A theorist who helped develop adiabatic quantum computation and the quantum approximate optimization algorithm, exploring ways quantum dynamics can be used to solve computational problems.
Massachusetts Institute of TechnologyarXivarXivFarhi’s research expands the set of ways to organize quantum computation. Adiabatic computation encodes a problem in the gradual evolution of a physical system, while the quantum approximate optimization algorithm uses alternating operations and adjustable parameters. Both approaches connect mathematical problems with quantum dynamics. Their inclusion here reflects the influence of these frameworks on research; it does not assume that either delivers a general practical advantage over the best classical methods.
Massachusetts Institute of TechnologyarXivarXivThe work behind the recognition
Work, in context- 2000
Adiabatic quantum computation
With Jeffrey Goldstone, Sam Gutmann and Michael Sipser, Farhi proposed a computing approach based on slowly changing a Hamiltonian so that its ground state encodes a solution. The proposal established a framework for studying computation through quantum adiabatic evolution.
Coauthor of the adiabatic-computation proposal with Jeffrey Goldstone, Sam Gutmann and Michael Sipser.
Source-supported recordarXiv - 2014
The quantum approximate optimization algorithm
Farhi, Goldstone and Gutmann introduced QAOA, a parameterized quantum algorithm for approximate combinatorial optimization. Its alternating operations provide a structured family of trial states; performance depends on the problem, circuit depth, parameters and comparison method.
Coauthor of QAOA with Jeffrey Goldstone and Sam Gutmann; no general practical advantage is attributed.
Source-supported recordarXiv
Keep in perspective
No general or demonstrated commercial quantum advantage is claimed for QAOA or adiabatic computation.
The evidence map
Both views link each contribution to its sources. Choose a contribution on the map or read them all in the list.
Adiabatic quantum computation
Quantum Computation by Adiabatic Evolution
Supports: Farhi–Goldstone–Gutmann–Sipser proposal and adiabatic approach.
Published 28 Jan 2000 · Checked 19 Sept 2026Credit & limits: Coauthor of the adiabatic-computation proposal with Jeffrey Goldstone, Sam Gutmann and Michael Sipser.
Read the full contribution| Contribution | Evidence & what it supports | Credit & limits |
|---|---|---|
| Adiabatic quantum computation2000 | Primary paperQuantum Computation by Adiabatic Evolution Farhi–Goldstone–Gutmann–Sipser proposal and adiabatic approach. Published 28 Jan 2000 · Checked 19 Sept 2026 | Coauthor of the adiabatic-computation proposal with Jeffrey Goldstone, Sam Gutmann and Michael Sipser. |
| The quantum approximate optimization algorithm2014 | Primary paperA Quantum Approximate Optimization Algorithm QAOA authorship and approximate combinatorial optimization framework. Published 14 Nov 2014 · Checked 19 Sept 2026 | Coauthor of QAOA with Jeffrey Goldstone and Sam Gutmann; no general practical advantage is attributed. |
Follow the evidence
3 sourcesPrimary papers, institutional records and attributed announcements. Each source supports the claims linked above.
- Massachusetts Institute of TechnologyInstitutional record
Edward Farhi
MIT emeritus identification, Google researcher status and research history.
Checked 19 Sept 2026 - arXivPrimary paper
Quantum Computation by Adiabatic Evolution
Farhi–Goldstone–Gutmann–Sipser proposal and adiabatic approach.
Published 28 Jan 2000 · Checked 19 Sept 2026 - arXivPrimary paper
A Quantum Approximate Optimization Algorithm
QAOA authorship and approximate combinatorial optimization framework.
Published 14 Nov 2014 · Checked 19 Sept 2026