Abstract: A hybrid quantum-classical (HQC) computer takes advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. The HQC computer derives inspiration from quantum metrology, phase estimation, and the more recent “alpha-VQE” proposal, arriving at a general formulation that is robust to error and does not require ancilla qubits. The HQC computer uses the “engineered likelihood function” (ELF)to carry out Bayesian inference. The ELF formalism enhances the quantum advantage in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.

Abstract: Model-free error correction in quantum processors is provided, allowing tailoring to individual devices. In various embodiments, a quantum circuit is configured according to a plurality of configuration parameters. The quantum circuit comprises an encoding circuit and a decoding circuit. Each of a plurality of training states is input to the quantum circuit. The encoding circuit is applied to each of the plurality of training states and to a plurality of input syndrome qubits to produce encoded training states. The decoding circuit is applied to each of the encoded training states to determine a plurality of outputs. A fidelity of the quantum circuit is measured for the plurality of training states based on the plurality of outputs. The fidelity is provided to a computing node. The computing node determines a plurality of optimized configuration parameters. The optimized configuration parameters maximize the accuracy of the quantum circuit for the plurality of training states.

Abstract: A hybrid quantum-classical (HQC) computer takes advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. The HQC computer derives inspiration from quantum metrology, phase estimation, and the more recent “alpha-VQE” proposal, arriving at a general formulation that is robust to error and does not require ancilla qubits. The HQC computer uses the “engineered likelihood function” (ELF) to carry out Bayesian inference. The ELF formalism enhances the quantum advantage in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.

Abstract: Preparation of correlated fermionic states on a quantum computer for determining a ground state of a correlated fermionic system is provided. In various embodiments, a quantum circuit is provided that comprises a linear chain of qubits and a plurality of matchgates arranged in layers. Each matchgate is configured to perform a two-qubit rotation on neighboring qubits within the linear chain. An initial state is provided for each qubit in the linear chain, The quantum circuit is applied to the initial values, thereby preparing an ansastz on the linear chain of qubits, the ansatz corresponding to a fermionic state.

Abstract: Computer-implemented methods and systems define hardware constraints for quantum processors such that the time required to estimate the energy expectation value of a given fermionic Hamiltonian using the method of Bayesian Optimized Operator Expectation Algorithm (BOOEA) is minimized.

Abstract: A quantum computer or a hybrid quantum-classical (HQC) computer leverages the power of noisy intermediate-scale quantum (NISQ) superconducting quantum processors at and/or beyond the supremacy regime to evaluate the ground state energy of an electronic structure Hamiltonian.

Abstract: A quantum computer or a hybrid quantum-classical (HQC) computer leverages the power of noisy intermediate-scale quantum (NISQ) superconducting quantum processors at and/or beyond the supremacy regime to evaluate the ground state energy of an electronic structure Hamiltonian.

Abstract: A computer system and method implement a conditional reflection operator on a quantum computer (such as an ion trap quantum computer) with a trap topology containing at least two t-junctions and at least one central interaction zone that can execute Molmer-Sorensen gates on at least two ions.

Abstract: Preparation of correlated fermionic states on a quantum computer for determining a ground state of a correlated fermionic system is provided. In various embodiments, a quantum circuit is provided that comprises a linear chain of qubits and a plurality of matchgates arranged in layers. Each matchgate is configured to perform a two-qubit rotation on neighboring qubits within the linear chain. An initial state is provided for each qubit in the linear chain, The quantum circuit is applied to the initial values, thereby preparing an ansastz on the linear chain of qubits, the ansatz corresponding to a fermionic state.

Abstract: A hybrid quantum-classical (HQC) computer takes advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. The HQC computer derives inspiration from quantum metrology, phase estimation, and the more recent “alpha-VQE” proposal, arriving at a general formulation that is robust to error and does not require ancilla qubits. The HQC computer uses the “engineered likelihood function” (ELF) to carry out Bayesian inference. The ELF formalism enhances the quantum advantage in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.

Abstract: Model-free error correction in quantum processors is provided, allowing tailoring to individual devices. In various embodiments, a quantum circuit is configured according to a plurality of configuration parameters. The quantum circuit comprises an encoding circuit and a decoding circuit. Each of a plurality of training states is input to the quantum circuit. The encoding circuit is applied to each of the plurality of training states and to a plurality of input syndrome qubits to produce encoded training states. The decoding circuit is applied to each of the encoded training states to determine a plurality of outputs. A fidelity of the quantum circuit is measured for the plurality of training states based on the plurality of outputs. The fidelity is provided to a computing node. The computing node determines a plurality of optimized configuration parameters. The optimized configuration parameters maximize the accuracy of the quantum circuit for the plurality of training states.