PDF《Finding low-energy conformations of》

rather quantum fluctuations are a tool we use to solve the optimization problem. The QA protocol performed here is also known as adiabatic quantum computation (AQC)17,23 . Of all the quantum-computational models, AQC is perhaps the most naturally suited for studying and solving optimization problems17,24 . For the experiments presented here, the small finite temperature of the superconducting device is SUBJECT AREAS: QUANTUM PHYSICS BIOPHYSICS STATISTICAL PHYSICS, THERMODYNAMICS AND NONLINEAR DYNAMICS COMPUTATIONAL BIOLOGY Received

10 May

2012 Accepted

16 July

2012 Published

13 August

2012 Correspondence and requests for materials should be addressed to A.A.-G. (aspuru@ chemistry.harvard. edu) SCIENTIFIC REPORTS |

2 :

571 | DOI: 10.1038/srep00571

1 enough to make the process less coherent than the original formula- tion of AQC, where the theoretical limit of zero temperature and quasi-adiabaticity are usually assumed17,23 . As we show in the discus- sion, numerical simulations including these unavoidable envir- onmental effects accurately reproduce our experimental results. Experimental implementations of QA or AQC are limited either by the number of qubits available in state-of-the-art quantum devices or by the programmability required to fulfill the problem specifica- tion. For example, the first realization of AQC was performed on a three-qubit NMR quantum device25 and newer NMR implementa- tions involve four qubit experiments26 . Other experimental realiza- tions of spin systems have been based on measuring bulk magnetization properties of the systems in which there is no control over the individual spins and the couplings among them19,27,28 . Quantum architectures using superconducting qubits29C36 offer promising device scalability while maintaining the ability to control individual qubits and the strength of their interaction couplings. During the preparation of this manuscript, an 84-qubit experimental determination of Ramsey numbers with quantum annealing was performed37 , underscoring the programmable capabilities of the device for problems with over

80 qubits. In this letter, we present a quantum annealing experimental implementation of lattice protein models with general (Miyazawa-Jernigan38 ) interactions among the amino acids. Even though the cases presented here still can be solved on a classical computer by exact enumeration (the six-amino-acid problem has only

40 possible configurations), it is remarkable that the device anneals to the ground state of a search space of

281 possible computational outcomes. This study provides a proof-of-principle that optimization of biophysical problems such as protein folding can be studied using quantum mechanical devices. Figure

1 | Device architecture and qubit connectivity. The array of superconducting quantum bits is arranged in

4 3

4 unit cells that consist of

8 quantum bits each. Within a unit cell, each of the

4 qubits in the left-hand partition (LHP) connects to all

4 qubits in the right-hand partition (RHP), and vice versa. A qubit in the LHP (RHP) also connects to the corresponding qubit in the LHP (RHP) of the units cells above and below (to the left and right of) it. (a) Qubits are labeled from

0 to

127 and edges between qubits represent couplers with programmable coupling strengths. Grey qubits indicate the

115 usable qubits, while vacancies indicate qubits under calibration which were not used. The larger experiments (Experiments 1,2, and 4) were performed on this chip, while the three remaining smaller experiments were run on other chips with the same architecture. (b) Embedding and qubit connectivity for Experiment 4, coloring the