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Quantum simulation of chemical systems is one of the most promising near-term applications of quantum computers. The variational quantum eigensolver, a leading algorithm for molecular simulations on quantum hardware, has a serious…

Quantum Physics · Physics 2019-07-16 Harper R. Grimsley , Sophia E. Economou , Edwin Barnes , Nicholas J. Mayhall

Quantum algorithms require accurate representations of electronic states on a quantum device, yet the approximation of electronic wave functions for strongly correlated systems remains a profound theoretical challenge, with existing methods…

Quantum Physics · Physics 2025-04-29 Dipanjali Halder , Dibyendu Mondal , Rahul Maitra

Quantum chemistry is one of the most promising applications of quantum computers in the near future. For noisy intermediate-scale quantum devices, the quantum-classical hybrid framework based on the variational quantum eigensolver (VQE) has…

A key requirement to perform simulations of large quantum systems on near-term quantum hardware is the design of quantum algorithms with short circuit depth that finish within the available coherence time. A way to stay within the limits of…

Quantum computational chemistry has emerged as an important application of quantum computing. Hybrid quantum-classical computing methods, such as variational quantum eigensolvers (VQE), have been designed as promising solutions to quantum…

The near-term utility of quantum computers is hindered by hardware constraints in the form of noise. One path to achieving noise resilience in hybrid quantum algorithms is to decrease the required circuit depth -- the number of applied…

Quantum Physics · Physics 2024-04-18 Erika Magnusson , Aaron Fitzpatrick , Stefan Knecht , Martin Rahm , Werner Dobrautz

Adaptive ansatz construction has emerged as a powerful technique for reducing circuit depth and improving optimization efficiency in variational quantum eigensolvers. However, existing adaptive methods, including ADAPT-VQE, rely solely on…

Quantum Physics · Physics 2026-03-12 Mohammad Aamir Sohail , Toshiaki Koike-Akino

The study and prediction of chemical reactivity is one of the most important application areas of molecular quantum chemistry. Large-scale, fully error-tolerant quantum computers could provide exact or near-exact solutions to the underlying…

Quantum Physics · Physics 2019-09-12 Michael Kühn , Sebastian Zanker , Peter Deglmann , Michael Marthaler , Horst Weiß

Determining the exponentially scaled ground state wavefunction and the associated molecular properties remains one of the central challenges in quantum chemistry. Hybrid quantum-classical algorithms implemented on quantum computers offer a…

Quantum Physics · Physics 2026-04-22 Dibyendu Mondal , Ashish Kumar Patra , Rahul Maitra

The calculation time for the energy of atoms and molecules scales exponentially with system size on a classical computer but polynomially using quantum algorithms. We demonstrate that such algorithms can be applied to problems of chemical…

Quantum Physics · Physics 2007-05-23 Alán Aspuru-Guzik , Anthony D. Dutoi , Peter J. Love , Martin Head-Gordon

Quantum simulation, one of the most promising applications of a quantum computer, is currently being explored intensely using the variational quantum eigensolver. The feasibility and performance of this algorithm depend critically on the…

Quantum computers promise to revolutionise electronic simulations by overcoming the exponential scaling of many-electron problems. While electronic wave functions can be represented using a product of fermionic unitary operators, shallow…

Quantum Physics · Physics 2022-07-04 Hugh G. A. Burton , Daniel Marti-Dafcik , David P. Tew , David J. Wales

ADAPT-VQE is a robust algorithm for hybrid quantum-classical simulations of quantum chemical systems on near-term quantum computers. While its iterative process systematically reaches the ground state energy, ADAPT-VQE is sensitive to local…

Universal fault-tolerant quantum computers will require error-free execution of long sequences of quantum gate operations, which is expected to involve millions of physical qubits. Before the full power of such machines will be available,…

The ability to simulate a fermionic system on a quantum computer is expected to revolutionize chemical engineering, materials design, nuclear physics, to name a few. Thus, optimizing the simulation circuits is of significance in harnessing…

Quantum Physics · Physics 2021-07-28 Qingfeng Wang , Ming Li , Christopher Monroe , Yunseong Nam

A common requirement of quantum simulations and algorithms is the preparation of complex states through sequences of 2-qubit gates. For a generic quantum state, the number of gates grows exponentially with the number of qubits, becoming…

Quantum Physics · Physics 2024-07-08 Matan Ben Dov , David Shnaiderov , Adi Makmal , Emanuele G. Dalla Torre

A milestone in the field of quantum computing will be solving problems in quantum chemistry and materials faster than state-of-the-art classical methods. The current understanding is that achieving quantum advantage in this area will…

Quantum Physics · Physics 2023-11-08 Guoming Wang , Daniel Stilck França , Ruizhe Zhang , Shuchen Zhu , Peter D. Johnson

Quantum algorithm design usually assumes access to a perfect quantum computer with ideal properties like full connectivity, noise-freedom and arbitrarily long coherence time. In Noisy Intermediate-Scale Quantum (NISQ) devices, however, the…

Quantum Physics · Physics 2020-09-11 Xiangzhen Zhou , Sanjiang Li , Yuan Feng

We explore a non-variational quantum state preparation approach combined with the ADAPT operator selection strategy in the application of preparing the ground state of a desired target Hamiltonian. In this algorithm, energy gradient…

By design, the variational quantum eigensolver (VQE) strives to recover the lowest-energy eigenvalue of a given Hamiltonian by preparing quantum states guided by the variational principle. In practice, the prepared quantum state is…

Quantum Physics · Physics 2021-06-29 Daniel Claudino , Jerimiah Wright , Alexander J. McCaskey , Travis S. Humble
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