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Quantum Measurement Classification Using Statistical Learning
Interpreting the results of a quantum computer can pose a significant challenge due to inherent noise in these mesoscopic quantum systems. Quantum measurement, a critical component of quantum computing, involves determining the probabilities linked with ...
Hybrid Quantum-classical Search Algorithms
Search is one of the most commonly used primitives in quantum algorithm design. It is known that quadratic speedups provided by Grover’s algorithm are optimal, and no faster quantum algorithms for Search exist. While it is known that at least some quantum ...
An Optimal Linear-combination-of-unitaries-based Quantum Linear System Solver
Solving systems of linear equations is one of the most important primitives in many different areas, including in optimization, simulation, and machine learning. Quantum algorithms for solving linear systems have the potential to provide a quantum ...
Optimizing Initial State of Detector Sensors in Quantum Sensor Networks
In this article, we consider a network of quantum sensors, where each sensor is a qubit detector that “fires,” i.e., its state changes when an event occurs close by. The change in state due to the firing of a detector is given by a unitary operator, which ...
On the Success Probability of Quantum Order Finding
We prove a lower bound on the probability of Shor’s order-finding algorithm successfully recovering the order r in a single run. The bound implies that by performing two limited searches in the classical post-processing part of the algorithm, a high ...
A Characterization of Quantum Generative Models
- Carlos A. Riofrio,
- Oliver Mitevski,
- Caitlin Jones,
- Florian Krellner,
- Aleksandar Vuckovic,
- Joseph Doetsch,
- Johannes Klepsch,
- Thomas Ehmer,
- Andre Luckow
Quantum generative modeling is a growing area of interest for industry-relevant applications. This work systematically compares a broad range of techniques to guide quantum computing practitioners when deciding which models and methods to use in their ...
Quantum Circuit Cutting for Classical Shadows
Classical shadow tomography is a sample-efficient technique for characterizing quantum systems and predicting many of their properties. Circuit cutting is a technique for dividing large quantum circuits into smaller fragments that can be executed more ...