International Business Machines Corporation

Jsr Corporation

Panagiotis Barkoutsos

Ivano Tavernelli

Stefan Woerner

Pauline Ollitrault

Marco Pistoia

Stephen Peter Wood

Antonio Mezzacapo

Giacomo Nannicini

Anton Robert

Jay Michael Gambetta

Richard Chen

Alessandro Curioni

Shaohan Hu

Rudy Raymond Harry Putra

Nikolaj Moll

Peng Liu

Gavin O Jones

Julia Elizabeth Rice

Daniel Josef Egger

Joseph Latone

Edward Oliver Pyzer-Knapp

Mario Motta

Christa Zoufal

Fotios Gkritsis

Jakob Max Guenther

Francesco Tacchino

James Robin Wootton

Yuya Onishi

Max Rossmannek

Embodiments are provided for error mitigation in quantum programs. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components include a compilation component that causes encoding of one or more qubits according to a circular repetition code at a time after operations on the one or more qubits and before readout.

Mitigation of readout error in a quantum computation

Techniques facilitating density-functional theory determinations using a quantum computing system are provided. A system can comprise a first computing processor and a second computing processor. The first computing processor can generate a density-functional theory determination. The second computing processor can input a quantum density into the density-functional theory determination. The first computing processor can be operatively coupled to the second computing processor. Further, the first computing processor can be a classical computer and the second computing processor can be a quantum computer.

Density-functional theory determinations using a quantum computing system

Techniques for facilitating utilizing a quantum computing circuit in conjunction with a stochastic control problem are provided. In one embodiment, a system is provided that comprises a quantum computing circuit that prepares a quantum state that represents a stochastic control problem. The system can further comprise a classical computing device that determines parameters for the quantum computing circuit.

Stochastic control with a quantum computer

A method of designing a molecule for an environment of interest using a quantum computer includes providing a linear superposition of a plurality of molecular species, the plurality of molecular species being initially weighted by equal initial coefficients; determining a lowest-energy quantum state for the superposition of the plurality of molecular species in a vacuum environment and in the environment of interest using a quantum optimization process; calculating a difference in lowest energy states between the vacuum environment and the environment of interest for each molecular species to provide a cost of the superposition of the plurality of molecular species; performing a quantum optimization process to determine a minimum cost for the superposition of the plurality of molecular species and to determine updated coefficients weighting the plurality of molecular species; and identifying the molecule for the environment of interest based on a comparison of the updated coefficients.

System and method for molecular design on a quantum computer

A method for calculating excited state properties of a molecular system using a hybrid classical-quantum computing system includes determining, using a quantum processor and memory, a ground state wavefunction of a combination of quantum logic gates. In an embodiment, the method includes forming a set of excitation operators. In an embodiment, the method includes forming a set of commutators from the set of excitation operators and a Hamiltonian operator. In an embodiment, the method includes mapping the set of commutators onto a set of qubit states, the set of qubit states corresponding to a set of qubits of the quantum processor. In an embodiment, the method includes evaluating, using the quantum processor and memory, the set of commutators. In an embodiment, the method includes causing a quantum readout circuit to measure an excited state energy from the set of computed commutators.

Calculating excited state properties of a molecular system using a hybrid classical-quantum computing system

Techniques regarding determining a three-dimensional structure of a heteropolymer 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 polymer folding component that can generate a course-grained model to determine a three-dimensional structure of a heteropolymer based on a first qubit registry that encodes a conformation of the heteropolymer on a lattice and a second qubit registry that encodes an interaction distance between monomers comprised within the heteropolymer.

Branched heteropolymer lattice model for quantum optimization

Techniques and a system to facilitate simulation-based optimization on a quantum computer are provided. In one example, a system includes a quantum processor. The quantum processor performs a quantum amplitude estimation process based on a probabilistic distribution associated with a decision-making problem. Furthermore, the quantum processor includes a simulator that simulates the decision-making problem based on the quantum amplitude estimation process.

Simulation-based optimization on a quantum computer

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

Solving mixed integer problems using a hybrid classical-quantum computing system includes generating a plurality of decision variables for a function associated with a combinatorial optimization problem by a first processor using an optimizer, and deriving at least one quantum state parameter for a quantum processor based upon one or more of the decision variables. The quantum processor is initiated in a quantum state based upon the at least one quantum state parameter. A plurality of intermediate quantum states of the quantum processor are measured using a plurality of quantum measurements of the quantum state to obtain a plurality of samples. The plurality of samples are evaluated by the first processor to obtain a measure of a quality of the quantum state and of one or more solutions to the combinatorial optimization problem.

Solving mixed integer optimization problems on a hybrid classical-quantum computing system

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

A method for enhancing hybrid-classical algorithms for combinatorial optimization includes executing, on a quantum processor, a variational algorithm, the execution producing a subset of a set of solutions (solution space) of the variational algorithm within a predefined period, the variational algorithm computing a quantum state of a quantum system corresponding to a combinatorial optimization problem, each solution in the subset having a corresponding value; sorting, according to a sorting criterion, the subset of solutions; isolating, based on the sorting, a portion of the subset of solutions wherein a value corresponding to each solution in the portion is within a boundary defined by a threshold value; computing an average value of the solutions from the portion of the subset of solutions; and altering the variational algorithm to produce a second subset of solutions such that the second subset of solutions comprises solutions having values within the boundary.

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Zurich, Switzerland

Panagiotis Barkoutsos