arXiv subjects

Vadim Smelyanskiy

Publications and source records attributed to Vadim Smelyanskiy.

23 records · Page 2Linked to original sources

arXiv2016

What is the Computational Value of Finite Range Tunneling?

Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic exploiting tunneling. Here, we demonstrate how finite range tunneling can provide considerable computational advantage. For a crafted problem designed to have tall and narrow energy barriers separating local minima, the D-Wave 2X quantum annealer achieves significant runtime advantages relative to Simulated Annealing (SA). For instances with 945 variables, this results in a time-to-99%-success-probability that is $\sim 10^8$ times faster than SA running on a single processor core. We also compared physical QA with Quantum Monte Carlo (QMC), an algorithm that emulates quantum tunneling on classical processors. We observe a substantial constant overhead against physical QA: D-Wave 2X again runs up to $\sim 10^8$ times faster than an optimized implementation of QMC on a single core. We note that there exist heuristic classical algorithms that can solve most instances of Chimera structured problems in a timescale comparable to the D-Wave 2X. However, we believe that such solvers will become ineffective for the next generation of annealers currently being designed. To investigate whether finite range tunneling will also confer an advantage for problems of practical interest, we conduct numerical studies on binary optimization problems that cannot yet be represented on quantum hardware. For random instances of the number partitioning problem, we find numerically that QMC, as well as other algorithms designed to simulate QA, scale better than SA. We discuss the implications of these findings for the design of next generation quantum annealers.

quant-ph↗

arXiv2014

Bayesian Network Structure Learning Using Quantum Annealing

We introduce a method for the problem of learning the structure of a Bayesian network using the quantum adiabatic algorithm. We do so by introducing an efficient reformulation of a standard posterior-probability scoring function on graphs as a pseudo-Boolean function, which is equivalent to a system of 2-body Ising spins, as well as suitable penalty terms for enforcing the constraints necessary for the reformulation; our proposed method requires $\mathcal O(n^2)$ qubits for $n$ Bayesian network variables. Furthermore, we prove lower bounds on the necessary weighting of these penalty terms. The logical structure resulting from the mapping has the appealing property that it is instance-independent for a given number of Bayesian network variables, as well as being independent of the number of data cases.

quant-ph↗

arXiv2014

Quantum Optimization of Fully-Connected Spin Glasses

The Sherrington-Kirkpatrick model with random $\pm1$ couplings is programmed on the D-Wave Two annealer featuring 509 qubits interacting on a Chimera-type graph. The performance of the optimizer compares and correlates to simulated annealing. When considering the effect of the static noise, which degrades the performance of the annealer, one can estimate an improvement on the comparative scaling of the two methods in favor of the D-Wave machine. The optimal choice of parameters of the embedding on the Chimera graph is shown to be associated to the emergence of the spin-glass critical temperature of the embedded problem.

cond-mat.dis-nn↗

arXiv2010

Hazards Induced by Breach of Liquid Rocket Fuel Tanks: Conditions and Risks of Cryogenic Liquid Hydrogen-Oxygen Mixture Explosions

We analyze the data of purposeful rupture experiments with LOx and LH2 tanks, the Hydrogen-Oxygen Vertical Impact (HOVI) tests that were performed to clarify the ignition mechanisms, the explosive power of cryogenic H2/Ox mixtures under different conditions, and to elucidate the puzzling source of the initial formation of flames near the intertank section during the Challenger disaster. We carry out a physics-based analysis of general explosions scenarios for cryogenic gaseous H2/Ox mixtures and determine their realizability conditions, using the well-established simplified models from the detonation and deflagration theory. We study the features of aerosol H2/Ox mixture combustion and show, in particular, that aerosols intensify the deflagration flames and can induce detonation for any ignition mechanism. We propose a cavitation-induced mechanism of self-ignition of cryogenic H2/Ox mixtures that may be realized when gaseous H2 and Ox flows are mixed with a liquid Ox turbulent stream, as occurred in all HOVI tests. We present an overview of the HOVI tests to make conclusion on the risk of strong explosions in possible liquid rocket incidents and provide a semi-quantitative interpretation of the HOVI data based on aerosol combustion. We uncover the most dangerous situations and discuss the foreseeable risks which can arise in space missions and lead to tragic outcomes. Our analysis relates to only unconfined mixtures that are likely to arise as a result of liquid propellant space vehicle incidents.

physics.chem-ph↗

arXiv2002

Total suppression of a large spin tunneling barrier in quantum adiabatic computation

We apply a quantum adiabatic evolution algorithm to a combinatorial optimization problem where the cost function depends entirely on the of the number of unit bits in a n-bit string (Hamming weight). The solution of the optimization problem is encoded as a ground state of the problem Hamiltonian H_p for the z-projection of a total spin-n/2. We show that tunneling barriers for the total spin can be completely suppressed during the algorithm if the initial Hamiltonian has its ground state extended in the space of the z-projections of the spin. This suppression takes place even if the cost function has deep and well separated local minima. We provide an intuitive picture for this effect and show that it guarantees the polynomial complexity of the algorithm in a very broad class of cost functions. We suggest a simple example of the Hamiltonian for the adiabatic evolution: H(tau) = (1-tau) hat S_{x}^{2} + tau H_p, with parameter tau slowly varying in time between 0 and 1. We use WKB analysis for the large spin to estimate the minimum energy gap between the two lowest adiabatic eigenvalues of H(tau).

quant-ph↗