Jason Cong talks about
Compilation for Quantum Computing: Gap Analysis and Optimal Solution

Papers in this session.

From the abstract:

As quantum computing devices continues to scale up, we would like to access the quality of the existing quantum compilation (or design automation) tools. As the first step, we focus on the layout synthesis step. We develop a novel method to construct a family of quantum circuits with known optimal, QUEKO, which have known optimal depths and gate counts on a given quantum device coupling graph. With QUEKO, we evaluated several leading industry and academic LSQC tools, including Cirq from Google, Qiskit from IBM, and t|ket from CQC.

We found rather surprisingly large optimality gaps, up to 45x on even near-term feasible circuits. Then, we went on to develop a tool for optimal layout synthesis for quantum computing, named OLSQ, which formulates LSQC as a mathematical optimization problem. OLSQ more compactly represents the solution space than previous optimal solutions and achieved exponential reduction in computational complexity. 

Siraj Raval explains Quantum Machine Learning in the fun and approachable way he’s know for.

Quantum Machine Learning may sounds daunting to most people, but it’s way more fun to learn about than Classical Machine Learning. Creative algorithms that leverage concepts like quantum entanglement and superposition are already being studied by various teams to enable new solutions in fields like Chemistry, Finance, Supply Chain, and Energy. 

Before you email or comment regarding controversies around Siraj, I said my piece in a Data Driven data point.

Listen to that before you send angry comments my way. Winking smile

You’ve likely noticed an uptick of content related to quantum computing over the last few months.

This article in Forbes sheds some light on why.

“We can have quantum impact right now,” says Krysta Svore, General Manager of Quantum Software at Microsoft. “Quantum-inspired solutions allow you to have improvements today,” she adds. Svore received her Ph.D. in computer science from Columbia and a B.A. in mathematics from Princeton, where she first encountered quantum computing in a seminar on cryptography and realized that “there is a different model of computation that could unlock solutions to problems that we couldn’t expect to unlock with classical computers.”

Quantum is coming. Get ready.

Because our most powerful classical computers are limited in the chemical modeling they can perform, so are the solutions they can unlock.

Quantum computing could change that.

On this episode of Quantum Impact, Dr. Krysta Svore, general manager of quantum systems and software at Microsoft, heads to Richland, Washington to meet with Dr. Nathan Baker and Dr. Bojana Ginovska at Pacific Northwest National Laboratory (PNNL).

Microsoft is partnering with PNNL to bring the power of quantum to our understanding of chemistry. One of PNNL’s areas of interest is catalysis, or the process of converting chemicals from one form to another, and Nathan shares the complexity involved in truly understanding that process.

Bojana, a computational chemist, then speaks with Krysta about her work studying nitrogenase, an enzyme present in healthy soil. She’s exploring how we can turn nitrogen into ammonia for agriculture in a way that doesn’t deplete our energy resources.

Together with PNNL, Microsoft is working to develop quantum algorithms to help solve challenging problems in chemistry, which will have hugely positive impacts on our world and our planet’s future.