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arXiv:quant-ph/0001106

Quantum Computation by Adiabatic Evolution

Edward Farhi, Jeffrey Goldstone, Sam Gutmann, Michael Sipser

Published Jan 28, 2000• 536 citations
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Abstract

We give a quantum algorithm for solving instances of the satisfiability problem, based on adiabatic evolution. The evolution of the quantum state is governed by a time-dependent Hamiltonian that interpolates between an initial Hamiltonian, whose ground state is easy to construct, and a final Hamiltonian, whose ground state encodes the satisfying assignment. To ensure that the system evolves to the desired final ground state, the evolution time must be big enough. The time required depends on the minimum energy difference between the two lowest states of the interpolating Hamiltonian. We are unable to estimate this gap in general. We give some special symmetric cases of the satisfiability problem where the symmetry allows us to estimate the gap and we show that, in these cases, our algorithm runs in polynomial time.

DOI
10.48550/arxiv.quant-ph/0001106

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We give a quantum algorithm for solving instances of the satisfiability problem, based on adiabatic evolution. The evolution of the quantum state is governed by a time-dependent Hamiltonian that interpolates between an initial Hamiltonian, whose ground state is easy to construct, and a final Hamiltonian, whose ground state encodes the satisfying assignment. To ensure that the system evolves to the desired final ground state, the evolution time must be big enough. The time required depends on the minimum energy difference between the two lowest states of the interpolating Hamiltonian. We are unable to estimate this gap in general. We give some special symmetric cases of the satisfiability problem where the symmetry allows us to estimate the gap and we show that, in these cases, our algorithm runs in polynomial time.
We give a quantum algorithm for solving instances of the satisfiability problem, based on adiabatic evolution. The evolution of the quantum state is governed by a time-dependent Hamiltonian that interpolates between an initial Hamiltonian, whose ground state is easy to construct, and a final Hamiltonian, whose ground state encodes the satisfying assignment. To ensure that the system evolves to the desired final ground state, the evolution time must be big enough. The time required depends on the minimum energy difference between the two lowest states of the interpolating Hamiltonian. We are unable to estimate this gap in general. We give some special symmetric cases of the satisfiability problem where the symmetry allows us to estimate the gap and we show that, in these cases, our algorithm runs in polynomial time.
Edward Farhi, Jeffrey Goldstone, Sam Gutmann, Michael Sipser give a quantum algorithm for solving instances of (e) the satisfiability problem, based on (e) adiabatic evolution. The evolution of the quantum state is governed by (e&eb)a time-dependent Hamiltonian that interpolates between (e&eb) an initial Hamiltonian, whose ground state is easy to construct, and a final Hamiltonian, whose ground state encodes (e) the satisfying assignment. To ensure that the system evolves to *(e&eb) the desired final ground state, the evolution time must be (eb) big enough. Time required depends on (=) the minimum energy differences between (e&eb) the two lowest.... The full paper: http://www.iiste.org/PDFshare/APTA-PAGENO-427314-431866.pdf
Quantum computation by adiabatic evolution, as described in quant-ph/0001106, will solve satisfiability problems if the running time is long enough. In certain special cases (that are classically easy) we know that the quantum algorithm requires a running time that grows as a polynomial in the number of bits. In this paper we present numerical results on randomly generated instances of an NP-complete problem and of a problem that can be solved classically in polynomial time. We simulate a quantum computer (of up to 16 qubits) by integrating the Schrodinger equation on a conventional computer. For both problems considered, for the set of instances studied, the required running time appears to grow slowly as a function of the number of bits.
Edward Farhi, Jeffrey Goldstone, Sam Gutmann, Michael Sipser give a quantum algorithm for solving instances of (e) the satisfiability problem, based on (e) adiabatic evolution. The evolution of the quantum state is governed by (e&eb)a time-dependent Hamiltonian that interpolates between (e&eb) an initial Hamiltonian, whose ground state is easy to construct, and a final Hamiltonian, whose ground state encodes (e) the satisfying assignment. To ensure that the system evolves to *(e&eb) the desired final ground state, the evolution time must be (eb) big enough. Time required depends on (=) the minimum energy differences between (e&eb) the two lowest states of the interpolating Hamiltonian. Authors state they are unable to estimate this gap in general. They give some special symmetric cases of (e) the satisfiability problem where (e) the symmetry allows... The full paper: http://www.iiste.org/PDFshare/APTA-PAGENO-462105-466356.pdf
Adiabatic quantum computation is based on the adiabatic evolution of quantum systems. We analyze a particular class of quantum adiabatic evolutions where either the initial or final Hamiltonian is a one-dimensional projector Hamiltonian on the corresponding ground state. The minimum-energy gap, which governs the time required for a successful evolution, is shown to be proportional to the overlap of the ground states of the initial and final Hamiltonians. We show that such evolutions exhibit a rapid crossover as the ground state changes abruptly near the transition point where the energy gap is minimum. Furthermore, a faster evolution can be obtained by performing a partial adiabatic evolution within a narrow interval around the transition point. These results generalize and quantify earlier works.
In quantum adiabatic evolution algorithms, the quantum computer follows the ground state of a slowly varying Hamiltonian. The ground state of the initial Hamiltonian is easy to construct; the ground state of the final Hamiltonian encodes the solution of the computational problem. These algorithms have generally been studied in the case where the "straight line" path from initial to final Hamiltonian is taken. But there is no reason not to try paths involving terms that are not linear combinations of the initial and final Hamiltonians. We give several proposals for randomly generating new paths. Using one of these proposals, we convert an algorithmic failure into a success.