International Business Machines Corporation

Goldman Sachs &co. LLC

Stefan Woerner

Ivano Tavernelli

Panagiotis Barkoutsos

Daniel Josef Egger

Almudena Carrera Vazquez

Giacomo Nannicini

Christophe Piveteau

William Joseph Zeng

Guglielmo Mazzola

Christa Zoufal

Anton Robert

Nikitas Stamatopoulos

David Sutter

Marco Pistoia

Jay Michael Gambetta

Alessandro Curioni

Antonio Mezzacapo

Paul Kristan Temme

Sergey Bravyi

Takashi Imamichi

Pauline Ollitrault

Atsushi Matsuo

Jennifer Ranae Glick

Dimitry Grinko

Fotios Gkritsis

Rajiv Krishnakumar

Shouvanik Chakrabarti

Described herein are quantum gradient algorithms that result in a quantum advantage over conventional methods. In an example, a quantum circuit is configured to implement a quantum gradient algorithm when executed on qubits of a quantum computing system. The quantum gradient algorithm includes a phase oracle Odefined by a finite difference approximation with an order greater than zero, and a complexity of the quantum gradient algorithm scales as(√{square root over (k)}/ϵ). The quantum circuit is repeatedly executed on qubits of a quantum computing system to determine a k-dimensional gradient of a function ƒ(x) within an error ϵ at point x.

Quantum advantage using quantum circuit for gradient estimation

Techniques facilitating error mitigation for quantum computing devices. In one example, a system can comprise a process that executes computer executable components stored in memory. The computer executable components comprise: an approximation component; a budget component; and an optimization component. The approximation component can generate an approximate decomposition of a quantum gate. The budget component can set a budget value (C) for a C-factor that is a metric for increase in variance of quasi-probability sampling. The optimization component can determine an optimal decomposition for the quantum gate as a function of C.

Quantum error mitigation using hardware-friendly probabilistic error correction

Systems, computer-implemented methods, and computer program products to facilitate estimation of quantum resources to calculate an expectation value of a stochastic process using a re-parameterization method 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 re-parameterization component that applies a quantum fault-tolerant operation to a variationally prepared quantum state corresponding to a probability distribution to produce a quantum state corresponding to a target probability distribution. The computer executable components can further comprise an estimation component that estimates at least one defined criterion of a quantum computer to be used to compute an expectation value of a stochastic process associated with the target probability distribution.

Quantum resource estimation using a re-parameterization method

Systems, computer-implemented methods, and computer program products to facilitate quantum state preparation of a probability distribution to perform amplitude estimation 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 data loader component that prepares a quantum state of a probability distribution based on structure of a quantum amplitude estimation algorithm. The computer executable components can further comprise an operator component that constructs a quantum operator based on the quantum state to perform quantum amplitude estimation.

Quantum state preparation of a probability Distribution facilitating quantum amplitude estimation

Systems and methods that facilitate quantum state preparation of a probability distribution and constructing a quantum operator for a stochastic process based on quantum state to facilitate quantum amplitude estimation. A loading component uses a context-aware distribution loading scheme to load arbitrary random distributions to facilitate preparing a quantum state of a probability distribution based on a structure of a quantum amplitude estimation algorithm, and an operating component constructs a quantum operator for arbitrary computable functions or stochastic processes based on the quantum state to perform quantum amplitude estimation.

Quantum amplitude estimation state preparation for stochastic processes

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

Systems and methods that address an optimized method to handle portfolio constraints such as integer budget constraints and solve portfolio optimization problems that map both to mixed binary and quadratic binary optimization problems. A digital processor is used to create a hierarchical clustering; this clustering is leveraged to allocate capital to sub-clusters of the hierarchy. Once the sub-clusters are sufficiently small, a quantum processor is used to solve the portfolio optimization problem. Thus, the innovation employs clustering to reduce an optimization problem to sub-problems that are sufficiently small enough to be solved using a quantum computer given available qubits.

Hierarchical portfolio optimization using clustering and near-term quantum computers

Quantum state preparation of a probability distribution facilitating quantum amplitude estimation

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

Systems, computer-implemented methods, and computer program products to facilitate iterative quantum amplitude estimation 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 iterative quantum amplitude estimation component that increases a multiplier value of a confidence interval in an estimation problem to a defined value that positions the confidence interval in a defined plane of a defined circle. The computer executable components can further comprise a measurement component that captures a quantum state measurement of a qubit in a quantum circuit based on the defined value.

Iterative quantum amplitude estimation

Systems and methods that address an optimized method to improve systems and methods for handling inequality constraints in mixed binary optimization problems on quantum computers and to solve local optima which significantly improves system performance. Embodiments employ an improved methodology that can optimize parameters, determine an optimal slack variable and optimize variational parameters for fixed slack variables. This procedure allows to move out of local minima, solve an optimization and improve the system performance by providing optimal results. These embodiments also extend to variational hybrid quantum/classical algorithms for gate-based quantum computers.

System and method for handling inequality constraints in mixed binary optimization on quantum computers

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.

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Zurich, Switzerland

Stefan Woerner