Abstract Quantum annealing provides a way of solving optimization problems by encoding them as Ising spin models which are implemented using physical qubits. The solution of the optimization problem then corresponds to the ground state of the system. Quantum tunneling is harnessed to enable the system to move to the ground state in a potentially high non-convex energy landscape. A major difficulty in encoding optimization problems in physical quantum annealing devices is the fact that many real world optimization problems require interactions of higher connectivity, as well as multi-body terms beyond the limitations of the physical hardware. In this work we address the question of how to implement multi-body interactions using hardware which natively only provides two-body interactions. The main result is an efficient circuit design of such multi-body terms using superconducting flux qubits in which effective N -body interactions are implemented using N ancilla qubits and only two inductive couplers. It is then shown how this circuit can be used as the unit cell of a scalable architecture by applying it to a recently proposed embedding technique for constructing an architecture of logical qubits with arbitrary connectivity using physical qubits which have nearest-neighbor four-body interactions. It is further shown that this design is robust to non-linear effects in the coupling loops, as well as mismatches in some of the circuit parameters.
Abstract Quantum annealing provides a way of solving optimization problems by encoding them as Ising spin models which are implemented using physical qubits. The solution of the optimization problem then corresponds to the ground state of the system. Quantum tunneling is harnessed to enable the system to move to the ground state in a potentially high non-convex energy landscape. A major difficulty in encoding optimization problems in physical quantum annealing devices is the fact that many real world optimization problems require interactions of higher connectivity, as well as multi-body terms beyond the limitations of the physical hardware. In this work we address the question of how to implement multi-body interactions using hardware which natively only provides two-body interactions. The main result is an efficient circuit design of such multi-body terms using superconducting flux qubits in which effective N -body interactions are implemented using N ancilla qubits and only two inductive couplers. It is then shown how this circuit can be used as the unit cell of a scalable architecture by applying it to a recently proposed embedding technique for constructing an architecture of logical qubits with arbitrary connectivity using physical qubits which have nearest-neighbor four-body interactions. It is further shown that this design is robust to non-linear effects in the coupling loops, as well as mismatches in some of the circuit parameters.
The field of quantum information has matured and various protocols implementing a quantum computer are being pursued. Most similar to a classical computer is the circuit model. In 2007 Aharonov et al. showed the equivalence between the circuit model and a quantum annealer, and with this proofed the universality of quantum annealing. Here the system starts in an easily preparable ground state and evolves adiabatically to a final ground state which yields the solution of the computational problem. However, equivalence with the circuit model requires multi-local interactions, i.e. interaction terms involving more than two subsystems. Natural interactions are only two-local, hence the construction or simulation of higher order couplers is indispensable for a universal quantum annealer. Also, four-local couplers allow compact implementation of error correction with the Bacon-Shor code. Four-local interactions can further serve as a tool for basic research. Here we show that in a specific flux qubit coupler design without ancilla qubits, four body interactions are induced by virtual coupler excitations. For specific parameter regimes they are even the leading effect and can be tuned up to the GHz range.
Quantum interactions are strikingly different from their classical counterparts, and they lead to counter-intuitive effects such as quantum entanglement. The advances in quantum science and technology over the past two decades have shown that such interactions can be harnessed in quantum devices to address hard computational problems. The field of quantum computation has emerged, and superconducting circuits have become a leading platform for quantum processors. Because of the large design space and the difficulty to simulate design prototypes on a classical computer, a key challenge in quantum processor engineering is to translate desired quantum characteristics into a circuit design. A particular obstacle has been the implementation of interactions between multiple quantum bits (qubits) in one coupling mechanism. Multi-qubit interactions are necessary for a number of applications in quantum annealing, analog quantum simulation, and digital quantum computation. In this work, we develop classical and quantum computational methods to address the challenge of implementing such interactions. We present a computational method for automated design of superconducting circuits. Our method performs a parallelized, closed-loop optimization to design superconducting circuit diagrams that match predefined properties, such as dispersion features and noise sensitivities. We employ it to design a coupler for superconducting flux qubits that mediates 4-qubit interactions. The coupler circuit is then realized experimentally. We find that it has the desired ground state dispersion properties versus flux and that it does not adversely affect the lifetime of a connected flux qubit. Moreover, the coupling concept can be extended by systematically adding loops to the coupler. In a second iteration, we implement a circuit architecture in which the coupler mediates interactions between three flux qubits. The system Hamiltonian is estimated via multi-qubit pulse sequences that implement Ramsey-type interferometry between all neighboring excitation manifolds. We verify that the multi-qubit system exhibits a 3-qubit coupling that is coherently tunable, which is important for practical applications. Finally, we address the challenge that complex multi-qubit circuits are difficult to simulate accurately on a classical computer as they become larger. As a solution, we propose quantum algorithms to design and test the performance of next-generation quantum hardware on a quantum computer. We have therefore addressed the challenge of quantum device design both from a classical optimization and from a quantum computational angle.
The field of quantum information has been a fast growing field of research in the last few decades. The reason for this development is the potentially immense power of quantum computers, capable of solving hard problems in a fraction of the time it would take a classical computer. Quantum annealers are a type of quantum computer that seek to solve computational problems by finding the ground state of a quantum system. This type of quantum computer has been implemented on a large scale using superconducting flux quantum bits, but the computational power is limited. One potential improvement to help in this context is the implementation of multi-body interactions between quantum bits. Many-body interactions would be useful in allowing quantum annealers to solve more complex problems that are relevant in various areas of quantum information and science and implementing quantum error correction. \n \nWe present a superconducting device that implements a strong and tunable in sign and magnitude three-body interaction between superconducting flux quantum bits. The circuit design proposed has vanishing two-body interactions and robustness against noise and parameter variations. This circuit behaves as an ideal computational basis ZZZ coupler in a simulated three-qubit quantum annealing experiment. These properties are confirmed by calculations based on the Born-Oppenheimer approximation, a two-level spin model for the coupling circuit, and full numerical diagonalization. \n \nThe proposed circuit is based on already available technology and therefore could be readily implemented in the next generation of quantum annealing hardware. This work will be relevant for advanced quantum annealing protocols and future developments of high-order many-body interactions in quantum computers and simulators.
The field of quantum information has matured and various protocols implementing a quantum computer are being pursued. Most similar to a classical computer is the circuit model. In 2007 Aharonov et al. showed the equivalence between the circuit model and a quantum annealer, and with this proofed the universality of quantum annealing. Here the system starts in an easily preparable ground state and evolves adiabatically to a final ground state which yields the solution of the computational problem. However, equivalence with the circuit model requires multi-local interactions, i.e. interaction terms involving more than two subsystems. Natural interactions are only two-local, hence the construction or simulation of higher order couplers is indispensable for a universal quantum annealer. Also, four-local couplers allow compact implementation of error correction with the Bacon-Shor code. Four-local interactions can further serve as a tool for basic research. Here we show that in a specific flux qubit coupler design without ancilla qubits, four body interactions are induced by virtual coupler excitations. For specific parameter regimes they are even the leading effect and can be tuned up to the GHz range.
Quantum annealing may provide advantages over simulated annealing on solving some problems such as Kth order binary optimization problem. No feasible architecture exists to implement the high-order optimization problem (K > 2) on current quantum annealing hardware. We propose a two-dimensional quantum annealing architecture to solve the 4th order binary optimization problem by encoding four-qubit interactions within the coupled local fields acting on a set of physical qubits. All possible four-body coupling terms for an N-qubit system can be implemented through this architecture and are readily realizable with the existing superconducting circuit technologies. The overhead of the physical qubits is O(N4), which is the same as previously proposed architectures in four-dimensional space. The equivalence between the optimization problem Hamiltonian and the executable Hamiltonian is ensured by a gauge invariant subspace of the experimental system. A scheme to realize local gauge constraint by single ancillary qubit is proposed.
Critical decision-making issues in science, engineering, and industry are based on combinatorial optimization; however, its application is inherently limited by the NP-hard nature of the problem. A specialized paradigm of analogue quantum computing, quantum annealing (QA), has been proposed to solve these problems by encoding optimization problems into physical energy landscapes and solving them by quantum tunnelling systematically through exploration of solution space. This is a critical review that summarizes the current applications of quantum annealing to combinatorial optimization and includes a theoretical background, hardware designs, algorithm implementation strategies, encoding and embedding schemes, protocols to benchmark quantum annealing, areas of implementation, and links with the quantum algorithms implementation with gate-based hardware and classical solvers. We develop a unified framework, relating adiabatic quantum dynamics, Ising and QUBO models, stoquastic and non-stoquastic Hamiltonians, and diabatic transitions to modern flux-qubit annealers (Chimera, Pegasus, Zephyr topologies), and emergent architectures (Lechner-Hauke-Zoller systems, Rydberg atom platforms), and hybrids of quantum and classical computation. Through our analysis, we find that overhead in embedding and encoding is the largest determinant of the scalability and performance (this is not just the number of qubits). Minor embeddings also usually have a physical qubit count per logical variable of between 5 and 12 qubits, which limits effective problem capacity by 80-92% and, due to chain-breaking errors, compromises the quality of solutions.