541 research outputs found
A Hybrid Quantum-Classical Paradigm to Mitigate Embedding Costs in Quantum Annealing
Despite rapid recent progress towards the development of quantum computers
capable of providing computational advantages over classical computers, it
seems likely that such computers will, initially at least, be required to run
in a hybrid quantum-classical regime. This realisation has led to interest in
hybrid quantum-classical algorithms allowing, for example, quantum computers to
solve large problems despite having very limited numbers of qubits. Here we
propose a hybrid paradigm for quantum annealers with the goal of mitigating a
different limitation of such devices: the need to embed problem instances
within the (often highly restricted) connectivity graph of the annealer. This
embedding process can be costly to perform and may destroy any computational
speedup. In order to solve many practical problems, it is moreover necessary to
perform many, often related, such embeddings. We will show how, for such
problems, a raw speedup that is negated by the embedding time can nonetheless
be exploited to give a real speedup. As a proof-of-concept example we present
an in-depth case study of a simple problem based on the maximum weight
independent set problem. Although we do not observe a quantum speedup
experimentally, the advantage of the hybrid approach is robustly verified,
showing how a potential quantum speedup may be exploited and encouraging
further efforts to apply the approach to problems of more practical interest.Comment: 30 pages, 6 figure
The Road to Quantum Computational Supremacy
We present an idiosyncratic view of the race for quantum computational
supremacy. Google's approach and IBM challenge are examined. An unexpected
side-effect of the race is the significant progress in designing fast classical
algorithms. Quantum supremacy, if achieved, won't make classical computing
obsolete.Comment: 15 pages, 1 figur
Improving Urban Traffic Mobility via a Versatile Quantum Annealing Model
The growth of cities and the resulting increase in vehicular traffic poses significant challenges to the environment and citizens' Quality of Life. To address these challenges, a new algorithm has been proposed that leverages the Quantum Annealing paradigm and D-Wave's machines to optimize the control of traffic lights in cities. The algorithm considers traffic information collected from a wide urban road network to define activation patterns that holistically reduce congestion. An in-depth analysis of the model's behaviour has been conducted by varying its main parameters. Robustness tests have been performed on different traffic scenarios, and a thorough discussion on how to configure D-Wave's quantum annealers for optimal performance is presented. Comparative tests show that the proposed model outperforms traditional control techniques in several traffic conditions, effectively containing critical congestion situations, reducing their presence, and preventing their formation. The results obtained put in evidence the state-of-the-art of these quantum machines, their actual capabilities in addressing the problem, and opportunities for future applications
Control and calibration strategies for quantum simulation
The modeling and prediction of quantum mechanical phenomena is key to the continued development of chemical, material, and information sciences. However, classical computers are fundamentally limited in their ability to model most quantum effects. An alternative route is through quantum simulation, where a programmable quantum device is used to emulate the phenomena of an otherwise distinct physical system. Unfortunately, there are a number of challenges preventing the widespread application of quantum simulation arising from the imperfect construction and operation of quantum simulators. Mitigating or eliminating deleterious effects is critical for using quantum simulation for scientific discovery. This dissertation develops strategies for implementing quantum simulation and simultaneously mitigating error through the use of device control and calibration. First, an example of the benefits of calibration and control on simulator performance is provided through a case study on simulating the classical Shastry-Sutherland Ising model using quantum annealing. Motivated by the increased precision and accuracy provided by such strategies, a paradigm for parameterized Hamiltonian simulation using quantum optimal control is proposed and validated through numerical simulation. Finally, we apply the methods developed to demonstrate the feasibility of using optimal control for simulation of exotic, dynamical quantum phenomena. Specifically, we demonstrate that quantum optimal control can realize the quantum simulation of string order melting in superconducting quantum devices. These results affirm the utility of quantum optimal control methods for quantum simulation tasks and establish new opportunities for applications of quantum computing to the study of phenomena in quantum physics
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Simulation for Reliability, Hardware Security, and Ising Computing in VLSI Chip Design
The continued scaling of VLSI circuits has provided a wealth of opportunities andchallenges to the VLSI circuit design area. Both these challenges and opportunities, however,require new simulation tools that can enable their solution or exploitation as classicalmethods typically dealt with problem domains with smaller scales or less complexity. Inthis dissertation, simulation methods are presented to address the emerging VLSI designtopics of Electromigration induced aging and Ising computing and are then applied to theapplication areas of hardware security and graph partitioning respectively.The Electromigration aging effect in VLSI circuits is a long-term reliability issueaffecting current carrying metal wires leading to IR drop degradation. Typically, simpleanalytical equations can determine a wire’s effective age or if it will be affected by the EMaging effect at all. However, these classical methods are overly conservative and can lead toover design or unnecessary design iterations. Furthermore, it is expected that the EM agingeffect will become more severe in future Integrated Cirucits (ICs) due to increasing currentdensities and the prevalance of polycrystaline copper atom structures seen at small wiredimensions. For this reason, more comprehensive simulation techniques that can efficientlysimulate the EM effect with less conservative results can help mitigate overdesign andincrease design margins while reducing design iterations.The area of Hardware Security is becoming increasingly important as the chipsupply chain becomes more globalized and the integrity of chips becomes more diffiuclt toverify. Utilizing the accurate simulation techniques for EM, we can utilize this reliabilityeffect to demonstrate how a reliability based attack could be perpatrated. Furthermore, wecan utilize this aging effect as a defense mechanism to help us validate the integrity of anIC and detect counterfeit chips in the component supply chain market.Ising computing is an emerging method of solving combinatorial optimization problemsby simulating the interactions of so-called spin glasses and their interactions. Borrowingconcepts from quantum computing, this methods mimics the quantum interaction betweenspin glasses in such a way that finding a ground state of these spin glass models leadsto the solution of a particular problem. In this dissertation, effective methods of simulatingthe spin glass interactions using General Purpose Graphics Processing Units (GPGPUs)and finding their ground state are developed.In addition to the GPU based Ising model simulations, important combinatorialproblems can be mapped to the Ising model. In this dissertation the Ising solver is appliedto graph partitioning which can be utilized in VLSI design and many other domains as well.Specifically, solvers for the maxcut problem and the balanced min-cut partitioning problemare developed
Comparative study of variations in quantum approximate optimization algorithms for the Traveling Salesman Problem
The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard
problems in computer science to study the effectiveness of computing models and
hardware platforms. In this regard, it is also heavily used as a vehicle to
study the feasibility of the quantum computing paradigm for this class of
problems. In this paper, we tackle the TSP using the quantum approximate
optimization algorithm (QAOA) approach by formulating it as an optimization
problem. By adopting an improved qubit encoding strategy and a layerwise
learning optimization protocol, we present numerical results obtained from the
gate-based digital quantum simulator, specifically targeting TSP instances with
3, 4, and 5 cities. We focus on the evaluations of three distinctive QAOA mixer
designs, considering their performances in terms of numerical accuracy and
optimization cost. Notably, we find a well-balanced QAOA mixer design exhibits
more promising potential for gate-based simulators and realistic quantum
devices in the long run, an observation further supported by our noise model
simulations. Furthermore, we investigate the sensitivity of the simulations to
the TSP graph. Overall, our simulation results show the digital quantum
simulation of problem-inspired ansatz is a successful candidate for finding
optimal TSP solutions.Comment: 18 pages, 6 figures, 3 table
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