Google's quantum supremacy announcement has received broad questions from academia and industry due to the debatable estimate of 10,000 years' running time for the classical simulation task on the Summit supercomputer. Has quantum supremacy already come? Or will it come in one or two decades later? To avoid hasty advertisements of quantum supremacy by tech giants or quantum startups and eliminate the cost of dedicating a team to the classical simulation task, we advocate an open-source approach to maintain a trustable benchmark performance. In this paper, we take a reinforcement learning approach for the classical simulation of quantum circuits and demonstrate its great potential by reporting an estimated simulation time of less than 4 days, a speedup of 5.40x over the state-of-the-art method. Specifically, we formulate the classical simulation task as a tensor network contraction ordering problem using the K-spin Ising model and employ a novel Hamiltonina-based reinforcement learning algorithm. Then, we establish standard criteria to evaluate the performance of classical simulation of quantum circuits. We develop a dozen of massively parallel environments to simulate quantum circuits.

What makes a quantum computer good? Claims that one quantum computer is better than another rest on terms like quantum advantage or quantum supremacy, fault-tolerance or qubits with better coherence - what does it all mean? Eleven years ago, I was just getting a start on my PhD in theoretical physics, and to be honest with you I never thought about quantum computers, or writing about them, at all. Meanwhile, staff were hard at work putting together the world's first " Quantum computer buyer's guide " (we've always been ahead of the curve). Looking through it reveals what a different time it was - John Martinis at University of California, Santa Barbara got a shout out for working on an array of only nine qubits, and just last week he was awarded the Nobel Prize in Physics .

Google's "quantum supremacy" announcement [3] has received broad questions from academia and industry due to the debatable estimate of 10, 000 years' running time for the classical simulation task on the Summit supercomputer. Has "quantum supremacy" already come? Or will it come in one or two decades later? To avoid hasty advertisements of "quantum supremacy" by tech giants or quantum startups and eliminate the cost of dedicating a team to the classical simulation task, we advocate an open-source approach to maintain a trustable benchmark performance. In this paper, we take a reinforcement learning approach for the classical simulation of quantum circuits and demonstrate its great potential by reporting an estimated simulation time of less than 4 days, a speedup of 5 .40

Google's quantum supremacy announcement has received broad questions from academia and industry due to the debatable estimate of 10,000 years' running time for the classical simulation task on the Summit supercomputer. Has quantum supremacy already come? Or will it come in one or two decades later? To avoid hasty advertisements of quantum supremacy by tech giants or quantum startups and eliminate the cost of dedicating a team to the classical simulation task, we advocate an open-source approach to maintain a trustable benchmark performance. In this paper, we take a reinforcement learning approach for the classical simulation of quantum circuits and demonstrate its great potential by reporting an estimated simulation time of less than 4 days, a speedup of 5.40x over the state-of-the-art method.

Google has taken a major step towards creating a quantum computer, after unveiling a'mind-boggling' quantum chip - its most powerful yet. Measuring 1.5-inches (4cm) – a little larger than an After Eight mint – the chip takes five minutes to complete tasks that would take conventional computers 10 septillion years. Crucially, Google's chip has demonstrated the ability to reduce computational errors exponentially as it scales up – a feat that has eluded researchers for nearly 30 years. Ultimately, the aim is to build a'commercial' quantum computer – one that could be purchased by members of the public and used in labs, offices and even homes. As this is still a decade or two away at least, for now, firms like Google and IBM are building'experimental' quantum computers that are still in the research and development phase. In the near future, scientists expect quantum computers will replace the'classical' computers at our desks and revolutionise our lives.

In 2019, Google claimed that its Sycamore quantum computer could perform calculations that would take even the world's most powerful classical supercomputer 10,000 years to complete – but now it seems that a non-quantum computer crunches the numbers several times faster than Google's machine, and uses less energy doing so. Quantum computers have the potential to carry out some kinds of calculations vastly more quickly than classical computers, but are still in their infancy. Google announced in 2019 that Sycamore had achieved "quantum supremacy" – the point at which a quantum computer can…

For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used based on multi-agent reinforcement learning (MARL). However, the MARL-based algorithms suffer from huge parameter utilization and convergence difficulties with many agents. To tackle these problems, a quantum MARL (QMARL) algorithm based on the concept of actor-critic network is proposed, which is beneficial in terms of scalability, to deal with the limitations in the noisy intermediate-scale quantum (NISQ) era. Additionally, our QMARL is also beneficial in terms of efficient parameter utilization and fast convergence due to quantum supremacy. Note that the reward in our QMARL is defined as task precision over computation time in multiple agents, thus, multi-agent cooperation can be realized. For further improvement, an additional technique for scalability is proposed, which is called projection value measure (PVM). Based on PVM, our proposed QMARL can achieve the highest reward, by reducing the action dimension into a logarithmic-scale. Finally, we can conclude that our proposed QMARL with PVM outperforms the other algorithms in terms of efficient parameter utilization, fast convergence, and scalability.

On a hot summer's morning in July, Robert Oppenheimer stood in a control bunker in New Mexico and watched the results of his Manhattan Project burn the desert sand, transforming it into a mild but lightly radioactive green glass. Years later, when asked what went through his head when he saw that great grey cloud rise out of the sand, he said he was reminded of Hindu Scripture, the line from Vishnu: 'Now I am become Death, the destroyer of worlds'. Although, according to his brother, what he actually said after seeing the bomb explode was: 'I guess it worked'. As romantic as the potential of science can be, there is also a banality to the discoveries and inventions that shape our world. It is irrefutable that the atomic bomb changed the trajectory of the 20th century, ending the Second World War and fuelling the Cold War between the Soviet Union and the United States, and their proxies.

Quantum machine learning promises to efficiently solve important problems. There are two persistent challenges in classical machine learning: the lack of labeled data, and the limit of computational power. We propose a novel framework that resolves both issues: quantum semi-supervised learning. Moreover, we provide a protocol in systematically designing quantum machine learning algorithms with quantum supremacy, which can be extended beyond quantum semi-supervised learning. In the meantime, we show that naive quantum matrix product estimation algorithm outperforms the best known classical matrix multiplication algorithm. We showcase two concrete quantum semi-supervised learning algorithms: a quantum self-training algorithm named the propagating nearest-neighbor classifier, and the quantum semi-supervised K-means clustering algorithm. By doing time complexity analysis, we conclude that they indeed possess quantum supremacy.