Abstract: Described are techniques for optimizing a quantum kernel for a support vector machine task. The techniques include receiving, by digital processor, a set of training data, each member of the set representing a data vector (x) and a label (y) identifying the respective member to be part of either a first class or a second class The techniques further include providing, by the digital processor, the quantum kernel comprising a set of unitary operations adapted for acting on a zero state of qubits of a universal quantum circuit The techniques further include performing, by a quantum processor comprising a set of interlinked quantum circuits, an alignment of the quantum kernel using an optimization algorithm based on the set of training data on a primal problem approach of the support vector machine task.

Abstract: 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.

Abstract: 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.

Abstract: 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 OSƒm defined 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)}/?). 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 ? at point x0.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Techniques that combine quantum error correction and quantum error mitigation are used to simulate a fault-tolerant T-gate with low sampling overhead using the quasiprobability decomposition method. In some embodiments, the T-gate can be simulated using two logical bits and a magic state preparation that mitigates the need for magic state distillation and consequently has a low sampling overhead. Alternatively, the T-gate can be simulated based on code deformation performed on the surface code. Noise is removed from the T-gate using quasiprobability decomposition based on a learned logical error rate.

Abstract: 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 (Cbudget) 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 Cbudget.

Abstract: The present invention relates to compounds and methods useful for the detection and treatment of disorders associated with frameshift mutations in coding microsatellite regions. The compounds and methods are applicable in cancers, especially of DNA mismatch repair deficient (MMR) sporadic tumors and HNPCC associated tumors. The compounds are useful for detection of disorders and in therapy such as immuno-therapy. The diagnostic methods relate to diagnosis and prognostic assessment of disorders associated with frameshift polypeptides originating from frameshift mutations in coding microsatellite regions of genes based on the detection of immunological entities directed against said frameshift polypeptides in body fluids.

Abstract: 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.