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quant-ph2026

Quantum simulation in the Heisenberg picture via vectorization

Shao-Hen Chiew, Armando Angrisani, Zoë Holmes +1

We present a general framework for simulating quantum systems in the Heisenberg picture on quantum hardware. Based on the vectorization map, our framework fully exploits the mappin…

quant-ph2025

Advantage for Discrete Variational Quantum Algorithms in Circuit Recompilation

Oleksandr Kyriienko, Chukwudubem Umeano, Zoë Holmes

The relative power of quantum algorithms, using an adaptive access to quantum devices, versus classical post-processing methods that rely only on an initial quantum data set, remai…

quant-ph2025

Grover's algorithm is an approximation of imaginary-time evolution

Yudai Suzuki, Marek Gluza, Jeongrak Son +3

We reveal the power of Grover's algorithm from thermodynamic and geometric perspectives by showing that it is a product formula approximation of imaginary-time evolution (ITE), a R…

quant-ph2025

On Dequantization of Supervised Quantum Machine Learning via Random Fourier Features

Mehrad Sahebi, Alice Barthe, Yudai Suzuki +2

In the quest for quantum advantage, a central question is under what conditions can classical algorithms achieve a performance comparable to quantum algorithms--a concept known as…

quant-ph2025

Double-bracket algorithm for quantum signal processing without post-selection

Yudai Suzuki, Bi Hong Tiang, Jeongrak Son +3

Quantum signal processing (QSP), a framework for implementing matrix-valued polynomials, is a fundamental primitive in various quantum algorithms. Despite its versatility, a potent…

quant-ph2024

Double-bracket quantum algorithms for quantum imaginary-time evolution

Marek Gluza, Jeongrak Son, Bi Hong Tiang +5

Efficiently preparing approximate ground-states of large, strongly correlated systems on quantum hardware is challenging and yet nature is innately adept at this. This has motivate…