Brandon Severin

Modern web development has become incredibly complex. We build layers upon layers of abstraction to render simple text on a screen.

There is a certain beauty in the raw <p> tag. It loads instantly. It is readable on everything from a 4K monitor to a terminal browser. It respects the user's agent stylesheet.

This blog attempts to replicate that feeling. No heavy CSS frameworks (well, we use Tailwind utility classes to mimic the lack of style, ironically), no 5MB JavaScript bundles for a simple article.

The Aesthetic of Information

When you remove the decoration, only the content remains. If the content isn't good, you can't hide it behind a parallax scroll effect.

For the last two years, we've been stuck in the chat box.

"Chat with your data." "Chat with your PDF." "Chat with your customer support."

But the real shift happens when the AI stops talking and starts doing. We are seeing the early signs of agentic workflows—models that can plan, execute, and verify complex tasks without human hand-holding.

The bottleneck isn't intelligence anymore; it's reliability and latency. Once we solve that, software fundamentally changes from a tool we use to a teammate we delegate to.

1. Ship early. Perfection is the enemy of feedback. If you aren't embarrassed by your v1, you launched too late.

2. Delete code. The best code is no code. Every line you write is a liability that needs to be tested, maintained, and read by someone else.

3. Optimize for change. Requirements will change. The market will change. Build systems that are loosely coupled so you can pivot without rewriting the world.

A few books I've been revisiting:

• Snow Crash by Neal Stephenson. It's startling how much of the modern metaverse and crypto landscape was predicted here.

• The Design of Everyday Things by Don Norman. Essential reading for anyone building user interfaces.

• Hackers & Painters by Paul Graham. Still the best collection of essays on the philosophy of software creation.

We are witnessing a fundamental shift in how we interact with computers.

For decades, the paradigm has been direct manipulation: point, click, type. You tell the computer exactly what to do, step by step.

Autonomous agents change this. You define the goal, and the agent figures out the steps.

The Loop

1. Perceive: The agent reads the screen or API.
2. Think: It uses an LLM to decide on an action.
3. Act: It clicks a button or sends a request.
4. Learn: It sees the result and adjusts.

This loop allows for software that feels less like a tool and more like an employee. The UI implications are massive.

React Server Components (RSC) are controversial. They blur the line between backend and frontend in a way that makes many uncomfortable.

But if you look past the complexity of the implementation, the mental model is sound.

Sending zero JavaScript for a static blog post (like this one) while keeping the ability to hydrate interactive islands is the holy grail. It's what we tried to do with Islands Architecture, but baked into the framework itself.

Yes, it feels a bit like we've reinvented PHP. But PHP with component encapsulation is actually a pretty good idea.

For the past decade, we've obsessed over Design Systems. Tokens, atomic design, strict component libraries.

It was necessary because maintaining consistency across 100+ screens by hand is impossible.

But Generative UI is changing the calculus. If an AI generates the interface on the fly based on user intent, strict static design systems become less important than rules of engagement.

We will move from designing screens to designing constraints.

The biggest mistake technical founders make is writing code too early.

Code is expensive. It has bugs. It needs maintenance.

A landing page is cheap. A Figma prototype is cheap. A conversation with a customer is free.

Validate the problem before you validate the solution. If people aren't complaining about the problem, no amount of beautiful code will make them care about your solution.

"100ms is instantaneous. 1 second is a pause. 10 seconds is a distraction."

In a world of heavy SPAs and hydration delays, a site that loads instantly feels magical.

We've become too comfortable with loading spinners. A spinner is an admission of failure. It says "our architecture is too slow for your thought process."

Aggressive pre-fetching, optimistic UI updates, and edge caching aren't optimizations; they are requirements for modern UX.

I still see projects with "strict": false in their tsconfig.json.

This defeats the purpose of TypeScript. The "any" type is a virus that spreads throughout your codebase, silently disabling the type checker.

Yes, strict mode is annoying at first. It forces you to handle null and undefined explicitly. But that is exactly where 90% of runtime errors come from.

Pay the tax upfront, or pay it with interest during a production outage.

Remote work doesn't work if you try to replicate the office online.

If you are on Zoom for 6 hours a day, you aren't doing remote work; you are doing office work from your bedroom.

Real remote work requires a shift to asynchronous communication. This means writing things down.

• Write detailed specs.
• Write thoughtful pull request descriptions.
• Write decision logs.

If it isn't written down, it didn't happen.

I've been trying to reduce my digital footprint.

• Deleted social media apps from my phone.
• Turned off all non-human notifications.
• Switched to a dumb phone for weekends.

The clarity of thought that returns when you aren't constantly dopamine-looping is startling. We are drowning in information but starving for wisdom.

Sydney, Australia

• Cathy Foley - Quantum in Action
• State of the Nation - Panel discussion
• Joel Wallman Keysight - bridging the gap between theory and experiment
• Warwick Bowen - The potential for quantum in biotechnology
• Panel - the role of government in quantum ecosystem
• Barry Sanders - Quantum Canada
• Jeremy O’Brien - PsiQuantum
• Hon Ed Husic MP - federal minister for industry and science - Delivering on australia’s strengths in quantum technologies
• Panel: Cyber security in the quantum age Alexey Bocharnikov, APAC Quantum Technology Leader, EY
• Andrew Dzurak, CEO & Founder, Diraq - quantum counting private investment greater than $1B per annum
• Panel: Australia’s strengths in quantum sensing > Andre Luiten, Managing Director, QuantX Pty Ltd;
• Panel: Bridging the research to commercialisation gap > Clare Birch, Associate, Blackbird;

---

Cathy Foley - Quantum in Action

Quantum has entered the lexicon of marvel movies. You in industry need to be taking action now. Will Australia lose out on this race due to lack of investments? Even Singapore invests more than Australia in to Quantum apparently? How do you see cooperation with the United States and the UK.

State of the Nation - Panel discussion

• Collaboration is a great way to find out where the market is heading.
• International collaboration is just fun.
• Australia quantum alliance sits under the Australia tech council.

Joel Wallman Keysight - bridging the gap between theory and experiment

• How to build a quantum computer.
• Keysight is originally under HP 15 hubs - global support network.
• Acquired - Signadyne, Labber and Quantum Benchmark.
• The quantum application: Quantum algorithms researcher developing quantum algorithms to solve real problems.
• The quantum memory: Passing algorithms to fault-tolerance experts. Error correction involves encoding logical qubits, measuring syndromes, and applying correction operators.
• Translating to physical control: Quantum control researcher developing signals for hardware (measurements, x-gates, Hadamard gates, etc.).
• Translating to FPGA: Enabling efficient operation with real-time decision making.

Warwick Bowen - The potential for quantum in biotechnology

• Biochemical dynamics span a vast range of spatial and temporal scales. life is motion in some sense.
• Quantum market cap projected to reach $9B after 2030. McKinsey estimates the majority of the future quantum industry will be in biotech.
• Partnership with IBM, employing their quantum computing technologies like optical nanocavities.
• Enzyme catalysis - ammonia production is crucial for us; plants do it naturally.

Panel - the role of government in quantum ecosystem

• Q-CTRL is Australia's first backed company.
• It is a market failure when world-changing companies are not supported by private investors.
• Community needs to articulate how their tech can be used to government more effectively.

Barry Sanders - Quantum Canada

• Canada wants to create a DARPA-like agency but doesn’t want a pure defense strategy.
• Quantum City - a world-leading quantum innovation hub in Alberta.

Jeremy O’Brien - PsiQuantum

• Utility means error correction, which means millions of qubits.
• Photons: Chips can be manufactured using mature semiconductor fabs. Photons do not feel heat and operate at less demanding cryogenic temperatures.
• Solving the connectivity issue: approx 50x reduction in run-time for compiled fault-tolerant algorithms.
• What is needed for photonic quantum computing to work? Lots of engineering challenges. Single photon generation purity at 99.9%.

Hon Ed Husic MP - federal minister for industry and science

• 19k jobs by 2045 (conservative estimate, likely 50k). Could add $9Bn to GDP.
• $1B fund, loans, bills and equity.

Panel: Cyber security in the quantum age

• A quantum network will be more sparse than classical, meaning it may be possible to cut nodes off due to reduced redundancy.
• QKD (Quantum Key Distribution) doesn't meet the assurance requirements of most western governments yet. PQC (Post-Quantum Cryptography) is the future.
• Need to be able to change the underlying encryption algorithm when needed. This is hard.
• Homomorphic encryption and blind computing: You don’t want the person performing your computation to know what you’re computing.

Andrew Dzurak, CEO & Founder, Diraq

• Only 10^5 qubits are needed if the error rates are 10^-9.
• Diraq wants quantum computer on a chip - similar to current computers.
• History of 1998 to 2023 of work on Silicon. Get 9 logical qubits in 3 years.

Panel: Australia’s strengths in quantum sensing

• If you are able to speak the language that industry understands, labs will find partners very easily.
• Why quantum sensors: What do we do if GPS goes down? We need sovereign capability for time and location. $2B a day in Australia depends on accurate time.
• Quantum sensors give an absolute result; they don't drift and don't need calibration.

School of Physics, The University of New South Wales, Sydney, Australia Program

Mark Friesen | Wiggle Well: engineering valley and spin-orbit properties of silicon

• wiggle well solves two problems: reliably large valley splitting and large intrinsic spin-orbit coupling.
• Valley states compete with the spin as a possible qubit.
• deterministic scheme: find a way to grow the short-period wiggle well.
• randomly dominated scheme: add uniform Ge to the quantum well and reposition the dot.

Kristiaan De Greve (imec) | Si spin qubits fabricated in advanced, industry-standard CMOS facilities

• Si MOS: best for valley splitting, worst for charge noise.
• Si/SiGe: worst for valley splitting, best for charge noise.
• process optimization via Hall characterisation.

Amanda Seedhouse | Wavelet-based methods for noise analysis

• Taking inspiration from climate science (sea surface index) for noise analysis.
• Wavelets are useful for edge detection - robust against 1/f noise.
• if we can break down noise into frequency components, we can understand sources of noise and lead to better controlled pulses.

Maja Cassidy | Experimental progress in hybrid super semi-devices

• Majorana recipe: 1D quantum wire + spin-orbit interaction + superconductivity + magnetic field.
• Soft gap linked to disorder - majorana signatures mimicked by disorder.
• Non-local conductance measurements to measure the full conductance matrix of the device.
• Topological gap protocol: tune to N=1 subband, check local conductance for ZBPs, check non-local conductance for bulk phase transition.

Rainer Blatt | Quantum Simulations with trapped-ion spin chains

• 100 ions in a chain.
• Ca+ qubit.
• measure the state of the qubit by shining in 397 nm light.

20220905, Pontresina Speakers: https://spinqubit5.nccr-spin.ch/speakers/program?

Vandersypen - Spin qubits and simulators - more, better, easier

• 25 years ago was the proposition
• why are spin qubits great?

Vision of a semiconductor qubit processor

• local registers

• quantum links

• integrated circuits

  • overcome the wiring bottleneck

• Linear six qubit array

• visibility of 96%, one qubit control at a time, decreases with simultaneous measurement

• spin resonance frequency shifts with microwave driving and with temperature, non-monotonous temperature dependence

  • should we operate at 200mK?
    • no RF heating effects, and T2 barely reduced.

• Cross talk during simultaneous driving of two qubits by EDSR

  • after you reach a certain Rabi frequency you see a plateau - this is expected, as the maximum distance that an electron can move inside a dot is capped.

• 2x2 Si/SiGe quantum dot array

  • multi-layer design
    • one electron regime reached
    • independent control tunnel coupling achieved

Quantum links

Electron shuttling

• take an electron and move it across the chip in hope that the spin state will be preserved
  • Charge shuttling in a 4-dot array, arXiv 2209.000920
  • estimated spin-flip probability per hop below 0.01%
  • Vande thinks that electron shuttling will be a very attractive approach (Boter PRA 2022)

Superconducting resonators

• coupling distance spins through a superconducting resonator
  • try a dispersive regime.
    • spins are resonant with each other but detuned from the resonator
• quantum simulation of Fermi Hubbard physics
• can get exciton physics in 4x ladder

Silvano De Franceschi - Long life to holes

• 100MHz you can drive holes
  • electrically addressable, but exposed to charge noise defects
  • "sweet spots" where a single hole spin has enhanced coherence in silicon
• Fast single-shot readout of single-hole spin in SiMOS
• Strong coupling between a photon and a hole spin in silicon (see Copenhagen conference)
  • create coupling with electric field in the device and electric field in the cavity

Dominik Zumbuhl - Hot Hole spin qubits in Ge/Si nanowires

• Holes could have a great future
• NCCR Spin - developing fast, compact and scalable electron and hole spin qubits
• Spin-orbit interaction: Hso L, fast all electrical qubits, price charge noise coupling
• no contact hyperfine interaction -> longer coherence times
• g-factor can be modulated strongly with gate voltages

finfets

• Rabi oscillations: 100MHz, Q factor of 100
• operable at 5K, readout actually ends up being the limiting factor via Pauli spin blockade
• Anisotropy of G-factor is strong

nanowires

• spin mixing transitions in spin blockade
• strong spin-orbit coupling ISO ~65nm
• all electrical spin manipulation (EDSR)
• Rabi oscillations can be made extremely fast, 1ns for spin flips
• tunable g-factor is useful for coupling to a resonator

Guido Burkard - Finger prints qubits noise in cavity QED

• Two-qubit gates between spin qubits AC/DC
• Qubit noise limiting gate fidelities and qubit coherence
• describe the system using quantum Langevin equations
• Semiconductor spin qubits arXiv 2112.08863

Andreas Wallraff - repeated quantum error correction in surface code

• google computer fidelity was 10-3, so way less than 1
• Challenges in quantum error correction: bit flips and phase flips
• why surface code?
  • can realise it in a planar geometry
  • has the largest error threshold, 1%
• fast QEC cycle of 1.1 us

Pasquale Scarlino - superconducting and semi-conducting, hybrid quantum circuits

• Barely reached the strong coupling regime until fairly recently (2017/2018)
• High impedance technology: josephson junction array
• NbN disordered thin films
• High impedance SQUID array resonator (100x smaller than competition)

Paris 2022

Opening ceremony - Only Dust

• tokenomics have been revealed - reward people who are building for the ecosystem
• want to be the number one go to platform for contribution in the starknet ecosystem

Uri Kolodny - Starknet: scaling Ethereum by devs, for devs

• Starks hit mainnet June 2020. Starknet announced on Jan 2021.
• Phases:
  • 1 - feature completeness e.g. cairo 1.0
  • 2 - scale, recursion.
  • 3 - decentralisation e.g. token
• L3: hyper scale control: exploring features, data solutions, and verifiable VMs. Test experiments and then come to the community.

Eli Ben Sasson - Starks ecosystem: StarkNet next steps

• Starks prove integrity
• Scalable proofs of integrity
  • privacy
  • scalability: size of the proof and running time is exponentially smaller than running the computation itself
• Magic Theorem: any proof of false statements must make 99% of constraints unsatisfied.
• The reason for the token is not a fund raise - starknet needs the token for decentralisation.
• Token allocation: smart contract developers will get a portion of fees.

WARP Transpiler: Write solidity code, deploy to starknet

• Write solidity, transpile to Cairo, deploy to Starknet.
• handling addresses and function names.
• target: delegate call transpilation and contract factories.

Talks at the HackerHouse, Paris 2022 WindRanger, EduDAO, BitDAO Chaired by Tina, Steward/Researcher at Flashbots

Cross-domain MEV & the crucial conundrum of proposer selection - Christopher Goes

• Tendermint has a weird proposer sharing algorithm
• MEV attacks abound
• randomized proposer selection
• cross chain MEV is a hard problem to solve
  • cross-domain anticorrelated incentives
  • Shared mempools?

Tarun Chitra - Towards Theory of Maximal Extractable Value

Link to paper is here

• Constant function market makers (e.g. Uniswap)
• sandwich attack with slippage limits
• amount of quantity: concavity of the trading function implies price and quantity slippage limits are equivalent
• MEV for aggregations of sandwich attacks
• Routing? how much worse does my route get if a sandwicher gets in there somewhere
• Link to the study of traffic congestion (Braess’ paradox)

Fields

• Define a field just for two operations
• A field is a set of integers with two operations
• Division is tricky because if we divide two integers we get may get a float

zk Ecosystem

• stark system came out in 1976
• ZKSNARKS have small proofs but require an initial setup and are not quantum resistant
• STARKS are quantum resistant and don't require a trusted setup

Rollups

• transaction execution is done outside layer 1
• data or proof of transaction is on layer 1 (incl. state transitions)
• Verifier is a contract on layer 1 and can secure the latest state
• zero knowledge proofs can be done recursively
  • a proof to prove 100 proofs are valid
  • layer 3 and layer 4 -> can create specific applications on each layer

Starknet Specifics

• Account abstraction: your account is a contract
• Cairo language: prove the correctness of the computation
• Hints: pass the value that we want to prove into the programme as a hint (using python)
• range_check_ptr: to make sure things haven’t overflowed
• No loops in cairo, use recursion
• Validium mode: data is stored off chain
• Volition mode: hybrid mode, data is stored off chain and on chain

Martin Wood Complex, University of Oxford

Inspired by nature, building on spin - Christiane Timmel

• hypothesis: magnetosensitive radical pairs are formed in light sensitive proteins, aka cryptochromes
• controlling spin in molecular wires
  • controlling and quantifying spin localisation and delocalisation by design, synthesis and EPR
  • can change geometry of system as well
  • quantum interference in molecular rings
• Oxford is world leading in the understanding of cryptochromes and their importance in bird migration.

Aleks Kissinger - Quantum computing meets computer science

• ZX-calculus and quantum circuit optimisation.

Dominic O’Brien - Engineering quantum technologies

• short range indoor links: polarisation based quantum schemes
  • symmetric encryption key
• Challenges of quantum key distribution in free space:
  • daylight swamps the signal
  • people can do it between satellites, but the key rates are really slow
• Hardware: MEMS mirror based beam streaming
• Entanglement QKDs: David Lucas, Ion traps
• Dynamic optics and photonics groups
  • use lasers for fabrication inside transparent materials
  • write single NV centres in diamond

Peter Leek - superconducting quantum technology

• how do we build a quantum computer out of electric circuits
  • LC resonators, superconductors, good microwave engineering
• get very close to the ground state - remove thermal excitations (10mK)
• by using josephson junctions we can create a effective non-linear resonator which creates unequally spaced levels
• Scaling challenge: we can get to a certain scale (1000) in a fridge, then you need microwave to microwave optical conversion between fridges.

David Lucas - Ion trap technologies

• atom ion in a trap is a mass on spring, in megahertz regime.
• DIQKD challenge, need high number of Bell pairs and high fidelity.

Quantum Matter - Experimental Frontiers - Seamus Davis

• Bosons: swap you don’t change the sign by minus 1
• Fermions: you do change the sign
• Refrigerate bosons —> collapse into a single quantum state —> bose-einstein quantum state
• Refrigerate fermions —> they pair up --> superconductivity
• Correlated electron metal - Consider the Coulomb interaction within the metals and you find that the structure changes (MOTT) / HUND correlated metals

Boostrapping an interoperable ecosystem on polkadot - Alberto Viera

• Polkadot: specialisation and connectivity. Layer zero which is a blockchain for other blockchains (parachains)
  • connected security - projects don’t need to worry about security.
• Moonbeam: Ethereum compatible smart contract parachain on polkadot
• Ethereum composability - e.g. sushi, aave, curve
• Composability powered by XCM and GMP protocols
• EVM with expanded features? EVM smart contracts can access substrate features
  • staking precompile
  • governance unchain
  • ERC20 - precompile: access MVR and GLMR as an ERC20 without wrapping
  • Batch precompile
  • XCM related precompile - liquid staking LIDO : KSM and DOT

Managing a DAO treasury for the revolution - Cult DAO

• Cult.dao: empowers and funds those building and contributing towards our decentralised future
  • the guardians: top 50 stakers, put forward proposals
  • the many: they can vote
• fee taken which takes 0.4% on every transfer
• treasury contract is renounced

Clean Smart Contract Practices

• have meaningful names
• test driven development
• setter and getter should be separated
• error messages: short messages save bytecode size eg: CH_PSCF (ClearingHouse Price Slippage Check Fills)
• storage stored within separate files
• avoid repetitive inputs checks by adding comments

Web3 security in 2022

• 1.3B dollars lost in 2021, 2.2B so far in 2022
• many issues are centralisation issues (super user access)
• auditors are starting to have skin in the game
• SDLC framework: secure development lifecycle
• monitor alerting is the next big thing
• smart contracts should have a pause function
• Wallets should have transaction simulations built in

Building full stack web3 apps - Ivan on Tech

• what is full stack? onchain logic, off chain logic, UI/UX logic
• Moralis workflow: sync historic and real time events from any contract and any chain

Fix the money, fix the world - Juan (MakerDAO SES)

• Money is debt: aligns to your values
• MetaDAOs: Maker - make MakerDAO into a credit facility at the centre.
• Coordination: work, workforce, capital
• governance design: open software, open frameworks, redundancy first
• redundancy first: make your system resilient, minimise single points of failure.

Python

Anaconda

Create an environment spec file (operating system specific)

conda list --explicit > spec_file.txt

cloning an environment

conda create --name myclone --clone myenv

removing an environment

conda remove --name myenv --all

General

Compatible type hints across various versions

Place the following at the top of the module

from __future__ import annotations

Greek letters

Python has support for unicode fonts

>>> print('Omega: \u03A9')
Omega: Ω
>>> print('Delta: \u0394')
Delta: Δ
>>> print('sigma: \u03C3')
sigma: σ
>>> print('mu: \u03BC')
mu: μ
>>> print('epsilon: \u03B5')
epsilon: ε
>>> print('degree: \u00B0')
degree: °
>>> print('6i\u0302 + 4j\u0302-2k\u0302')
6î + 4ĵ-2k̂

Automatically reload modules in jupyter notebook

# reload modules if there is a change
%load_ext autoreload
%autoreload 2

Place in top cell where modules are imported - useful for rapid testing and development of a python package

plot legend position

import matplotlib.pyplot as plt

plt.legend(loc='upper right')

Git

Commit messages

The commit type can include the following:

• feat – a new feature is introduced with the changes
• fix – a bug fix has occurred
• chore – changes that do not relate to a fix or feature and don't modify src or test files
• refactor – refactored code that neither fixes a bug nor adds a feature
• docs – updates to documentation
• style – changes that do not affect the meaning of the code
• test – including new or correcting previous tests
• perf – performance improvements
• ci – continuous integration related
• build – changes that affect the build system or external dependencies
• revert – reverts a previous commit

Example:

feat: improve performance with lazy load implementation for images

Libre Office - Impress

Create an image with rounded corners

• Overlay a rectangle with rounded corners over the image.
• select the image and the rectangle
• right-click -> shapes -> Intersect

LaTex

Installation on local machine: https://www.tug.org/texlive/

Inkscape

Crop an image in inkscape

1. Open Your Image in Inkscape.
2. Select a Vector Shape.
3. Add the Shape to the Canvas.
4. Reduce the Shape Opacity to Position the Crop.
5. Select the Shape and the Image Together.
6. Go to Object > Clip > Set Clip.
7. Check the Crop.
8. Release the Clip (if Needed)

Lady Margaret Hall, University of Oxford

Martin Kiffner - calculating intermolecular dispersion energies with quantum processors

London dispersion forces:

• fluctuating dipoles - correlated multipole moments
  • exchange of virtual photon pairs
• small correction to gross energy structure
• not pairwise additive
  • need to study the whole ensemble of the molecules to find a meaningful answer
• Although small - important for supramolecular chemistry, structural biology, nano tech etc.

How to approach this problem

• Ab-initio e.g. DFT
  • try and solve the schrodinger equation, computationally very demanding
• Empirical methods
  • cheap
  • but not very accurate
• Electric coarse graining
  • pair of 1D harmonic oscillator
    • Parameters: coupling, polarisability, separation, ground state energy
    • match model parameters to molecular species
  • Simple system, but still quantum do naturally reproduce dispersion forces

Quantum Algorithm

• Variational Quantum Eigensolver method
• For each oscillator, take into account d number of Fock states
• Need log2d qubits - each oscillator is represented by two qubits
• need to prepare each harmonic oscillator into a given quantum state
• number of unique circuits scales linearly with the number of molecules
• You have a classical algorithm on top of the quantum computer that picks a subset of parameters to feed back into the quantum computer
  • details in the paper (L. W. Anderson Phys Rev. A 105 2022)

Results

• Two iodine molecules (non-polar, strong London forces)
• reasonable fit between quantum (ibm-montreal/simulated) calc and theoretical results

Features

• number of qubits grows linearly with number of molecules
• number of uniques circuits measuring the hamiltonian of all-to-all dipolar interaction scales linearly with the number of molecules
• relatively cheap to extend quantum algorithm to an-harmonic molecule unlike classical coarse-grained counterpart

Aleks Kissinger - Picturing Quantum Software

• code that makes that code better
  • compilers
  • optimisation
  • vectorisation
• small advances in software give big gains on NISQ hardware
• quantum circuits are the assembly language of quantum computation
  • how to make quantum circuits more efficient
  • cut gates open and find things that are more fundamental inside
  • replace gates with ZX-diagram
    • made of spiders
    • two kinds
    • can make any quantum circuit from two basic spiders
    • small set of rules (8 rules) - imply hundreds of circuit identities
    • can realise in a much simpler circuit diagram
• How do we scale up?
  • GitHub.com/quantomatic/pyzx
  • python library
  • scale large circuit down to skeleton, take skeleton and expand into a simpler circuit than previously
    • have flexibility in the latter step, circuit routing
    • extract circuits based on Gaussian elimination

application

• T-count reduction, reduce the number of T-gates in a quantum circuit
  • reduce T-gates as they are much more overhead in fault-tolerant quantum computing
  • extremely important for long-term fault-tolerant QC (optimistically 20 years away)
  • matters today: simulating quantum circuits on a classical computer
    • classical simulation is hard
    • more qubits, the harder to simulate
    • the more entanglement between the qubits the harder it is to simulate
    • the more t-gates, the harder to simulated - as it is far from something that is easy to simulate
      • stabilarizer rank decomposition: simulate difficult circuits as a sum of simple circuits
      • If there aren’t too many terms you can use gauss-mania theorem
      • decomposing each T gate into 2 stabiliser terms, gives 2^t terms
    • interleave decomposition with zx-simplifications
      • 1400 t-gates, age of uni verse to simulate, can be broken down with zx-simulation to 6 minutes

Links: zxcalculus.com

Daniel Egger - quantum computing and its applications to optimisation and finance

why quantum

• still problems we can’t solve
• computation model in a nutshell
  • initial state
  • state evolves following a sequence of ops
  • measure state at end

Applications

• machine learning
  • classification task
    • fraud detection, credit risk rating, customer segmentation
  • feature map, classical support vector machine
  • can apply a quantum feature map, a feature map that can’t be calculated efficiently on classical machine
    • can leverage entanglement to find correlations between features in a quantum feature map
    • increase accuracy of classification, not necessarily faster
  • quantum neural networks
    • a bit more expressive
    • train a lot faster
    • brining quantities like entanglement trains to lower losses faster, ibm montreal backend

Combinatorial optimisation

• portfolio optimisation efficient frontier
  • max risk, minimise return
• binary decision variables
• MAXCUT
• take optimisation problem and transform to Ising Hamiltonians to GS problem
  • Hamiltonian corresponds to optimisation problem, ground state of hamiltonian is the solution
• heuristic may find better solutions faster

Transaction settlement

• clearing house that receives trades, which trades will settle, certain parties may not have the resources to settle the trades
• mixed binary optimisation problem
• enables new application such as transaction settlement

Financial simulations

• risk analysis with a quantum computer
• monte carlo simulation on Q for pricing and risk analysis
  • estimate expected value
  • value at risk
  • conditional value at risk
  • scales 2x
• Can rely on Amplitude estimation giving you a quadratic speed-up
• Option pricing
  • what system size for practical advantage, 7500 logical qubits
  • error correcting code needs to be 3 order of magnitude faster

Pauline Ollitrault - Molecular quantum dynamics: a quantum computing perspective

• born-oppenheimer approx: separation of the electronic and nuclear DOFs based on the different time-scales for their respective motion
  • solve electrons and nuclei individually
• classical approaches and limitations
  • we can do pyridine accurately, that is the current limit
• quantum algorithms for quantum dynamics simulations
  • solve electronic structure
  • solve time evolution Schrodinger equation based on previously attained electronic structure

time dependent Schrodinger

• decomposition methods
  • map to quantum circuit
  • requires no ancillary qubits
  • leads to shorter quantum circuits
• Variational methods
  • don’t encode time propagator
  • encode a trial state
    • solve the parameters’ equations of motion and adjust them (built classically and measured classically)
    • measure
    • iterate

Hendrik Hamann - Accelerated discovery for climate sustainability

• global emission rated will reach 80 Gt CO2/year by 2075
• path to 1.5degC means you have to remove CO2 from the atmosphere
• no matter what you have to deal with some level of climate change
• Geolab and MDLab are built on foundational AI capabilities

MDLab: Discovery materials for sustainability

• focus is mitigation
• finding materials for carbon removal or energy storage for example
• Accelerated discovery for materials for separation membranes for CO2 from air
  • deep search, ingest structure technical knowledge at scale from pdfs and scientific papers create knowledge graph
  • then use generative models from this knowledge, come up with new candidates
  • rely on AI models to accelerate learning. drive through monte carlo simulations
  • test models on simulated materials

Accelerated carbon accounting and measurements

• better carbon accounting (AI-enabled)
  • once you have a better assessment of your carbon footprint you can drive decisions
• Direct carbon GHG measurements
  • Satellite imagery to measure methane emission

Geolab: Geospatial data indexing for optimise performance and parallelism

• optimise data structures to optimise calculations
  • multidimensional-indicies
  • resolutions
  • space-filling curves
  • overview layers
• Geospatial temporal data access
  • help scientists access data faster
  • If you index data two order of magnitudes speed up (Geolab)
• 10,000x acceleration for feature generation and subsequent AI climate impact modelling
  • 400k timestamps for 4.5M locations —> 52 minutes, (to model high flood risk impact areas)
  • conventional system would take 14 months

Philip Stier - Climate Research at Oxford

https://www.climate.ox.ac.uk

State of research in Oxford

• over 200 researchers
• areas: biodiversity, climate, energy, future of food, water
• climate: physical climate, climate impact, climate action
• COP26 <- 80 oxford attendees

Next-gen models

• can simulate a few decades with current next-gen models
• how do you evaluate high res climate models
  • 6.6 million gis points, 2.5km 90 verticals
  • Turing test: a model is good enough when you can’t distinguish the model from the observation

Ship tracking based on aerosols

• Every cloud droplet forms on an aerosol particle, more pollution means more cloud droplets - want to find the affect of aerosols on models/climate
• Can see the tracks of ships in clouds, graduate students would find this by hand.
  • Can do this via standard computer vision ML model of hand labelled data
  • can get an output of global shipping corridors

Saiful Islam - From batteries to solar cells: Modelling insights into energy materials

• lithium battery materials
• beyond lithium ions
• perovskite solar cells

Context

• issues, is characterisation and deeper understanding
  • modelling is crucial to understand crystal structure
  • how do ions move
  • defects and disorder: “Like people, solids are imperfect - making them unique & interesting”
  • doping in the crystal lattice
  • surfaces and interfaces, grain boundaries of nano(materials)
• Lithium battery is a success story of materials science and chemistry in action.

Challenge

• the positive electrode of the battery (cathode)
• energy density of the cathode is half that of the graphite anode
• Looking at new materials such as
  • Li-rich NMC
  • DRS - disordered rock salts
  • anion redox methods: Invoking metal redox as well as an-ion redox to increase energy density

How fast can you charge

• how fast ions move across the lattice is related to charging speed
• Can we leave Cobalt behind for Iron based cathodes (Li Iron phosphate)
• Li-ions move through the structure, via a curved 1D path. predicted via modelling and shown via experiment

All solid state

• can we replace flammable liquid electrolyte with solid electrolyte
• Sodium batteries, abundant and low cost, good for storage for grid

Solar energy

• Perovskite cell predicted to overtake silicon in terms of solar cell efficiency
  • cheap to make
  • Problem: stability and ion migration
  • Mixed ionic -electronic structure, it was the iodide vacancy that moved

Volker Deringer - Machine-learning-drive advances in atomic scale materials modelling

• we want to know the energy of a system of where the atoms are - draw an energy potential surface about the structure
• we can only do this at a few points as the calculations are very demanding
• can use data based approaches, get a few points along the surface and predict the rest

How do we build these interatomic potential force field models

• reference data - need the correct data
• representations (descriptors) - the way you encode the atomistic structure
• Regression (the fit itself)
  • neural network based methods
  • kernel methods
  • linear fitting

what now - we want to see things we haven’t seen before

• can we build general purpose machine learning force fields
• 10nm long amorphous silicon
• can understand structural transitions under pressure
  • can see phase change
  • crystallinity formation and grain boundaries

Flaviu Cipcigan - Materials discovery lab for climate sustainability

• scientific tools and data for materials researcher in CCUS and energy storage
• discover optimised materials
  • generate
  • screen
  • simulate
• validate outcome for scale
  • process simulation
  • lab synthesis and screening with autonomous labs

deep search and knowledge graphs

• annotations and natural language processing tasks to mine patents papers and other data sets to create a knowledge graph of existing CCUS methods and materials
• AI for prediction of nonporous material properties
• ML classifiers enable rapid computational screen of carbon capture materials

automated laboratory for CCUS material testing

• data generation, validation and process optimisation
• rapid assay platform for thermodynamic and kinetic measurement software solvents
• automated lab for polymer synthesis found discovery of catalysts for CO2 utilisation
• pilot-scale testing of rapid thermal swing processes with solid sorbents

accelerated discovery for sustainable batteries

• rely on AI as an expert in the loop for battery optimisation. Train the AI to think like an expert based on expert knowledge, rather than having a human in the loop