EP-PQM: Efficient Parametric Probabilistic Quantum Memory With Fewer Qubits and Gates

Machine learning (ML) classification tasks can be carried out on a quantum computer (QC) using probabilistic quantum memory (PQM) and its extension, parametric PQM (P-PQM), by calculating the Hamming distance between an input pattern and a database of r patterns containing z features with a distinct attributes. For PQM and P-PQM to correctly compute the Hamming distance, the feature must be […]

A Divide-and-Conquer Approach to Dicke State Preparation

We present a divide-and-conquer approach to deterministically prepare Dicke states |Dnk⟩ (i.e., equal-weight superpositions of all n -qubit states with Hamming weight k ) on quantum computers. In an experimental evaluation for up to n=6 qubits on IBM Quantum Sydney and Montreal devices, we achieve significantly higher state fidelity compared to previous results. The fidelity gains are achieved through several techniques: our circuits […]

Entanglement Distribution in a Quantum Network: A Multicommodity Flow-Based Approach

We consider the problem of optimizing the achievable EPR-pair distribution rate between multiple source-destination pairs in a quantum Internet, where the repeaters may perform a probabilistic Bell-state measurement and we may impose a minimum end-to-end fidelity as a requirement. We construct an efficient linear programming (LP) formulation that computes the maximum total achievable entanglement distribution […]