Interning at Microsoft Quantum – 2022

Mariia Mykhailova

November 10th, 20210 0

Co-authored by Martin Roetteler and Leanne Dillon

We’re excited to announce that the applications for the Microsoft Quantum internships 2022 are open! We’re offering two types of internships, research internships and quantum programming internships.

Our 2022 Intern Program will be fully virtual again, and open to students located in the US and Canada. You can read more about what the virtual internships on our team look like in last year’s blog post.

We always get a lot of questions about upcoming internships, their topics, and the required qualifications. While we can’t share information about future projects our interns will be working on, we can share some of the recent projects our interns have worked on in both the research and software engineering sides. (You can also check out the announcement from 2019 for some tidbits on even older intern projects.)

Research internships

Research internships are the most traditional of our internships, targeting graduate students pursuing a PhD in quantum computing, and focusing on exploration of new research directions under guidance of full-time researchers on our team.

Here are some examples of the projects our interns have worked on over the past few years, and the papers they’ve written based on these projects:

Aniruddha Bapat investigated local spin algorithms for the MAXCUT problem and developed new optimization algorithms that can be seen as classical analogues of the quantum approximate optimization algorithm (QAOA).
Vera van Burg worked on quantum computing enhanced computational catalysis and developed new quantum algorithms for double-factorized representations of the four-index integrals that can significantly reduce the computational cost. See also this blog on potential implications of her work for designing catalysts for the carbon capture problem and climate change.
Sam Jaques developed a quantum circuit simulator that leverages sparsity in the state vector, enabling efficient testing of quantum algorithm implementations for chemistry and crypto applications that could otherwise not be tested due to the large number of qubits required even for the smallest instances.
Giulia Meuli developed a method for tracking approximation errors in quantum program that enables accuracy-aware quantum circuit compilation and allows to exact symbolic resource estimates for given quantum programs.
Christopher Pattison developed an algorithm for decoding the surface code using “soft” side-information that might be available about measurement outcomes as opposed to hard 0/1 information as has been previously done in the field. His algorithm outperforms even the best theoretical upper bounds for the performance of “hard-decoders”, both in terms of maximum noise tolerable as well as in terms resource overhead for a given noise level.
Nishant Rodrigues developed the quantum double sieve which is a quantization of the classical double sieve for finding the shortest vector in a lattice. His work exceeds the state-of-the-art in quantum sieving methods for lattice cryptanalysis with potential impacts in the post-quantum cryptography space.
Eddie Schoute developed a new algorithm for surface code compilation via edge-disjoint paths, targeting geometries in which the qubits form a 2D grid with nearest-neighbor logical operations.
Kartik Singhal investigated static analysis of quantum programs via Gottesman Types and developed a method for tracking entanglement in quantum programs.
Ewin Tang worked on algorithms for learning of quantum Hamiltonians from high-temperature Gibbs states, proving tight bounds on the algorithms sample complexity along the way.
Maxime Tremblay investigated bounds on stabilizer measurement circuits and obstructions to local implementations of quantum low-density parity-check (quantum LDPC) codes. He also developed an algorithm to generate low-depth syndrome-extraction circuits for quantum low-density parity-check (quantum LDPC) codes and obtained new results on constant-overhead quantum error correction with thin planar connectivity.
Daochen Wang investigated quantum algorithms for reinforcement learning with a generative model, achieving quadratic speedups over the best-possible classical sample complexities in the approximation accuracy, the effective time horizon, and the size of the action space.

Quantum programming internships

Quantum programming internships focus on software projects related to the Quantum Development Kit or Azure Quantum, targeting undergraduate students in software engineering or quantum computing majors, and focusing on developing a new feature or a tool to improve the user experience under guidance of experienced software engineers on our team.

Here are several examples of projects and features done by our interns in the past two years:

Raphael Koh and Reem Larabi created %trace and %debug commands to offer program visualization and visualization of state evolution throughout the program execution in Q# Jupyter Notebooks (see this blog post for details on these tools). In fact, the JavaScript library that powers the %trace command is released as a separate library quantum-viz.js!
Kesha Hietala worked on tools for verification of quantum programs.
Eion Blanchard extended the capability of Azure Quantum QIO cost function formulation with the addition of native support for Squared Linear Combination terms, allowing customers to express larger and more complex problems using less resources.
Ethan Perry added Azure Quantum workspace insights (visuals that represent activity and resources utilization in that workspace) and SKU selection interface.
Ryan Moreno is working on improving the debugging experience for Q# code based on its QIR representation.