Quantum chemistry and charge transport in biomolecules with superconducting circuits

García-Álvarez, Laura; Heras, U. Las; Mezzacapo, Antonio; Sanz, Mikel; Solano, E.; Lamata, Lucas

doi:10.1038/srep27836

Cited by 24 publications

(23 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, on a quantum device the natural way is to solve the chemical system in second quantization [3,4,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33] formulated in terms of fermionic annihilation and creation operators. Because of the different statistics there is no direct one-to-one mapping: each fermion operator must be represented by a string of qubit operators, which induces long-range qubit-qubit correlations in the system and places demanding requirements on the connectivity and the number of gates (see Section 4.1).…”

Section: Introductionmentioning

confidence: 99%

Quantum optimization using variational algorithms on near-term quantum devices

Moll

Barkoutsos

Bishop³

et al. 2018

Quantum Sci. Technol.

673

484

View full text Add to dashboard Cite

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, near-term quantum devices will provide several hundred qubits and limited error correction. Still, there is a realistic prospect to run useful algorithms within the limited circuit depth of such devices. Particularly promising are optimization algorithms that follow a hybrid approach: the aim is to steer a highly entangled state on a quantum system to a target state that minimizes a cost function via variation of some gate parameters. This variational approach can be used both for classical optimization problems as well as for problems in quantum chemistry. The challenge is to converge to the target state given the limited coherence time and connectivity of the qubits. In this context, the quantum volume as a metric to compare the power of near-term quantum devices is discussed.With focus on chemistry applications, a general description of variational algorithms is provided and the mapping from fermions to qubits is explained. Coupledcluster and heuristic trial wave-functions are considered for efficiently finding molecular ground states. Furthermore, simple error-mitigation schemes are introduced that could improve the accuracy of determining ground-state energies. Advancing these techniques may lead to near-term demonstrations of useful quantum computation with systems containing several hundred qubits.PACS numbers: quantum computation, quantum chemistry, quantum algorithms

show abstract

Section: Introductionmentioning

confidence: 99%

Quantum optimization using variational algorithms on near-term quantum devices

Moll

Barkoutsos

Bishop³

et al. 2018

Quantum Sci. Technol.

673

484

View full text Add to dashboard Cite

show abstract

“…19,[36][37][38][39] Approaches that include the entanglement of internal states to bosonic modes could simulate more complex processes, such as the dynamics of fermionic lattice models, 40,41 with possible extensions to electron-nuclear dynamics for quantum chemistry, 41,42 as well as models of charge and energy transfer. [43][44][45][46] Here, we show that analog quantum simulators can efficiently simulate non-adiabatic chemical dynamics. Our approach can be implemented using any quantum system containing a qudit with controllable couplings to a set of bosonic modes, a device we call a mixed qudit-boson (MQB) simulator.…”

Section: Introductionmentioning

confidence: 85%

Analog quantum simulation of chemical dynamics

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Recent advances in the field of quantum computing have boosted the hope that one day we might be able to solve complex material-science problems using quantum computers [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]. It was shown that the direct mapping of the molecular wave function to the qubit state allows the unitary operator to be decomposed into a number of gates that only scales polynomially with system size [2].…”

Section: Introductionmentioning

confidence: 99%

Optimizing qubit resources for quantum chemistry simulations in second quantization on a quantum computer

Moll

Fuhrer

Staar

et al. 2016

J. Phys. A: Math. Theor.

View full text Add to dashboard Cite

Quantum chemistry simulations on a quantum computer suffer from the overhead needed for encoding the fermionic problem in a system of qubits. By exploiting the block diagonality of a fermionic Hamiltonian, we show that the number of required qubits can be reduced while the number of terms in the Hamiltonian will increase. All operations for this reduction can be performed in operator space. The scheme is conceived as a pre-computational step that would be performed prior to the actual quantum simulation. We apply this scheme to reduce the number of qubits necessary to simulate both the Hamiltonian of the two-site Fermi-Hubbard model and the hydrogen molecule. Both quantum systems can then be simulated with a twoqubit quantum computer. Despite the increase in the number of Hamiltonian terms, the scheme still remains a useful tool to reduce the dimensionality of specific quantum systems for quantum simulators with a limited number of resources.

show abstract

Quantum chemistry and charge transport in biomolecules with superconducting circuits

Cited by 24 publications

References 73 publications

Quantum optimization using variational algorithms on near-term quantum devices

Quantum optimization using variational algorithms on near-term quantum devices

Analog quantum simulation of chemical dynamics

Optimizing qubit resources for quantum chemistry simulations in second quantization on a quantum computer

Contact Info

Product

Resources

About