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Simulating interactions between fermions and bosons is central to understanding correlated phenomena, yet these systems are inherently difficult to treat classically. Previous quantum algorithms for fermion-boson models exhibit computation…

Quantum Physics · Physics 2026-01-27 Harriet Apel , Burak Şahinoğlu

We present a quantum algorithm for the dynamical simulation of time-dependent Hamiltonians. Our method involves expanding the interaction-picture Hamiltonian as a sum of generalized permutations, which leads to an integral-free Dyson series…

Quantum Physics · Physics 2021-09-15 Yi-Hsiang Chen , Amir Kalev , Itay Hen

Simulating fermionic lattice models with qubits requires mapping fermionic degrees of freedom to qubits. The simplest method for this task, the Jordan-Wigner transformation, yields strings of Pauli operators acting on an extensive number of…

Quantum Physics · Physics 2017-04-05 Vojtěch Havlíček , Matthias Troyer , James D. Whitfield

Recently, there has been increasing interest in designing schemes for quantum computations that are robust against errors. Although considerable research has been devoted to developing quantum error correction schemes, much less attention…

Quantum Physics · Physics 2020-02-03 Yingkai Ouyang , Yi Shen , Lin Chen

Fermionic atoms in optical lattices provide a native implementation of Fermi-Hubbard (FH) models that can be used as analog quantum simulators of many-body fermionic systems. Recent experimental advances include the time-dependent local…

We consider the task of simulating time evolution under a Hamiltonian $H$ within its low-energy subspace. Assuming access to a block-encoding of $H'=(H-E)/\lambda$ for some $E \in \mathbb R$, the goal is to implement an…

Quantum Physics · Physics 2024-08-28 Alexander Zlokapa , Rolando D. Somma

The quantum circuit model is the de-facto way of designing quantum algorithms. Yet any level of abstraction away from the underlying hardware incurs overhead. In the era of near-term, noisy, intermediate-scale quantum (NISQ) hardware with…

Quantum Physics · Physics 2021-08-27 Laura Clinton , Johannes Bausch , Toby Cubitt

Efficient encoding of electronic operators into qubits is essential for quantum chemistry simulations. The majority of methods map single electron states to qubits, effectively handling electron interactions. Alternatively, pairs of…

Quantum Physics · Physics 2025-09-09 Francisco Javier Del Arco Santos , Jakob S. Kottmann

As the search continues for useful applications of noisy intermediate scale quantum devices, variational simulations of fermionic systems remain one of the most promising directions. Here, we perform a series of quantum simulations of…

This paper introduces the Hamiltonian-Adaptive Ternary Tree (HATT) framework to compile optimized Fermion-to-qubit mapping for specific Fermionic Hamiltonians. In the simulation of Fermionic quantum systems, efficient Fermion-to-qubit…

Quantum Physics · Physics 2025-04-15 Yuhao Liu , Kevin Yao , Jonathan Hong , Julien Froustey , Ermal Rrapaj , Costin Iancu , Gushu Li , Yunong Shi

We present an algorithm for efficiently approximating of qubit unitaries over gate sets derived from totally definite quaternion algebras. It achieves $\varepsilon$-approximations using circuits of length $O(\log(1/\varepsilon))$, which is…

Quantum Physics · Physics 2015-10-16 Vadym Kliuchnikov , Alex Bocharov , Martin Roetteler , Jon Yard

Calculating the energy gradient in parameter space has become an almost ubiquitous subroutine of variational near-term quantum algorithms. "Faithful" classical emulation of this subroutine mimics its quantum evaluation, and scales as O(P^2)…

Quantum Physics · Physics 2020-09-08 Tyson Jones , Julien Gacon

We numerically analyze the feasibility of a platform-neutral, general strategy to perform quantum simulations of fermionic lattice field theories under open boundary conditions. The digital quantum simulator requires solely one- and…

Simulation of interacting fermionic Hamiltonians is one of the most promising applications of quantum computers. However, the feasibility of analysing fermionic systems with a quantum computer hinges on the efficiency of fermion-to-qubit…

Quantum Physics · Physics 2025-10-27 Jeffery Yu , Yuan Liu , Sho Sugiura , Troy Van Voorhis , Sina Zeytinoğlu

Simulation of fermionic Hamiltonians with gate-based quantum computers requires the selection of an encoding from fermionic operators to quantum gates, the most widely used being the Jordan-Wigner transform. Many alternative encodings…

Quantum Physics · Physics 2026-05-01 Michael Williams de la Bastida , Thomas M. Bickley , Peter V. Coveney

We present a new primitive for quantum algorithms that implements a discrete Hermite transform efficiently, in time that depends logarithmically in both the dimension and the inverse of the allowable error. This transform, which maps basis…

Quantum Physics · Physics 2025-10-07 Siddhartha Jain , Vishnu Iyer , Rolando D. Somma , Ning Bao , Stephen P. Jordan

Until high-fidelity quantum computers with a large number of qubits become widely available, classical simulation remains a vital tool for algorithm design, tuning, and validation. We present a simulator for the Quantum Approximate…

Quantum Physics · Physics 2023-11-14 Danylo Lykov , Ruslan Shaydulin , Yue Sun , Yuri Alexeev , Marco Pistoia

We simulate the excited states of the Lipkin model using the recently proposed Quantum Equation of Motion (qEOM) method. The qEOM generalizes the EOM on classical computers and gives access to collective excitations based on quasi-boson…

Quantum circuit simulations are essential for the verification of quantum algorithms on behalf of real quantum devices. However, the memory requirements for such simulations grow exponentially with the number of qubits involved in quantum…

Quantum Physics · Physics 2025-03-04 Dongin Lee , Enhyeok Jang , Seungwoo Choi , Junwoong An , Cheolhwan Kim , Won Woo Ro

Kernel methods are powerful tools in statistical learning, but their cubic complexity in the sample size n limits their use on large-scale datasets. In this work, we introduce a scalable framework for kernel regression with O(n log n)…

Machine Learning · Statistics 2025-09-04 Nathan Doumèche , Francis Bach , Gérard Biau , Claire Boyer