Zapata Computing, Inc.

Harvard College

Yudong Cao

Peter D Johnson

Pierre-Luc Dallaire-Demers

Jonathan P Olson

Andrei Kniazev

Eric R Anschuetz

Alan Aspuru-Guzik

Prineha Narang

Jerome Florian Gonthier

Jhonathan Romero Fontalvo

Amara Katabarwa

Guoming Wang

Christopher J Savoie

Will Thomas Finigan

Michael Cubeddu

Thomas Richard Lively

Enshan Dax Koh

A hybrid quantum-classical (HQC) computer system, which includes a classical computer and a quantum computer, solves linear systems. The HQC computer system splits the linear system to be solved into subsystems that are small enough to be solved by the quantum computer, under control of the classical computer. The classical computer synthesizes the outputs of the quantum computer to generate the complete solution to the linear system.

Hybrid quantum-classical computer for solving linear systems

A quantum-enhanced system and method for natural language processing (NLP) for generating a word embedding on a hybrid quantum-classical computer. A training set is provided on the classical computer, wherein the training set provides at least one pair of words, and at least one binary value indicating the correlation between the pair of words. The quantum computer generates quantum state representations for each word in the pair of words. The quantum component evaluates the quantum correlation between the quantum state representations of the word pair using an engineering likelihood function and a Bayesian inference. Training the word embedding on the quantum computer is provided using an error function containing the binary value and the quantum correlation.

Quantum enhanced word embedding for natural language processing

A quantum contextual measurement is generated from a quantum device capable of performing continuous time evolution, by generating a first measurement result and a second measurement result and combining the first measurement result and the second measurement result to generate the quantum contextual measurement. The first measurement result may be generated by initializing the quantum device to a first initial quantum state, applying a first continuous time evolution to the first initial state to generate a first evolved state, and measuring the first evolved state to generate the first measurement result. A similar process may be applied to generate a second evolved state which is at least approximately equal to the first evolved state, and then applying another continuous time evolution to the second evolved state to generate a third evolved state, and measuring the third evolved state to generate the second measurement result.

Generating non-classical measurements on devices with parameterized time evolution

A quantum computer directs an amplitude of a qubit to be proportional to the value of a function g of N variables {right arrow over (x)} by: (A) initializing M+1 qubits on the quantum computer, the M+1 qubits comprising: (1) a target qubit t having an amplitude of a reference state; and (2) a control register with M qubits {q}; and (B) changing the value of the amplitude of the reference state on the target qubit t, the changing comprising: (B)(1) applying a sequence of SU(2) gates to the target qubit t, the sequence of SU(2) gates comprising M controlled quantum gates Gand at least one rotation parameter, wherein at least one qubit of the control register acts as a control qubit for the controlled quantum gate G; and (B)(2) tuning the at least one rotation parameter until a halting criterion based on the amplitude of the reference state is satisfied.

Realizing controlled rotations by a function of input basis state of a quantum computer

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 quantum computer includes an efficient and exact quantum circuit for performing quantum state compression.

Quantum computer with exact compression of quantum states

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.

Hybrid quantum-classical computer for Bayesian inference with engineered likelihood functions for robust amplitude estimation

A hybrid quantum-classical (HQC) computer prepares a quantum Boltzmann machine (QBM) in a pure state. The state is evolved in time according to a chaotic, tunable quantum Hamiltonian. The pure state locally approximates a (potentially highly correlated) quantum thermal state at a known temperature. With the chaotic quantum Hamiltonian, a quantum quench can be performed to locally sample observables in quantum thermal states. With the samples, an inverse temperature of the QBM can be approximated, as needed for determining the correct sign and magnitude of the gradient of a loss function of the QBM.

Hybrid quantum-classical computer system for implementing and optimizing quantum Boltzmann machines

A computer system, designed according to a particular architecture, compiles and execute a general quantum program. Computer systems designed in accordance with the architecture are suitable for use with a variety of programming languages and a variety of hardware backends. The architecture includes a classical computer and a quantum device (which may be remote from the local computer) which includes both classical execution units and a quantum processing unit (QPU).

Computer architecture for executing quantum programs

Mapping of logical qubits to physical qubits is provided. In various embodiments, a first candidate subgraph is selected from a hardware graph. The hardware graph represents a physical quantum circuit. The hardware graph comprises a plurality of nodes corresponding to physical qubits and a plurality of edges corresponding to coupling among the plurality of qubits. An accepted subgraph is determined by: setting the accepted subgraph to be the first candidate subgraph; mapping a quantum circuit comprising a plurality of logical qubits to the accepted subgraph; generating a second candidate subgraph of the hardware graph based on the accepted subgraph; mapping the quantum circuit to the second candidate subgraph; comparing fidelities of the accepted subgraph and the second candidate subgraph for the quantum circuit; if the fidelity of the second candidate subgraph is greater than the fidelity of the accepted subgraph, setting the accepted subgraph to be the second candidate subgraph; if the fidelity of the second candidate subgraph is less than or equal to the fidelity of the accepted subgraph, setting the accepted subgraph to be the second candidate subgraph with a time-dependent probability.

Quantum circuit embedding by simulated annealing

A hybrid quantum-classical computer solves systems of equations and eigenvalue problems utilizing non-unitary transformations on the quantum computer. The method may be applied, for example, to principal component analysis, least squares fitting, regression, spectral embedding and clustering, vibrations in mechanics, fluids and quantum chemistry, material sciences, electromagnetism, signal processing, image segmentation and data mining.

Quantum-classical system and method for matrix computations

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 hybrid quantum-classical computing method for solving optimization problems though applications of non-unitary transformations. An initial state is prepared, a transformation is applied, and the state is updated to provide an improved answer. This update procedure is iterated until convergence to an approximately optimal solution.

Hybrid quantum-classical computer system and method for optimization

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.

Hybrid quantum-classical adversarial generator

Qubit allocation for noisy intermediate-scale quantum computers is provided. A quantum circuit comprises a plurality of logical qubits. A hardware specification comprising a connectivity graph of a plurality of physical qubits. A directed acyclic allocation graph is determined based on the plurality of logical qubits and the connectivity graph. The allocation graph comprises a node for each possible allocation of the plurality of logical qubits to the plurality of physical qubits, each allocation having a fidelity, and a plurality of directed edges, each edge connecting to its corresponding first node from its corresponding second node, the first node corresponding to a first allocation, the second node corresponding to a sub-allocation of the first allocation. The allocation graph is searched for a weighted shortest path from a root node of the allocation graph to a leaf node of the allocation graph. The allocation corresponding to the weighted shortest path is outputted.

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Cambridge, MA, United States of America

Yudong Cao