system.

Uncertainty aware parameter provision for a variational quantum algorithm

Method and system are provided for efficiently populating a phase diagram for modeling of multiple substances. The method may include defining an n-way phase diagram with data points each being an n-tuple describing the n substance inputs, wherein the n-way phase diagram is defined at a user-configured resolution. The method may select an initial subset of data points and calculate their contribution to the phase diagram. The method may then generate a Bayesian model based on the initial subset of calculated data points and predicting the resultant phase and an associated uncertainty of all the uncalculated data points in the defined phase diagram. The method may select a sample subset of the data points using maximum entropy sampling and calculating a resultant phase for each of the selected data points, and incorporate the calculated phases into the Bayesian model. Re-modeling the Bayesian model may predict the resultant phase and an associated uncertainty of all the remaining uncalculated data points in the defined phase diagram. Repeating the selecting a sample subset of the data points using maximum entropy sampling and re-modeling is carried out until a defined termination criterion is met.

Efficiently populating a phase diagram for multiple substances

Real-time intervention of an industrial process can include searching for a batch of candidate configurations for use by the industrial process, the batch of candidate configurations searched for by performing a batch Bayesian optimization (BBO). The batch of candidate configurations is transmitted to the industrial process to use in running the industrial process. A result of the run is received from the industrial process. Using the result in the BBO, a next batch of candidate configurations is searched. Whether a stopping criterion is met is determined, based on the next batch of candidate configurations and by applying a function to a BBO acquisition score. Responsive to determining that the stopping criterion is met, searching for the next batch of candidates is terminated.

Early experiment stopping for batch Bayesian optimization in industrial processes

Systems and methods that facilitate motion formalism utilizing quantum computing, to compute matrix operators in terms of commutators between qubit operators and measurements on the quantum hardware, wherein the commutators are computed utilizing symbolic calculus. Embodiments reduce computational cost of generalized eigenvalue synthesis relying on symbolic calculus and parallelization. Embodiments disclosed herein can also develop estimators of excited-states properties, considering constants of motion (e.g. spin) and non-constants of motions (e.g. dipoles, density matrices).

System-based extension of qEOM algorithm for quantum computation of excited-state properties

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Runcorn, United Kingdom

Edward Oliver Pyzer-Knapp