Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)--based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.

It's tempting to see AI as a solution to building a super-success investment engine. After all, if AI can solve text-to-speech or self-driving cars or landing rockets vertically, couldn't an artificially intelligent investing engine with access to all stock market, economy, weather, and trends data vastly outpace human investors and guarantee massive returns? And won't we be able to simply ask Alexa to buy a stock that's going to triple in value in six months? Well, never say never, but it's unlikely. One is that investment AI engines are returning benefits right now, but not Everest-sized performance that will blow your financial socks off and make you fire your investment advisor.

SoftBank is investing $146 million in the South Korean artificial intelligence (AI) company Qraft Technologies Inc. to help it expand into the U.S. As The Wall Street Journal (WSJ) reported Monday (Jan. The companies declined to disclose Qraft's valuation, per the WSJ. SoftBank, based in Tokyo, is one of the largest tech investors in the world, managing a portfolio in excess of $100 billion. Qraft has 50 employees, most of whom work on the company's AI project and who own about a third of the business, with outside investors controlling the rest. "SoftBank [now] makes up a large portion of that," Robert Nestor, the U.S. CEO of Qraft., told the WSJ.

Founded in 2016 by its chief executive, Marcus Hyung-Sik Kim, the Seoul-based firm plans to use the investment to further its expansion into the U.S. and other key markets, said Robert Nestor, Qraft's U.S. CEO. The companies declined to disclose Qraft's valuation. Tokyo's SoftBank is one of the world's largest investors in technology companies, with its Vision Fund and a successor managing a portfolio of more than $100 billion. Asset managers, once skeptical of the value of AI and mindful of their staffs' concerns that the programs would replace human stock- and bond-pickers, are now looking to add data-analysis tools that can help them combat chronic underperformance and justify the fees they charge investors. The industry's awakening has triggered an arms race to hire the programmers who can develop those tools and spot the market signals hidden in the data.

The growing appreciation that human stockpickers struggle to outperform their benchmark indices has helped fuel a massive surge in assets held by passively managed exchange traded funds. Now some companies are hoping to show that artificial intelligence can finally give them an edge. The technology is fast-evolving but at least two fund managers, EquBot and Qraft Technologies, running dedicated AI-powered ETFs are claiming early success, even though some of their AI models' decisions might have required strong nerves to implement. For example, the team at Qraft, which offers four AI-powered ETFs, listed on NYSE Arca, witnessed its technology build a weighting of 14.7 per cent in Tesla in its Qraft AI-Enhanced US Large Cap Momentum ETF (AMOM) in August last year, but when it rebalanced a month later on September 1 it sold it all. The ETF began buying Tesla again in November, amassing a stake of 7.6 per cent by January this year, but in the February rebalancing it sold the entire holding once again.