mod04lec19 - NISQ-era quantum algorithms

Summary

In this video, Shesha Raghunathan from IBM Systems discusses the evolution and importance of variational quantum algorithms in the NISQ (Noisy Intermediate-Scale Quantum) era. Highlighting the limitations of current quantum hardware, such as noise and qubit count, Raghunathan explains how variational algorithms like VQE and QAOA are structured to fit within these constraints. These hybrid algorithms leverage classical hardware for optimization while performing quantum calculations for tasks like energy evaluation. The talk also touches on the potential applications of these algorithms in quantum machine learning, optimization problems, and more, emphasizing the growing interest and development in this field.

Takeaways

• 📚 The speaker, Shesha Raghunathan, is an IBM Quantum Distinguished Ambassador and leads the ambassador program in India and South Asia.
• 🌟 The talk focuses on modern quantum algorithms, specifically variational algorithms, and their potential applications.
• 🔍 Quantum computing has evolved through three broad generations, starting from Richard Feynman's conceptualization in 1981 to the current era of NISQ (Noisy Intermediate-Scale Quantum) computers.
• ☁️ IBM's release of quantum machines on the cloud in 2016 marked a significant shift, making quantum computing more accessible and sparking increased interest in programming quantum hardware.
• 🔧 The NISQ era, coined by John Preskill in 2007, refers to quantum computers that are noisy and have a limited number of qubits, challenging developers to create algorithms that can provide value despite these constraints.
• 🚀 Variational quantum algorithms, such as VQE and QAOA, emerged in 2014 and gained traction post-2016, aligning with the hardware limitations of the time by focusing on shorter circuit depths.
• 📈 The lifetime of superconducting qubits has exponentially increased over the last 15-20 years, with recent advancements pushing towards millisecond lifetimes, allowing for more complex computations.
• 💡 Variational algorithms are hybrid, utilizing both classical and quantum computing. They are well-suited for the current NISQ hardware, which has constraints on circuit depth and noise levels.
• 🌐 Real-world applications of variational quantum algorithms are being explored, including quantum machine learning, option pricing, and battery optimization.
• 🛠️ The current state of quantum hardware, as of July 2021, shows average qubit lifetimes around 100-120 microseconds, with readout errors dominating over gate errors, emphasizing the need for compact and shallow quantum algorithms.

Q & A

Who is Shesha Raghunathan and what is his role at IBM?

Shesha Raghunathan is part of IBM Systems and works with the Electronic Design Automation team, particularly on timing analysis. He is also an IBM Quantum Distinguished Ambassador, a Qiskit Advocate, and a Technical Ambassador, leading the ambassador program in India and South Asia.

What is the significance of the year 1981 in the context of quantum computing?

The year 1981 is significant because it marks the starting point when Richard Feynman contextualized quantum computing in a more modern form factor.

Why is the year 2016 considered a turning point for quantum computing?

2016 is considered a turning point because that's when IBM put its quantum machine on the cloud for public access, along with a programming platform to program that hardware, which revolutionized the accessibility and programming of quantum computers.

What does the term NISQ stand for and who coined it?

NISQ stands for Noisy Intermediate-Scale Quantum. The term was coined by John Preskill in 2007 to describe quantum computers that are noisy and have a limited number of qubits.

What is the difference between traditional quantum algorithms and those developed for NISQ-era hardware?

Traditional quantum algorithms assume qubits are clean and error-free, whereas NISQ-era algorithms are designed to work with noisy qubits and take into account the hardware's limitations, such as a small number of qubits and noise.

What are variational quantum algorithms and why are they important for NISQ-era hardware?

Variational quantum algorithms are hybrid algorithms that use both quantum and classical computing to solve problems. They are important for NISQ-era hardware because they are designed to be compact and shallow, fitting within the hardware's time and error constraints, and can potentially demonstrate quantum advantage.

What is Quantum Volume and what does it indicate about a quantum computer's capabilities?

Quantum Volume is a measure of the power of a quantum computer, taking into account the number of qubits, the error rates, and the connectivity of the qubits. A higher Quantum Volume indicates a more capable quantum computer.

What is the current state of qubit lifetimes in quantum computers as of the script's reference date?

As of July 10th, 2021, the average qubit lifetime in quantum computers is around 100-120 microseconds, with some experimental qubits reaching milliseconds.

What are the average error rates for the quantum computers mentioned in the script?

The average CNOT error rate is around 0.1 percent, and the readout error rate is around 1 percent for the quantum computers mentioned in the script.

How do variational algorithms fit into the limitations of current quantum hardware?

Variational algorithms are structured as hybrid algorithms with a classical component for optimization and a quantum component for computation. This structure allows them to perform well within the current hardware limitations, such as short qubit lifetimes and error rates.

What are some potential applications of variational quantum algorithms mentioned in the script?

Some potential applications of variational quantum algorithms include quantum machine learning, option pricing, and battery optimization.