An apparatus, product and method includes partially compiling a quantum circuit from an initial cycle and until one or more intermediate cycles. A partial executable quantum circuit and measurements regarding a group of candidate auxiliary qubits are obtained by executing the partial executable quantum circuit multiple times and classifying the group of candidate auxiliary qubits to a qubit class with respect to a target group of qubits. The qubit class indicates that the group of candidate auxiliary qubits is not entangled with the target group. The quantum circuit is modified to apply a cleaning process on the group of candidate auxiliary qubits, whereby a modified quantum circuit is obtained. The modified quantum circuit is compiled thus obtaining an executable quantum circuit.

An apparatus, product and method includes partially compiling a quantum circuit from an initial cycle and until one or more intermediate cycles. A partial executable quantum circuit and measurements regarding a group of candidate auxiliary qubits are obtained by executing the partial executable quantum circuit multiple times and classifying the group of candidate auxiliary qubits to a qubit class with respect to a target group of qubits. The qubit class indicates that the group of candidate auxiliary qubits is not entangled with the target group. The quantum circuit is modified to apply a cleaning process on the group of candidate auxiliary qubits, whereby a modified quantum circuit is obtained. The modified quantum circuit is compiled thus obtaining an executable quantum circuit.

The is provided a computer-implemented method for generating a quantum circuit from a Unitary Coupled Cluster (UCC) Ansatz, wherein the Ansatz represents an excitation of a reference state by a parameterised operator including excitation operators, and wherein the Ansatz includes multi-qubit Pauli operators that are determined from each excitation operator. The method comprises: partitioning the Pauli operators into mutually commuting sets and sequencing the Pauli operators by set; generating Pauli gadgets from the Pauli operators by Trotterization, wherein the Pauli gadgets have a same sequencing by set as the Pauli operators; diagonalising each set of Pauli gadgets to convert the Pauli gadgets into phase gadgets; and transforming the phase gadgets into one- and two-qubit native gates to generate the quantum circuit. Moreover, there is also provided a system that is configured to implement the method.

The is provided a computer-implemented method for generating a quantum circuit from a Unitary Coupled Cluster (UCC) Ansatz, wherein the Ansatz represents an excitation of a reference state by a parameterised operator including excitation operators, and wherein the Ansatz includes multi-qubit Pauli operators that are determined from each excitation operator. The method comprises: partitioning the Pauli operators into mutually commuting sets and sequencing the Pauli operators by set; generating Pauli gadgets from the Pauli operators by Trotterization, wherein the Pauli gadgets have a same sequencing by set as the Pauli operators; diagonalising each set of Pauli gadgets to convert the Pauli gadgets into phase gadgets; and transforming the phase gadgets into one- and two-qubit native gates to generate the quantum circuit. Moreover, there is also provided a system that is configured to implement the method.

A method includes: obtaining training texts; for each of the training texts, performing the following operations: obtaining a word vector of each word in the current training text as a parameter of a first quantum circuit to obtain quantum states; inputting each of the quantum states to second, third, and fourth quantum circuits and performing measurement; calculating one group of weight values corresponding to each word to obtain a feature vector corresponding to the current training text; inputting the feature vector to a neural network model to obtain a prediction value; and determining a value of loss function based on the prediction value and a label value, and adjusting parameters corresponding to the second, third, and fourth quantum circuits and the neural network model based on the value of the loss function.

A method of measuring a physical quantity implemented in a hybrid classical-quantum system, the method comprising initializing the plurality of controllable quantum systems in an initial state, applying a set of preparation gates to the plurality of controllable quantum systems for preparing the plurality of controllable quantum systems in a non-classical state, evolving the non-classical state over a time period for obtaining an evolved state of the plurality of controllable quantum systems, applying a set of decoding gates to the plurality of controllable quantum systems in the evolved state, performing a measurement of the plurality of controllable quantum systems, and determining a derived value of the physical quantity based on a mapping function between an outcome of the measurement and the physical quantity on the classical computation system.

A method of measuring a physical quantity implemented in a hybrid classical-quantum system, the method comprising initializing the plurality of controllable quantum systems in an initial state, applying a set of preparation gates to the plurality of controllable quantum systems for preparing the plurality of controllable quantum systems in a non-classical state, evolving the non-classical state over a time period for obtaining an evolved state of the plurality of controllable quantum systems, applying a set of decoding gates to the plurality of controllable quantum systems in the evolved state, performing a measurement of the plurality of controllable quantum systems, and determining a derived value of the physical quantity based on a mapping function between an outcome of the measurement and the physical quantity on the classical computation system.

A method and system are provided for modeling the relative performance of algorithms, including quantum algorithms, over a set of problem instances. The model, referred to as a performance estimator, is generated from a selected algorithm and a set a set of problem instances as input, resulting in a generated model. Unlike prior methods, which model the performance of a fixed algorithm on a set of instances, embodiments of the present technology produce a performance estimate without needing to explicitly model the underlying algorithm. The model, once generated by the disclosed technology, may then be utilized to estimate the performance of new algorithms that the model has not been trained on.

A method comprising: forming a first mask over a substrate; forming one or more shadow walls in the openings of the first mask by selective area growth; forming a second mask over the substrate and shadow walls; forming a second material in the openings of the second mask by selective area growth; and depositing a layer of deposition material by angled deposition over parts of the substrate, shadow walls and second material, whereby regions shadowed by the shadow walls are left uncoated. In embodiments the second material may be a semiconductor and the deposition material may be a superconductor, and the method may be used to form one or more semiconductor-superconductor nanowires for inducing majorana zero modes as part of a quantum computing device.

A method includes receiving a plurality of quantum systems, wherein each quantum system of the plurality of quantum system includes a plurality of quantum sub-systems in an entangled state, and wherein respective quantum systems of the plurality of quantum systems are independent quantum systems that are not entangled with one another. The method further includes performing a plurality of joint measurements on different quantum sub-systems from respective ones of the plurality of quantum systems, wherein the joint measurements generate joint measurement outcome data and determining, by a decoder, a plurality of syndrome graph values based on the joint measurement outcome data.