 Award ID(s):
 1839196
 Publication Date:
 NSFPAR ID:
 10226553
 Journal Name:
 Conference on Lasers and ElectroOptics
 Page Range or eLocationID:
 AF1I.2
 Sponsoring Org:
 National Science Foundation
More Like this

Quantum networks are complex systems formed by the interaction among quantum processors through quantum channels. Analogous to classical computer networks, quantum networks allow for the distribution of quantum computation among quantum computers. In this work, we describe a quantum walk protocol to perform distributed quantum computing in a quantum network. The protocol uses a quantum walk as a quantum control signal to perform distributed quantum operations. We consider a generalization of the discretetime coined quantum walk model that accounts for the interaction between a quantum walker system in the network graph with quantum registers inside the network nodes. The protocol logically captures distributed quantum computing, abstracting hardware implementation and the transmission of quantum information through channels. Control signal transmission is mapped to the propagation of the walker system across the network, while interactions between the control layer and the quantum registers are embedded into the application of coin operators. We demonstrate how to use the quantum walker system to perform a distributed CNOT operation, which shows the universality of the protocol for distributed quantum computing. Furthermore, we apply the protocol to the task of entanglement distribution in a quantum network.

Abstract Designing quantum algorithms for simulating quantum systems has seen enormous progress, yet few studies have been done to develop quantum algorithms for open quantum dynamics despite its importance in modeling the systemenvironment interaction found in most realistic physical models. In this work we propose and demonstrate a general quantum algorithm to evolve open quantum dynamics on quantum computing devices. The Kraus operators governing the time evolution can be converted into unitary matrices with minimal dilation guaranteed by the Sz.Nagy theorem. This allows the evolution of the initial state through unitary quantum gates, while using significantly less resource than required by the conventional Stinespring dilation. We demonstrate the algorithm on an amplitude damping channel using the IBM Qiskit quantum simulator and the IBM Q 5 Tenerife quantum device. The proposed algorithm does not require particular models of dynamics or decomposition of the quantum channel, and thus can be easily generalized to other open quantum dynamical models.

Quantum computing (QC) is a new paradigm offering the potential of exponential speedups over classical computing for certain computational problems. Each additional qubit doubles the size of the computational state space available to a QC algorithm. This exponential scaling underlies QC’s power, but today’s Noisy IntermediateScale Quantum (NISQ) devices face significant engineering challenges in scalability. The set of quantum circuits that can be reliably run on NISQ devices is limited by their noisy operations and low qubit counts. This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers and quantum computers to enable evaluation of quantum circuits that cannot be run on classical or quantum computers alone. CutQC cuts large quantum circuits into smaller subcircuits, allowing them to be executed on smaller quantum devices. Classical postprocessing can then reconstruct the output of the original circuit. This approach offers significant runtime speedup compared with the only viable current alternative  purely classical simulations  and demonstrates evaluation of quantum circuits that are larger than the limit of QC or classical simulation. Furthermore, in realsystem runs, CutQC achieves much higher quantum circuit evaluation fidelity using small prototype quantum computers than the stateoftheart large NISQ devices achieve. Overall, this hybridmore »

Abstract Realizing the potential of nearterm quantum computers to solve industryrelevant constrainedoptimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrainedoptimization problem and demonstrate the largesttodate execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hammingweightpreserving XY mixer (XYQAOA) on trappedion quantum computer. We successfully execute XYQAOA circuits that restrict the quantum evolution to the inconstraint subspace, using up to 20 qubits and a twoqubit gate depth of up to 159. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the tradeoff between the inconstraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this tradeoff makes choosing good parameters difficult in general. We compare XYQAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constantdepth circuit, and the Quantum Approximate Optimization Algorithm. We discuss the respective tradeoffs of the algorithms and implications for their execution on nearterm quantum hardware.

We give two new quantum algorithms for solving semidefinite programs (SDPs) providing quantum speedups. We consider SDP instances with m constraint matrices, each of dimension n, rank at most r, and sparsity s. The first algorithm assumes an input model where one is given access to an oracle to the entries of the matrices at unit cost. We show that it has run time O~(s^2 (sqrt{m} epsilon^{10} + sqrt{n} epsilon^{12})), with epsilon the error of the solution. This gives an optimal dependence in terms of m, n and quadratic improvement over previous quantum algorithms (when m ~~ n). The second algorithm assumes a fully quantum input model in which the input matrices are given as quantum states. We show that its run time is O~(sqrt{m}+poly(r))*poly(log m,log n,B,epsilon^{1}), with B an upper bound on the tracenorm of all input matrices. In particular the complexity depends only polylogarithmically in n and polynomially in r. We apply the second SDP solver to learn a good description of a quantum state with respect to a set of measurements: Given m measurements and a supply of copies of an unknown state rho with rank at most r, we show we can find in time sqrt{m}*poly(logmore »