Zapata Computing, Inc.

Harvard College

Jonathan P Olson

Yudong Cao

Alan Aspuru-Guzik

Peter D Johnson

Pierre-Luc Dallaire-Demers

Eric R Anschuetz

Jhonathan Romero Fontalvo

Jhonathan Romero

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.

Device-tailored model-free error correction in quantum processors

A system and method include techniques for: generating, by a quantum autoencoder, based on a set of quantum states encoded in a set of qubits, a decoder circuit that acts on a subset of the set of qubits, a size of the subset being less than a size of the set; and generating a reduced-cost circuit, the reduced-cost circuit comprising: (1) a new parameterized quantum circuit acting only on the subset of the set of qubits, and (2) the decoder circuit.

Compressed unsupervised quantum state preparation with quantum autoencoders

A hybrid quantum-classical (HQC) computing system, including a quantum computing component and a classical computing component, computes the inverse of a Boolean function for a given output. The HQC computing system translates a set of constraints into interactions between quantum spins; forms, from the interactions, an Ising Hamiltonian whose ground state encodes a set of states of a specific input value that are consistent with the set of constraints; performs, on the quantum computing component, a quantum optimization algorithm to generate an approximation to the ground state of the Ising Hamiltonian; and measures the approximation to the ground state of the Ising Hamiltonian, on the quantum computing component, to obtain a plurality of input bits which are a satisfying assignment of the set of constraints.

Hybrid quantum-classical computer system and method for performing function inversion

A method for training an adversarial generator from a data set and a classifier includes: (A) training a classical noise generator whose input includes an output of a quantum generator, the classical noise generator having a first set of parameters, the training comprising: sampling from the data set to produce a first sample and a first corresponding label for the first sample; producing an output of the classical noise generator based on the output of the quantum generator and the first sample; producing a noisy example based on the output of the classical noise generator and the first sample; providing the noisy example to the classifier to produce a second corresponding label for the first sample; updating the first set of parameters such that the first corresponding label of the first sample differs from the second corresponding label of the first sample.

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Cambridge, MA, United States of America

Jonathan P Olson