# Keynote: Superconducting qubits for quantum computation: transmon vs fluxonium

TLDRIn this keynote, Michelle Davore discusses the hardware design for quantum computation, focusing on superconducting qubits. She compares two popular types of superconducting artificial atoms, the transmon and fluxonium, explaining their operation and the factors that differentiate them. Davore highlights the importance of non-linearity in qubit design and the challenges of optimizing coherence and minimizing noise. She concludes by suggesting that while current technology can be further optimized, significant advancements will likely require breakthroughs in materials and fabrication techniques.

### Takeaways

- 😀 Michelle Davore presents a review on superconducting qubits for quantum computation, focusing on hardware design.
- 🔬 Superconducting qubits are part of a larger structure known as a superconducting artificial atom, which mimics the behavior of natural atoms.
- 🌟 Two popular types of superconducting artificial atoms are the transmon and fluxonium, each with unique properties for use as qubits.
- 🔋 The energy levels in superconducting circuits are determined by the values of inductance and capacitance, unlike natural atoms where energy levels are fixed by fundamental constants.
- 🔗 Dissipation, or resistance, in superconducting circuits can broaden energy levels, affecting the quality factor, which is crucial for the performance of artificial atoms.
- 🔄 Non-linearity is introduced in superconducting circuits through Josephson junctions, allowing for the isolation of specific energy levels for qubit operations.
- 📉 The transmon and fluxonium are chosen for their ability to minimize the impact of noise, with the transmon currently having a slight edge in coherence.
- 🔄 The development of superconducting qubits involves optimizing the product of the quality factor and the non-linearity factor for better performance.
- 🔧 There is ongoing research to improve the coupling between qubits, which is essential for scaling up quantum computing architectures.
- ⚙️ Non-linearity, while beneficial for faster operations, also introduces challenges such as spurious transitions that need to be managed in the design of quantum circuits.

### Q & A

### What is the main focus of Michelle Davore's keynote?

-The main focus of Michelle Davore's keynote is a review of hardware design for quantum computation, specifically comparing superconducting qubits, namely transmon and fluxonium.

### What is a superconducting artificial atom?

-A superconducting artificial atom is a complex structure that includes many energy levels and behaves like an atom, but is created using superconducting circuits.

### How do superconducting circuits mimic the behavior of atoms?

-Superconducting circuits mimic atoms by using an electron condensate that moves within the circuit components, such as capacitors and inductors, to represent the behavior of electrons in atoms.

### What is the significance of the frequency decrease in superconducting circuits compared to natural atoms?

-The significant decrease in frequency in superconducting circuits compared to natural atoms allows them to emit microwave light instead of visible or UV light, which is a key aspect of their operation in quantum computation.

### Why is non-linearity important in the design of superconducting qubits?

-Non-linearity is important because it allows for the isolation of specific energy level transitions, which are necessary for defining and controlling qubits in quantum computation.

### What is the role of the Josephson junction in superconducting qubits?

-The Josephson junction provides the non-linearity required in superconducting qubits by introducing a sinusoidal potential that deviates from the perfect parabola, which is essential for creating non-degenerate energy level transitions.

### How do transmon and fluxonium qubits differ in their design?

-Transmon qubits use a small Josephson junction shunted by a large capacitance, while fluxonium qubits use a small Josephson junction shunted by a large inductance.

### What is the current state-of-the-art quality factor for transmon qubits?

-The current state-of-the-art quality factor for transmon qubits is around 10 million, although some experiments have reached up to 20 million.

### What are the challenges in optimizing superconducting qubits for quantum computation?

-Challenges include managing dissipation, controlling defects, minimizing noise influence, optimizing non-linearity, and dealing with spurious transitions and matrix elements between computational and non-computational levels.

### What are the future prospects for the optimization of superconducting qubits according to Michelle Davore?

-Michelle Davore suggests that the current technology can still be optimized, with the potential to reach a tenth of a percent for infidelity in all operations. However, to surpass this level, breakthroughs in new materials and device fabrication may be necessary.

### Outlines

### 🧲 Introduction to Quantum Hardware Design

Michelle Davore begins by expressing gratitude to the organizers for inviting her to present a review on quantum computation hardware design. She acknowledges her time at Google's Quantum AI team during her sabbatical and outlines her talk, which includes an explanation of superconducting qubits as part of a larger structure known as superconducting artificial atoms. Davore emphasizes the importance of understanding these atoms to grasp the creation of qubits. She introduces the concept by comparing a simple hydrogen atom to a superconducting circuit, highlighting the differences in scale and behavior between electrons in an atom and the electron condensate in a superconducting circuit.

### 🔗 Exploring Superconducting Circuits and Dissipation

Davore delves into the details of superconducting circuits, comparing them to a harmonic oscillator with controllable energy levels determined by inductance and capacitance values. She discusses the significance of dissipation in the circuit, which can be managed to optimize the quality factor, a measure of the circuit's energy retention. The talk also touches on the necessity of some dissipation for resetting the circuit to a known state. Davore highlights the advantage of superconducting circuits' ease of coupling, which allows for Hamiltonian engineering. She introduces the concept of non-linearity in the circuit, which is crucial for isolating specific energy transitions for qubit operations, and discusses the non-linearity ratio as a key design parameter.

### 🔄 Non-linearity and Design of Superconducting Qubits

The speaker discusses the introduction of non-linearity in superconducting circuits through the use of Josephson junctions, which act as non-linear inductances. She explains how these junctions, along with capacitances and inductances, form the basis of superconducting artificial atoms designed for qubits. Davore emphasizes the importance of optimizing the product of the quality factor and the non-linearity factor in these artificial atoms. She also addresses the physical aspects and challenges in designing these circuits, such as managing dissipative elements and controlling parasitic radiation.

### 🌐 Comparing Transmon and Fluxonium Qubits

Davore compares two types of artificial atoms used in quantum computing: the transmon and the fluxonium. She positions these on a parameter space diagram, highlighting their optimization to minimize noise influences. The transmon, shunted by a large capacitance, and the fluxonium, shunted by a large inductance, are described as the current favorites due to their noise resistance. She presents spectroscopic data comparing the quality factor of the hydrogen atom to that of the transmon, noting the progress in achieving high quality factors in人造 atoms. Davore also mentions the ongoing development and potential of the fluxonium.

### 🔄 Coherence and Challenges in Quantum Circuit Engineering

The speaker addresses the challenges in engineering quantum circuits, focusing on the need for optimizing not just individual qubits but also the coupling between them, readout resonators, and amplifier chains. She discusses the importance of various characteristics such as readout fidelity and time, reset fidelity and time, and multi-qubit operations. Davore points out that while the transmon can be coupled to multiple other qubits, the fluxonium has so far only been coupled to one, suggesting potential for future development. She also raises concerns about design constraints such as leakage, the impact of cosmic rays, and the need for a multi-dimensional parameter optimization approach.

### 🚀 Future Optimization and Closing Remarks

In the final part of her talk, Davore reflects on the potential for further optimization of superconducting circuits and the challenges of achieving higher precision in qubit operations. She suggests that while current technology can be optimized to reach very low infidelity rates, breakthroughs in materials and fabrication methods may be necessary for further advancements. Davore proposes the idea of sealing devices in vacuum as a potential innovation. She concludes by thanking her team at Yale, her colleagues, and funding agents for their support and acknowledges the audience's attention.

### Mindmap

### Keywords

### 💡Superconducting qubits

### 💡Transmon

### 💡Fluxonium

### 💡Artificial atoms

### 💡Non-linearity

### 💡Dissipation

### 💡Quality factor

### 💡Coupling

### 💡Coherence

### 💡Two-level systems

### Highlights

Michelle Davore presents a review on hardware design for quantum computation, focusing on superconducting qubits.

Superconducting qubits are part of a larger structure known as a superconducting artificial atom.

The simplest superconducting circuit is analogous to the hydrogen atom, with an electron condensate replacing the electron.

Superconducting circuits emit microwave light instead of visible light, due to their larger dipole moments.

The energy levels of superconducting circuits are determined by the values of inductance and capacitance.

Dissipation in superconducting circuits is undesirable as it broadens energy levels, reducing coherence.

Dissipation is necessary for resetting the circuit to a known state, which is crucial for quantum algorithms.

Superconducting circuits are easily coupled, allowing for Hamiltonian engineering through capacitive or inductive coupling.

Non-linearity in superconducting circuits is introduced through Josephson junctions, which are key for qubit design.

The transmon and fluxonium are popular superconducting qubit designs that minimize noise influence.

Transmon qubits are created by shunting a small Josephson junction with a large capacitance.

Fluxonium qubits involve a small Josephson junction shunted by a large inductance.

The quality factor of superconducting qubits is close to that of natural atoms, with the transmon reaching around 10 million.

Coupling between qubits is essential for quantum processors, with the transmon currently outperforming the fluxonium in this aspect.

Optimizing individual qubits is not enough; the entire system's parameters need to be considered for a functional quantum processor.

Non-linearity is a double-edged sword in qubit design; while it speeds up operations, it also introduces spurious transitions.

The future of superconducting qubits may rely on breakthroughs in materials and fabrication techniques.

Davore predicts that further optimization of current superconducting circuit technology is possible, aiming for higher fidelity operations.