Week 22 - my notes
Table of Contents
Week 22 - Types of Qubits and Intro to Near-Term Algorithms
Superconducting Qubits
Pros
- It’s on a chip
- Microwave technology is well developed
- High quality gates and readout
Cons
- Require cooling to mK temperatures
- Loss to substrate material
- Need bigger dilution
Photonic Qubits
Pros
- Room temperature
- Compatible with existing fabrication infrastructure
- Uses photonic technology which is well-developed
- Low single-photon error rates
Cons
- Entanglement is difficult (photons don’t interact with each other easily)
- Single photon generation is unreliable
- Superconducting detectors require cooling to K temperatures
Guest
Rydberg Atom Qubits
- Relativily new type of qubit
- Each atom is a qubit
- We use the shell of the electron to identify different states
- The types of atoms used are called neutral atom: atoms with only one atom in the valence
- If the valence electron is where it should be, it’s \[|0\rangle\] if it’s not, it’s \[|1\rangle\]
How do you hold atoms in the right locations?
- Optical tweezers are used to hold the atoms in place. It is even possible to create an array of atoms
Pros
- Large number of qubits
- Customizable qubit locations
- High decoherence time
Cons
- Physical crowding limits single-qubit gates
- High error rates for ~2 qubit gates
Near-term algorithms
- Designed to work on NISQ (Noisy Intermidiate-Scale Quantum) technology
- It is used by leveraging quantum computers alongside classical ones
- We don’t know about advantages in near-term algorithms over classical ones
- These algortihms are also called hybrid algorithms (bc of quantum + classical)
- The ultimate goal we can shift more computation into QC, but right now, most of them are done in CC
QPU & CPU partnership
- Quantum Computer: Runs your Quantum Circuit
- Quantum Computer: Send the results of your circuit to Classical Computer
- Classical Computer: Figure out how to modify the circuit
- Classical Computer: Send new-instructions to Quantum Computer
- Quantum Approximate Optimization Algorithms (QAOA)
- Variational Quantum Eigensolver (VQE)
Problems VQE and QAOA can solve
- Constrained Optimization Problems: Find the most optimal solution
- The Knapsack problem (the backpack problem) is the most famous problem
- The KP is that you are only allowed to bring one backpack. It is up to you to pack the bag most efficiently, given that you can only carry a certain wight and certain volume
- You can map the different solutions in discrete values!
Tunable Quantum Circuits
- We can implement the possible solutions to different settings of a quantum circuit
- For our tunable circuits, we will use tunable gates.
- Tuning the circuit to find the most optimal circuit, it’s just tuning these gates
Resources
VQE in the Qiskit Textbook: https://qiskit.org/textbook/ch-applications/vqe-molecules.htm Introductory Blog post on VQE: https://www.mustythoughts.com/variational-quantum-eigensolver-explained Blog post on simulating molecules using VQE: https://towardsdatascience.com/simulated-quantum-computation-of-molecular-energies-using-vqe-c717f8c86b94
Prof. Michelle Simmons: https://www.sydneyquantum.org/news/four-brilliant-women-in-quantum-share-stories-on-international-womens-day/
Dr. Hanhee Paik: https://researcher.watson.ibm.com/researcher/view.php?person=us-hanhee.paik