Search for: All records

Due to the limitations of current NISQ systems, error mitigation strategies are under development to alleviate the negative effects of error-inducing noise on quantum applications. This work proposes the use of machine learning (ML) as an error mitigation strategy, using ML to identify the accurate solutions to a quantum application in the presence of noise. Methods of encoding the probabilistic solution space of a basis-encoded quantum algorithm are researched to identify the characteristics which represent good ML training inputs. A multilayer perceptron artificial neural network (MLP ANN) was trained on the results of 8-state and 16-state basis-encoded quantum applications both in the presence of noise and in noise-free simulation. It is demonstrated using simulated quantum hardware and probabilistic noise models that a sufficiently trained model may identify accurate solutions to a quantum applications with over 90% precision and 80% recall on select data. The model makes confident predictions even with enough noise that the solutions cannot be determined by direct observation, and when it cannot, it can identify the inconclusive experiments as candidates for other error mitigation techniques. 

Multiplication is a frequent computation in many algorithms, classical and quantum. This paper targets the implementation of quantum integer multiplication. Quantum array multipliers take inspiration from classical array multipliers, with the result of reduced circuit depth. They take advantage of the quantum phase domain, through rotations controlled by the multiplier’s qubits. This work further explores this implementation by applying approximate rotations. Although this approach can have an impact on the accuracy of the result, the reduction in depth can result in better outcomes when noise is involved. 

This paper proposes a new and improved implementation of a quantum integer multiplier. Performing arithmetic computations is sometimes a necessary step in the implementation of quantum algorithms. In this work, Quantum Fourier Transform is used in order to perform scalable arithmetic in a generic bit-width quantum system. In the phase domain, addition can be implemented through accumulated controlled rotations on the qubits’ state. Leveraging this, and inspired by the classical implementation of an array multiplier, a new integer multiplier is fully designed and tested in a quantum environment. The depth of a quantum circuit is the number of computational steps necessary to completion, and it is a key parameter that reflects on the performance of the design. The new design reduces the quantum depth of the design from the exponential order of the previously proposed designs to polynomial order.