International Business Machines Corporation

The Boeing Company

Jsr Corporation

Mario Motta

Joseph Latone

Edward Oliver Pyzer-Knapp

Stephen Peter Wood

Julia Elizabeth Rice

Gavin O Jones

Ivano Tavernelli

Nam H Nguyen

Panagiotis Barkoutsos

Michael Johnston

Yuya Jeremy Ong

Pauline Ollitrault

Tanvi Pradeep Gujarati

Yuya Onishi

Barbara Anne Jones

Systems, computer-implemented methods, and/or computer program products to facilitate reduction of a quantum circuit are provided. A computer-implemented method can comprise performing, by a system operatively coupled to at least one processor, decomposition of an exponential of a first Pauli operator of 1 to n Pauli operators of the quantum circuit, and performing, by the system, a first Clifford transformation of a primary operator of the quantum circuit, where the primary operator can comprise a linear combination of primary Pauli operators, and where the first Clifford transformation can employ a result of the decomposition. Performing the decomposition can comprise decomposing the exponential of the first Pauli operator into a first non-Clifford operator and a first Clifford operator, with the first Clifford operator being employed for the first Clifford transformation. Additional decompositions and respective transformations can be performed iteratively until decomposition of all exponentials of the 1 to n Pauli operators.

Circuit reduction for exponentials of Pauli operators

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

Systems, computer-implemented methods, and computer program products to facilitate generalization of a quantum imaginary time evolution process and simulation by tensor networks are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a universal component that generalizes a quantum imaginary time evolution process to provide a quantum imaginary time evolution ansatz process. The computer executable components can further comprise a simulation component that applies the quantum imaginary time evolution ansatz process to one or more general multi-body quantum systems.

Generalization of a quantum imaginary time evolution process and simulation by tensor networks

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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San Jose, CA, United States of America

Mario Motta