International Business Machines Corporation

Goldman Sachs &co. LLC

Guglielmo Mazzola

Stefan Woerner

Ivano Tavernelli

Pauline Ollitrault

Nikitas Stamatopoulos

William Joseph Zeng

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

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 multireference parallelization of variational quantum computing to achieve high accuracy with short circuit depths. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a trial component that prepares a multireference trial state based on a qubit operator, by applying a unitary circuit operator to a sum of selected initial configurations.

Multireference procedure to parallelize variational quantum computing and achieve high accuracy with short circuit depths

Techniques of facilitating improved computational efficiency in Variational Quantum Eigensolver calculations by quantum computing devices. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise: a distribution component; and a feedback component. The distribution component can set a Pauli-dependent sample budget for a Pauli term of an operator to unevenly distribute a total sample budget for evaluating an expected value of the operator among a plurality of Pauli terms composing the operator. The plurality of Pauli terms can comprise the Pauli term. The feedback component can evaluate compatibility between a prescreening variance for the Pauli term and a production variance of the Pauli term generated using the Pauli-dependent sample budget.

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Zurich, Switzerland

Guglielmo Mazzola