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| Adiabatic quantum computation | |
|---|---|
| Name | Adiabatic quantum computation |
| Field | Quantum computing |
| Introduced | 2000s |
| Key people | Farhi, Goldstone, Gutmann, Aharonov, Born, Feynman |
Adiabatic quantum computation is a model of quantum computation that encodes problem instances in the ground state of a Hamiltonian and performs computation by evolving a quantum system slowly from an initial Hamiltonian to a problem Hamiltonian. It connects topics in quantum mechanics, Schrödinger equation, Born–Fock adiabatic concepts, and quantum algorithm design, and has inspired hardware efforts across research labs and industry. The approach has been studied in relation to paradigms developed by Feynman, computational complexity theory associated with Levin and Cook, and experimental platforms linked to institutes such as IBM, Google, and D-Wave Systems.
Adiabatic quantum computation arose from the proposal that a time-dependent Hamiltonian evolution can realize quantum algorithms by tracking the instantaneous ground state, an idea connecting to early work by Born and Fock and formalized in contexts influenced by Farhi and collaborators. The model situates itself among other approaches associated with figures like Deutsch and Shor and has been compared with efforts at institutions including MIT, Caltech, and Microsoft Research. Practical interest accelerated with experimental demonstrations at companies such as D-Wave Systems and collaborations involving laboratories like Los Alamos National Laboratory and NASA.
The foundational principle invokes the adiabatic theorem first explored by Born and Fock, refined in quantum control contexts related to work by Berry and spectral gap analyses used in studies by Wigner and von Neumann. The method encodes an instance into a final Hamiltonian whose ground state represents the solution, while an initial Hamiltonian with an easily prepared ground state anchors the start; these constructions relate to Hamiltonian complexity concepts pioneered by Kitaev and Aharonov. Evolution time must scale inversely with the minimum spectral gap, a notion tied to studies by Vazirani and Aaronson, and stability analyses reference frameworks from Shannon and Kolmogorov in information and complexity theory.
Algorithmic realizations mirror instances of optimization and decision problems studied in classical complexity research by Cook and Levin, including encodings of SAT and instances related to Ising model formulations analyzed by Onsager. Quantum annealing variants connect with work by Toshiba collaborations and algorithm designs influenced by Shor and Grover in search contexts. Reductions demonstrating equivalence to circuit-based algorithms reference proofs involving Aharonov, Landau, and Vazirani and draw on complexity class discussions involving Watrous and Kitaev.
Experimental platforms include superconducting flux qubits developed with contributions from researchers affiliated with UCSB, UMD, and industry groups such as IBM, Google, and D-Wave Systems. Alternative platforms explore trapped ions in laboratories at NIST and Innsbruck, and cold-atom approaches pursued at MIT and Harvard. Engineering challenges connect to fabrication centers at Bell Labs and cryogenic systems from collaborations with NIST and Los Alamos National Laboratory.
Theoretical work established that adiabatic protocols can efficiently simulate quantum circuits and vice versa, with key contributions from Aharonov, Kitaev, and Kempe and complexity class equivalences involving BQP studies by Aaronson and Preskill. Hardness results relate to quantum versions of classical NP-complete problems analyzed in contexts referencing Cook and Levin, and universality proofs connect to Hamiltonian constructions reminiscent of those in Feynman's path integral perspectives and circuit-to-Hamiltonian mappings used by Kitaev.
Decoherence and noise sources draw from condensed-matter studies by Anderson and control-theory approaches developed by groups at Caltech and MIT. Error mitigation leverages dynamical decoupling techniques influenced by Warren and quantum error correction ideas rooted in Shor and Steane, as well as energy-gap protection strategies tied to spectral engineering research by Yablonovitch and Kitaev. Fault-tolerance thresholds have been explored with methods attributed to Preskill and collaborative teams at IBM.
Benchmarks include problem instances run on hardware by D-Wave Systems and comparative analyses reported by research teams at Google and USC. Experimental milestones reference demonstrations in platforms developed at NIST and publications involving collaborations with Los Alamos National Laboratory and NASA. Performance assessments often use metrics and challenge instances inspired by classical benchmarks associated with DIMACS competitions and optimization suites used in studies at MIT and Caltech.