1QBit Raises $45M Series B Round from Fujitsu, Accenture, and Allianz to Advance Industry Applications of Quantum Computing

Strategic investment and revenue contracts fund continued development of 1QBit’s hardware-agnostic software platform and applications for solving industry’s most demanding computational challenges. Vancouver, Canada – November 28, 2017 1QB Information Technologies Inc. (1QBit), a software company solving industry’s most demanding computational challenges using the most advanced quantum and classical hardware available, today announced the close …
READ MORE

1QBit Raises $45M Series B Round from Fujitsu, Accenture, and Allianz to Advance Industry Applications of Quantum Computing

Strategic investment and revenue contracts fund continued development of 1QBit’s hardware-agnostic software platform and applications for solving industry’s most demanding computational challenges. Vancouver, Canada – November 28, 2017 1QB Information Technologies Inc. (1QBit), a software company solving industry’s most demanding computational challenges using the most advanced quantum and classical hardware available, today announced the close …
READ MORE

Fujitsu and 1QBit Collaborate on Quantum Inspired AI Cloud Service

May 15, 2017 The combination of Fujitsu’s digital annealer hardware and 1QBit’s software will enable advances in machine learning and large-scale optimization problems. Vancouver, Canada and Tokyo, Japan — Fujitsu Limited and 1QB Information Technologies Inc. (1QBit) announced that starting today they will collaborate on applying quantum-inspired technology to the field of artificial intelligence (AI), focusing …
READ MORE

Long-Short Minimum Risk Parity Optimization Using a Quantum or Digital Annealer

By Gili Rosenberg & Maxwell Rounds

In portfolio optimization, many weight allocation strategies result in long-only positions. We show how it is possible to formulate and solve an optimization problem that assigns a direction (long or short) to each weight allocation, such that the variance is minimized or maximized. This optimization problem can then be solved by many solvers, including D-Wave Systems’ quantum annealer, the Fujitsu Digital Annealer, and others. We present backtested results for three datasets solved using a tabu solver run on a CPU. Our results suggest that by utilizing intelligent shorting, this method is able to reduce the volatility of long-only strategies, leading to shorter maximum drawdowns and higher Sharpe ratios, albeit with a higher turnover. We also discuss possible extensions of this model such that it attempts to achieve market neutrality, sector neutrality, or takes into account a shorting aversion.

WHITE PAPER

Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer

By Maliheh Aramon, Gili Rosenberg, Elisabetta Valiante, Toshiyuki Miyazawa, Hirotaka Tamura, & Helmut G. Katzgraber

The Fujitsu Digital Annealer (DA) is designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS hardware and currently solves problems of up to 1024 variables. The DA’s algorithm is currently based on simulated annealing; however, it differs from it in its utilization of an efficient parallel-trial scheme and a dynamic escape mechanism. In addition, the DA exploits the massive parallelization that custom application-specific CMOS hardware allows. We compare the performance of the DA to simulated annealing and parallel tempering with isoenergetic cluster moves on two-dimensional and fully connected spin-glass problems with bimodal and Gaussian couplings. These represent the respective limits of sparse versus dense problems, as well as high-degeneracy versus low-degeneracy problems. Our results show that the DA currently exhibits a time-to-solution speedup of roughly two orders of magnitude for fully connected spin-glass problems with bimodal or Gaussian couplings, over the single-core implementations of simulated annealing and parallel tempering Monte Carlo used in this study. The DA does not appear to exhibit a speedup for sparse two-dimensional spin-glass problems, which we explain on theoretical grounds. We also benchmarked an early implementation of the Parallel Tempering DA. Our results suggest an improved scaling over the other algorithms for fully connected problems of average difficulty with bimodal disorder. The next generation of the DA is expected to be able to solve fully connected problems up to 8192 variables in size. This would enable the study of fundamental physics problems and industrial applications that were previously inaccessible using standard computing hardware or special-purpose quantum annealing machines.

PDF    ARXIV PREPRINT

WSJ: 1QBit Seeks to Fill Quantum Computing’s Software Void

July 10, 2017 The Wall Street Journal interviewed 1QBit CEO Andrew Fursman to gain an understanding of 1QBit’s business model and role in the quantum ecosystem: “In this new model, vendors develop the quantum computing technology and specialist firms such as 1QBit build software and software extensions to make quantum computers accessible through traditional programming …
READ MORE

Company Backgrounder

1QBit is a global leader in advanced computing and software development. Founded in 2012, 1QBit builds hardware-agnostic software and partners with companies taking on computationally exhaustive problems in advanced materials, life sciences, energy, and finance. Trusted by Fortune 500 companies and top research institutions internationally, 1QBit is seen as an industry leader in quantum computing, …
READ MORE