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Quantum computers (QC) are poised to drive important advances in several domains, including medicine, material science and internet security. While current QC systems are small, several industry and academic efforts are underway to build large systems with many hundred qubits.

Towards this, computer scientists at Princeton University and physicists from Duke University collaborated to develop methods to design the next generation of quantum computers. Their study focused on QC systems built using trapped ion (TI) technology, which is one of the current front-running QC hardware technologies. By bringing together computer architecture techniques and device simulations, the team showed that co-designing near-term hardware with applications can potentially improve the reliability of TI systems by up to four orders of magnitude.

Their study was conducted as a part of the Software-Tailored Architecture for Quantum co-design (STAQ) project, an NSF funded collaborative research effort to build an trapped-ion quantum computer and the NSF CISE Expedition in Computing Enabling Practical-Scale Quantum Computing (EPiQC) project. It was published recently in the 2020 ACM/IEEE International Symposium on Computer Architecture.

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Quantum information scientists have introduced a new method for machine-learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.

The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.

Quantum machine learning holds promise as one of the imperative applications for . In machine learning, one for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.

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