The IEEE Quantum Week (QCE20) is a conference where academics, newcomers, and enthusiasts alike come together to discuss new developments and challenges in the field of quantum computing and engineering. Due to COVID-19 restrictions, this year's conference will be held virtually, starting today and running till October 16.

Throughout the course of the event, QCE20 will host parallel tracks of workshops, tutorials, keynotes, and networking sessions by industry front-runners like Intel, Microsoft, IBM, and Zapata. From the pack, today well peek into what Intel has in store for the IEEE Quantum Week. Particularly, well be previewing Intels array of new papers on developing commercial-grade quantum systems.

Starting off, Intel will be presenting a paper in which researchers have employed a deep learning framework to simulate and design high-fidelity multi-qubit gates for quantum dot qubit systems. This research is interesting because quantum dot silicon qubits can potentially improve the scalability of quantum computers due to their small size. This paper also indicates that machine learning is a powerful technique in optimizing the design and implementation of quantum gates. A similar insight was used by another team at the University of Melbourne back in March in which the researchers used machine learning to pinpoint the spatial locations of phosphorus atoms in a silicon lattice to design better quantum chips and subsequently reduce errors in computations.

Next up, Intel's second paper proposes an algorithm that optimizes the loading of certain classes of functions, e.g. Gaussian and Probability distributions, which are frequently used for mapping real-world problems to quantum computers. By loading data faster in a quantum computer and increasing throughput, the researchers believe that we can save time and leverage the exponential compute power offered by quantum computers in practical applications.

One of the earliest and most useful applications of quantum computers is to simulate a quantum system of particles. Consider the scenario where the ground state of a particle is to be calculated to study a certain chemical process. Traditionally, this task usually involves obtaining the lowest eigenvalue from the corresponding eigenvectors of the states of a particle represented by a matrix known as the Hamiltonian. But this deceptively simple task grows exponentially for larger systems that have innumerable particles. Naturally, researchers have devised quantum algorithms for it. Intels paper highlights the development and research requirements of running such algorithms on small qubit systems. The firm believes that the insight garnered from these findings can have potential implications for designing qubit chips in the future while simultaneously making quantum computing more accessible.

While were still in the NISQ (Noisy Intermediate-Scale Quantum) era of quantum computers, meaning that perfect quantum computers with thousands of qubits running Shors algorithm are still a thing of the future, firms have already started preparing for a quantum-safe future. One of the foreseeable threats posed by quantum computers is the ease with which they can factor large numbers, and hence threaten to break our existing standards of encryption. In this paper, researchers at Intel have aimed to address this concern. By presenting a design for a BIKE (Bit-flipping Key Encapsulation) hardware accelerator, todays cryptosystems can be made resilient to quantum attacks. Another thing to note here is that this approach is also currently under consideration by the National Institute of Standards and Technology (NIST), so a degree of adoption and standardization might be on the cards in the future.

Addressing the prevalent issues of the NISQ era once again, this paper debuts a novel technique that helps quantum-classical hybrid algorithms run efficiently on small qubit systems. This technique can be handy in this era since most practical uses of quantum computers involve a hybrid setup in which a quantum computer is paired with a classical computer. To illustrate, the aforementioned problem of finding the ground state of a quantum particle can be solved by a Variational-Quantum-Eigensolver (VQE), which uses both classical and quantum algorithms to estimate the lowest eigenvalue of a Hamiltonian. But running such hybrid algorithms is difficult. But the new method to engineer cost functions outlined in this paper could allow small qubit systems to run these algorithms efficiently.

Finally, on the penultimate day of the conference, Dr. Anne Matsuura, the Director of Quantum Applications and Architecture at Intel Labs, will be delivering a keynote titled Quantum Computing: A Scalable, Systems Approach. In it, Dr. Matsuura will be underscoring Intels strategy of taking a systems-oriented, workload-driven view of quantum computing to commercialize quantum computers in the NISQ era:

Quantum computing is steadily transitioning from the physics lab into the domain of engineering as we prepare to focus on useful, nearer-term applications for this disruptive technology. Quantum research within Intel Labs is making solid advances in every layer of the quantum computing stack from spin qubit hardware and cryo-CMOS technologies for qubit control to software and algorithms research that will put us on the path to a scalable quantum architecture for useful commercial applications. Taking this systems-level approach to quantum is critical in order to achieve quantum practicality.

The research works outlined above accentuate Intels efforts to develop useful applications that are ready to run on near-term, smaller qubit quantum machines. They also put the tech giant alongside the ranks of IBM and Zapata that are working on the commercialization of quantum computers as well.

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QCE20: Here's what you can expect from Intel's new quantum computing research this week - Neowin

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