Quantum Processor Optimization Techniques

Thought you knew which #quantumcomputers were best for #quantum optimization? The latest results from Q-CTRL have reset expectations for what is possible on today's gate-model machines. Q-CTRL today announced newly published results that demonstrate a boost of more than 4X in the size of an optimization problem that can be accurately solved, and show for the first time that a utility-scale IBM quantum computer can outperform competitive annealer and trapped ion technologies. Full, correct solutions at 120+ qubit scale for classically nontrivial optimizations! Quantum optimization is one of the most promising quantum computing applications with the potential to deliver major enhancements to critical problems in transport, logistics, machine learning, and financial fraud detection. McKinsey suggests that quantum applications in logistics alone are worth over $200-500B/y by 2035 – if the quantum sector can successfully solve them. Previous third-party benchmark quantum optimization experiments have indicated that, despite their promise, gate-based quantum computers have struggled to live up to their potential because of hardware errors. In previous tests of optimization algorithms, the outputs of the gate-based quantum computers were little different than random outputs or provided modest benefits under limited circumstances. As a result, an alternative architecture known as a quantum annealer was believed – and shown in experiments – to be the preferred choice for exploring industrially relevant optimization problems. Today’s quantum computers were thought to be far away from being able to solve quantum optimization problems that matter to industry. Q-CTRL’s recent results upend this broadly accepted industry narrative by addressing the error challenge. Our methods combine innovations in the problem’s hardware execution with the company’s performance-management infrastructure software run on IBM’s utility-scale quantum computers. This combination delivered improved performance previously limited by errors with no changes to the hardware. Direct tests showed that using Q-CTRL’s novel technology, a quantum optimization problem run on a 127-qubit IBM quantum computer was up to 1,500 times more likely than an annealer to return the correct result, and over 9 times more likely to achieve the correct result than previously published work using trapped ions These results enable quantum optimization algorithms to more consistently find the correct solution to a range of challenging optimization problems at larger scales than ever before. Check out the technical manuscript! https://lnkd.in/gRYAFsRt

New blog post based on a collaboration between Continental and Entropica Labs to explore the performance of the quantum approximate optimization algorithm (QAOA) on quantum hardware accessible by Amazon Braket. In this case, the team took a look at a common optimization problem on the IonQ Harmony device using the hybrid jobs feature and includes all the code so you can follow along at home too! Thanks to Leonardo Disilvestro, Chiew Shao Hen, Jing Yi Chan, and Ewan Munro from Entropica Labs along with Sighard Schräbler and Volker Aken, van from Continental and Perminder Singh from Amazon Web Services (AWS) for sharing their work https://lnkd.in/gSuYGqVs Timo Bierwirth Vijitha Chekuri #quantumcomputing

I’m excited to share the results of a great #quantumcomputing collaboration between Global Technology Applied Research at JPMorgan Chase & Co. and Infleqtion! In this work, we propose and benchmark Q-CHOP, a new #quantum adiabatic algorithm applicable to a broad range of constrained #optimization problems. Our algorithm leverages the observation that for many problems, while the best solution is difficult to find, the worst feasible (constraint-satisfying) solution is known. The basic idea is to to enforce a Hamiltonian constraint at all times, thereby restricting evolution to the subspace of feasible states, and slowly "rotate" an objective Hamiltonian to trace an adiabatic path from the worst feasible state to the best feasible state. Through extensive benchmarks, we show that our algorithm consistently outperforms the state-of-the-art adiabatic approach on a wide range of problems, including a real-world financial use case, namely bond #exchangetradedfunds (#etf) basket optimization. See our arXiv paper for more details: https://lnkd.in/epuBs98V Authors: Michael A Perlin, Ruslan Shaydulin, Benjamin Hall, Pierre Minssen, Changhao Li, Kabir Dubey, Rich Rines, Eric Anschuetz, Marco Pistoia, and Pranav Gokhale