Digitized adiabatic quantum computing with a superconducting circuit

By R. Barends, A. Shabani, Lucas Lamata, J. Kelly, A. Mezzacapo, U. L. Heras, Ryan Babbush, Austin G. Fowler, B. Campbell, Yu Chen and 19 more. Nature, 2015

469 citations recorded by the source corpus.

A digitized approach to adiabatic quantum computing, combining the generality of the adiabatic algorithm with the universality of the digital method, is implemented using a superconducting circuit to find the ground states of arbitrary Hamiltonians. Adiabatic quantum computers are analogue machines that, with the help of quantum tunnelling, slowly evolve from a simple input to the desired, more complicated output. Although adiabiatic quantum computers can be very fast at specific tasks, they are limited by noise and errors that cannot be corrected during the computation. In contrast, universal quantum computers are digital devices that use logic gates and allow for error correction. Here, Rami Barends et al. combine the advantages of adiabiatic and universal quantum computers by digitizing an adiabiatic quantum computation. This approach allows for encoding non-stoquastic Hamiltonians, which are crucial for simulating physical and chemical systems with interacting fermions. Quantum mechanics can help to solve complex problems in physics1 and chemistry2, provided they can be programmed in a physical device. In adiabatic quantum computing3,4,5, a system is slowly evolved from the ground state of a simple initial Hamiltonian to a final Hamiltonian that encodes a computational problem. The appeal of this approach lies in the combination of simplicity and generality; in principle, any problem can be encoded. In practice, applications are restricted by limited connectivity, available interactions and noise. A complementary approach is digital quantum computing6, which enables the construction of arbitrary interactions and is compatible with error correction7,8, but uses quantum circuit algorithms that are problem-specific. Here we combine the advantages of both approaches by implementing digitized adiabatic quantum computing in a superconducting system. We tomographically probe the system during the digitized evolution and explore the scaling of errors with system size. We then let the full system find the solution to random instances of the one-dimensional Ising problem as well as problem Hamiltonians that involve more complex interactions. This digital quantum simulation9,10,11,12 of the adiabatic algorithm consists of up to nine qubits and up to 1,000 quantum logic gates. The demonstration of digitized adiabatic quantum computing in the solid state opens a path to synthesizing long-range correlations and solving complex computational problems. When combined with fault-tolerance, our approach becomes a general-purpose algorithm that is scalable.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/41f01162d8a461366272b6a4e77703313dd803bb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

Citations in the corpus

An atom-by-atom assembler of defect-free arbitrary two-dimensional atomic arrays (2016, background): “Many experimental platforms, from trapped ions [3] to superconducting qubits [4], are actively explored.”
Quantum algorithms to simulate many-body physics of correlated fermions. (2017, background): “The controlled-phase gate (47) is implemented off resonantly to achieve the required high fidelity in the Xmon superconducting qubits [28].”
Quantum simulation with interacting photons (2016, background): “The theory for these approaches was worked out in [113] and [10], see also [183] for a related theoretical and [21] for related experimental work.”
Scalable Quantum Simulation of Molecular Energies (2015, methodology): “[32] for details of pulse detuning and shaping).”
Reinforcement Learning in Different Phases of Quantum Control (2017, background): “Reliable quantum state manipulation is essential for many areas of physics ranging from nuclear magnetic resonance experiments [1] and cold atomic systems [2, 3] to trapped ions [4–6], quantum optics [7], superconducting qubits [8], nitrogen vacancy centers [9], and quantum computing [10].”
Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices (2018, background): “trapped ions [7, 57–59], and superconducting qubits [5, 6, 60, 61].”
Warm-starting quantum optimization (2020, methodology): “The quantum approximate optimization algorithm (QAOA) [16–18], inspired by Trotterization of adiabatic quantum computing [19–21], can be run on gate-based quantum computers [22, 23].”
Low-Depth Quantum Simulation of Materials (2017, background): “[32] R Barends, A Shabani, L Lamata, J Kelly, A Mezzacapo, U Las Heras, R Babbush, A Fowler, B Campbell, Y Chen, Z Chen, B Chiaro, A Dunsworth, E Jeffrey, E Lucero, A Megrant, J Mutus, M Neeley, C Neill, P O’Malley, C Quintana, P Roushan, D Sank, A Vainsencher, J Wenner, T White, E Solano, H Neven, ”
An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network (2020, methodology): “Quantum computing is a new computing technique for solving various problems [21].”
A Practical Quantum Instruction Set Architecture (2016, unknown): “0.20170217”
Quantum information processing with superconducting circuits: a review (2016, methodology): “The experiment, as well as that in [37], therefore may present a step on the path to creating an analoguedigital quantum simulator using discrete fermionic modes combined with dis crete or continuous bosonic modes [450].”