The rate at which quantum computing is hitting the media stream is ever-increasing. This piece is a collection of recent articles and reports covering various aspects of quantum computing from the lens of algorithms and software. Mea Cubitt
QUANTUM COMPUTING AND THE SUEZ CANAL | Shipping decisions are complex, and so are ground- and air-based shipping. Which ship to send where? In what order should a ship – or a UPS truck – visit its destinations? Those decisions depend on many variables: the distance between the various destinations, the cost of travel, the risk of delays and more. Different companies might make different decisions with the same data depending on what they wish to optimize for: cost, time, minimal fuel consumption, minimal risk, fewest cargo carriers and so forth. Source: Yuval Boger. QUANTUM COMPUTING AND THE SUEZ CANAL…
Quantum computing: IBM just created this new way to measure the speed of quantum processors | Named CLOPS (Circuit Layer Operations Per Second), the metric is the first to measure the number of quantum circuits a quantum processing unit (QPU) can execute per unit of time, and is designed to provide an objective understanding of the amount of work a quantum system can do in a particular period. Source: ZDNet. Quantum computing: IBM just created this new way to measure the speed of quantum processors…
CMU Software Engineering Institute Asserts Bold Vision for Engineering Future Software Systems | In as little as 10 to 15 years, software engineering may look more like a technical conversation between humans and computers than a process of manually refining specifications and code, and the software ecosystem must prepare for that future. That is just one of the conclusions of a new study titled Architecting the Future of Software Engineering: A National Agenda for Software Engineering Research & Development that was released today by the Software Engineering Institute at Carnegie Mellon University. Source: CISION PR Newswire (CMU). CMU Software Engineering Institute Asserts Bold Vision for Engineering Future Software Systems…
TECH & SCIENCESoftware will become the final key to unlocking quantum computing power | A new report suggests China might be winning the race to build the most powerful quantum computers, based on the development of a 66-qubit programmable superconducting quantum computing system. This feat was performed at Hefei National Laboratory for Physical Sciences at the Microscale of the University of Science and Technology of China. Source: DIGITAL JOURNAL. TECH & SCIENCESoftware will become the final key to unlocking quantum computing power…
Modulation leakage vulnerability in continuous-variable quantum key distribution | A team of researchers from CiViQ experimentally demonstrate and theoretically analyze an information leakage vulnerability, in a continuous-variable quantum cryptographic system. They show that the final secret key may not be secure anymore if the users of that system do not properly take the leakage into account. Source: CiViQ. marta martin Modulation leakage vulnerability in continuous-variable quantum key distribution…
Noise Measurement of BB84 E91 Protocol Using Single Qubit in Qiskit | Quantum Cryptography is the quantum analog to Classical Cryptography where the key distribution is an integral part of the algorithm. Unlike Classical Computers, Quantum Computers and channels are prone to the disturbances in the environment and often these noises corrupt the information in Quantum Key Distribution procedure. As noise resistant/immune quantum computer is far from real, the only way is to correct the error induced by the noise. This has its own overhead of qubits and performance. Here we have used a real quantum computer (IBM-q-experience) to execute two QKD protocols, BB84 and E91 and subjected them to two different noise models. We measured the systems post execution and calculated the deviation in errors. Source: Why GitHub? Noise Measurement of BB84 E91 Protocol Using Single Qubit in Qiskit…
Software will become the final key to unlocking quantum computing power | A new report suggests China might be winning the race to build the most powerful quantum computers, based on the development of a 66-qubit programmable superconducting quantum computing system. This feat was performed at Hefei National Laboratory for Physical Sciences at the Microscale of the University of Science and Technology of China. Source: DIGITAL JOURNAL. Software will become the final key to unlocking quantum computing power…
This New Quantum Computing Method Reduces Error Rates By 2500% | Q-CTRL, a Sydney-based start-up, has released the results of its algorithmic benchmarking experiments, which show that using an error suppression technique significantly improves the performance of quantum computers. The method resulted in a more than 2,500% improvement. Source: Wonderful Engineering. This New Quantum Computing Method Reduces Error Rates By 2500%…
Post-Quantum Encryption: A Q&A With NIST’s Matt Scholl | Quantum computing algorithms seek to use quantum phenomena to perform certain types of calculations much more efficiently than today’s classical, binary, transistor-based computers can. If and when a powerful enough quantum computer is built, it could run algorithms that would break many of the encryption codes we use to protect our data. In this interview with Taking Measure’s Mark Esser, Matt Scholl, chief of the Computer Security Division at the National Institute of Standards and Technology (NIST), discusses how worried we should be about this and what’s being done to mitigate the danger a future quantum computer poses to our data. Source: U.S. National Institute of Standards and Technology (NIST). Post-Quantum Encryption: A Q&A With NIST’s Matt Scholl…
Machine learning a useful tool for quantum control, finds new study (w/video) | Now, an international group of researchers from the Quantum Machines Unit at the Okinawa Institute of Science and Technology Graduate University (OIST), Japan, and the University of Queensland, Australia, has shown, through simulations, that reinforcement learning, a type of machine learning, can be used to produce accurate quantum control even with noisy measurements. Source: nanowerk. Machine learning a useful tool for quantum control, finds new study (w/video)…
What are the Q# programming language and Quantum Development Kit (QDK)? | Q# is Microsoft’s open-source programming language for developing and running quantum algorithms. It’s part of the Quantum Development Kit (QDK), which includes Q# libraries, quantum simulators, extensions for other programming environments, and API documentation. In addition to the Standard Q# library, the QDK includes Chemistry, Machine Learning, and Numeric libraries. Source: Microsoft| Docs. What are the Q# programming language and Quantum Development Kit (QDK)?
Quantum computing enhanced machine learning for physico-chemical applications | Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to conclude that ML and its close cousin deep learning (DL) have ushered unprecedented developments in all areas of physical sciences especially chemistry. Not only the classical variants of ML , even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionzed material design and performance of photo-voltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is to not only to foster exposition to the aforesaid techniques but also to empower and promote cross-pollination among future-research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms. Source: arXiv. Quantum computing enhanced machine learning for physico-chemical applications…
